COMPARATIVE SAFETY AND EFFECTIVENESS OF ...ufdcimages.uflib.ufl.edu/UF/E0/04/92/80/00001/MURIMI_I.pdfCOMPARATIVE SAFETY AND EFFECTIVENESS OF TREATMENTS FOR DEGENERATIVE DISC DISEASE:

COMPARATIVE SAFETY AND EFFECTIVENESS OF TREATMENTS FOR

DEGENERATIVE DISC DISEASE: AN ANALYSIS OF THE LUMBAR SPINAL FUSION

PROCEDURE

By

IRENE BERITA MURIMI

A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL

OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT

OF THE REQUIREMENTS FOR THE DEGREE OF

DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA

2015

© 2015 Irene Berita Murimi

To my family

—

Mama, Emma, Francisca, Rimo, Mung’a,

and in loving memory of Romanus Manko Murimi

__

Thank you for your unfailing faith through this remarkable journey.

4

ACKNOWLEDGMENTS

This work would not have been possible with the unwavering support and encouragement

provided by the members of my dissertation committee: Dr. Abraham Hartzema, Dr. Richard

Segal, Dr. Earlene Lipowski, Dr. Robert Decker and Dr. Xiaomin Lu. The insights shared and

advice generously given has made this work better than I could have managed on my own. I am

especially indebted to the chair of my committee, Dr. Hartzema, whose kind indulgence nurtured

my independence and allowed me to grow as a researcher.

I am grateful to the Center for Devices and Radiological Heath at the Food and Drug

Administration for trusting me with the Multi-Payer Claims Database. I offer specific thanks to

Dr. Danica Marinac-Dabic, Dr. Anna Ghambaryan, Dr. Nilsa Loyo-Berrios and Nicole Jones

who facilitated my work with the FDA, read draft manuscripts of this work and offered their

seasoned mentorship throughout this project.

I appreciate the input provided by my colleagues in the Department of Pharmaceutical

Outcomes and Policy who have allowed me to learn from and with them during the last several

years. I thank Jill Hunt, Nicole Corwine, Linda Orr and Katherine Morris for their tireless

efforts to ensure that all the administrative components of my doctoral education were addressed.

Their patience, resourcefulness and ever present listening ear were invaluable to this project. I

am also immensely grateful to Paul Kublis who graciously offered helpful critiques and an

encouraging word.

I have been privileged to have the support of many others during this pursuit. I

particularly thank John Wuestneck, Susan Pothier and the entire Pothier family who have, for

more than 10 years, provided me with a home away from home. I am grateful for the Christmas

holidays, the birthday cards, the phone calls just to see how I am doing and most especially for

the gift of being known. I am also thankful to my host mother, Janet Fischer, who signed up for a

5

four year commitment that turned into so much more. Being part of Fischer host family has been

one the greatest pleasures of my educational journey.

Finally, I would like to thank my family to whom this dissertation is humbly dedicated. I

am thankful for Mama’s vision, Emma’s confidence, Fran’s optimism, Rimo’s faith and

Munga’s joy, all of which have given meaning to this endeavor. In the end I am the product of

one Pauline Hilda Murimi. It is through my mother’s example and innumerable sacrifices that

this dissertation, and every other achievement that I can claim, have found fruition. Asante Sana.

6

TABLE OF CONTENTS

page

ACKNOWLEDGMENTS ...............................................................................................................4

LIST OF TABLES ...........................................................................................................................9

LIST OF FIGURES .......................................................................................................................12

LIST OF ABBREVIATIONS ........................................................................................................14

ABSTRACT ...................................................................................................................................17

CHAPTER

1 INTRODUCTION ..................................................................................................................19

Background .............................................................................................................................19

Need for Study .................................................................................................................20 Purpose of Study ..............................................................................................................21

Study Significance ...........................................................................................................22 Research Questions and Hypothesis .......................................................................................23

Part I: Correlates of rhBMP Use during LDDD-indicated Lumbar Fusion

Procedures ....................................................................................................................23 Part IIA: Association between rhBMPs Use and Subsequent Refusion Procedures ......23

Part IIB: Effect of rhBMP Use on Post-Discharge Hospitalization Patterns .................23

Part IIC: Effect of rhBMP Use on Post-Discharge Emergency Room Visit Patterns ....23

Part IID: Effect of rhBMP Use on Changes in Opioid Analgesic Use ...........................24 Part III: Safety Analysis of Recombinant Human Bone Morphogenetic Proteins. .........24

2 LITERATURE REVIEW .......................................................................................................25

Part 1: The Spine ....................................................................................................................25

The Human Spine ............................................................................................................25 Characterizing Disc Degeneration ...................................................................................26

Part 2: The Lumbar Spinal Fusion Procedure .........................................................................27 The Anterior Lumbar Interbody Fusion (ALIF) ..............................................................27 The Lateral Interbody Fusion (LIF) ................................................................................28

The Posterior Lumbar Interbody Fusion (PLIF) .............................................................29

The Transforaminal Lumbar Interbody Fusion (TLIF) ...................................................29

The Posterolateral Fusion ................................................................................................30 The Circumferential Fusion (360º) ..................................................................................30

Part 3: Recombinant Human Bone Morphogenetic Proteins ..................................................31 Recombinant Human Bone Morphogenetic Proteins in the U.S Fusion Market ............31 Utilization Patterns and the Correlates of rhBMP Exposure ...........................................34

Part 4: Assessment of Treatment Effectiveness ......................................................................36 Revision Procedures ........................................................................................................37 Inpatient Services ............................................................................................................39

7

Emergency Room Visits ..................................................................................................41 Opioid Analgesic Use ......................................................................................................42

Part 3: Safety Analysis of Recombinant Bone Morphogenetic Proteins ................................43 Assessment of Biological Plausibility .............................................................................43

Overview of rhBMPs and Cancer ....................................................................................45 Tables and Figures ..................................................................................................................49

3 METHODOLOGY .................................................................................................................50

Introduction .............................................................................................................................50 Data Source .............................................................................................................................50

Study Design ...........................................................................................................................52 Part I: Correlates of rhBMP Use during LDDD-indicated Lumbar Fusion

Procedures ....................................................................................................................52 Part IIA: Association between rhBMPs Use and Subsequent Refusion Procedures ......54 Part IIB: Effect of rhBMP Use on Post-Discharge Hospitalization Patterns .................60 Part IIC: Effect of rhBMP Use on Post-Discharge Emergency Room Visit Patterns ....65

Part IID: Effect of rhBMP Use on Changes in Opioid Analgesic Use ...........................66 Part III: Safety Analysis of Recombinant Human Bone Morphogenetic Proteins ..........71

Tables and Figures ..................................................................................................................75

4 RESULTS ...............................................................................................................................77


Procedures ....................................................................................................................77 Part IIA: Association between rhBMPs Use and Subsequent Refusion Procedures ......90

Part IIB: Effect of rhBMP Use on Post-Discharge Hospitalization Patterns ...............112 Part IIC: Effect of rhBMP Use on Post-Discharge Emergency Room Visit Patterns ..138

Part IID: Effect of rhBMP Use on Changes in Opioid Analgesic Use .........................144 Part III: Effect of Intraoperative rhBMP Use on Cancer Risk ......................................164

5 DISCUSSION .......................................................................................................................180


Procedures ..................................................................................................................180 Part IIA: Association between rhBMPs Use and Subsequent Refusion Procedures ....183 Part IIB: Effect of rhBMP Use on Post-Discharge Hospitalization Patterns ...............186 Part IIC: Effect of rhBMP Use on Post-Discharge Emergency Room Visit Patterns ..188

Part IID: Effect of rhBMP Use on Changes in Opioid Analgesic Use .........................189 Part III: Effect of Using Intraoperative rhBMPs on Cancer Risk .................................192 General Discussion ........................................................................................................195

APPENDIX

A MPCD DATA STRUCTURE...............................................................................................200

B CASE DEFINITIONS AND RELATED BILLING CODES ..............................................211

8

C EXPLORATION OF MODEL ASSUMPTIONS ................................................................215

LIST OF REFERENCES .............................................................................................................238

BIOGRAPHICAL SKETCH .......................................................................................................253

9

LIST OF TABLES

Table page

4-1 Characteristics of LDDD-indicated fusion procedure population (hierarchical

definition cohort) ...............................................................................................................84

4-2 Characteristics of LDDD-indicated fusion procedure population (primary diagnosis


4-3 Characteristics of LDDD-indicated fusion procedure population (comprehensive


4-4 Baseline characteristics of refusion analysis cohort (any degenerative condition

population) .........................................................................................................................99

4-5 Baseline characteristics of refusion analysis cohort (LDDD population) .......................102

4-6 Baseline characteristics of refusion analysis cohort (Stenosis population) .....................105

4-7 Baseline characteristics of refusion analysis cohort (Listhesis population) ....................108

4-8 Refusion-rhBMP risk analyses summary results .............................................................111

4-9 Baseline characteristics of readmission risk analysis population (hierarchical

algorithm definition) ........................................................................................................118

4-10 Baseline characteristics of readmission risk analysis nested population (hierarchical

algorithm definition) ........................................................................................................121

4-11 Baseline characteristics of readmission risk analysis population (primary diagnosis

definition) .........................................................................................................................124

4-12 Baseline characteristics of readmission risk analysis nested population (primary

diagnosis definition).........................................................................................................127

4-13 Baseline characteristics of readmission risk analysis population (comprehensive case

definition) .........................................................................................................................130

4-14 Baseline characteristics of readmission risk analysis nested population

(comprehensive case definition) ......................................................................................133

4-15 rhBMP-30 day readmission rate summary results ...........................................................136

4-16 Association between rhBMP use and time to the first LDDD-related readmission

analysis summary results .................................................................................................137

4-17 Association between rhBMP use and the number of LDDD-related readmissions

summary results ...............................................................................................................137

10

4-18 Association between rhBMP use and the number of LDDD-related ER visits

summary results ...............................................................................................................140

4-19 Baseline characteristics of opioid access patterns analysis cohort ..................................151

4-20 Baseline characteristics of patients on opioid analgesic therapy prior to index

procedure, stratified by estimated Oral Morphine Units (OMEUs) accessed daily * ....154

4-21 Distribution of opioid analgesic access levels in the three months prior to the index

procedure (summary statistics) ........................................................................................157

4-22 Baseline characteristics of typical versus outlier range opioid access rate groups ..........158

4-23 Baseline characteristics of typical versus extreme range opioid access rate groups .......160

4-24 Distribution of opioid analgesic access levels during the three observation windows

(propensity score matched cohort summary statistics) ....................................................163

4-25 Baseline characteristics of patients in the primary cervical fusion procedure study

population ........................................................................................................................171

4-26 Baseline characteristics of patients in the primary thoracolumbar fusion procedure

study population ...............................................................................................................174

4-27 Incidence of cancer by organ system of the first tumor diagnosed..................................177

4-28 rhBMP-cancer risk analyses summary results (cervical procedure cohort) .....................178

4-29 rhBMP-cancer risk analyses summary results (thoracolumbar procedure cohort) ..........179

A-1 Distribution of fusion procedure claims, stratified by the claim setting ..........................207

A-2 Assessment of complimentary concurrent codes .............................................................207

A-3 Analysis of the encounter type variable, stratified by insurance type .............................208

A-4 Analysis of the place of service variable, stratified by insurance type ............................209

A-5 Analysis of the revenue code variable, stratified by insurance type ................................210

B-1 Procedure codes used to identify spinal fusion surgeries, stratified by region and

fusion intent .....................................................................................................................211

B-2 Diagnostic codes used to identify degenerative conditions of the lumbar spine .............211

B-3 Diagnostic codes used to identify non-degenerative conditions of the lumbar spine ......212

B-4 Diagnostic codes used to identify degenerative conditions of the spine .........................212

11

B-5 Diagnostic codes used to identify non-degenerative conditions of the spine ..................213

B-6 Diagnostic codes used to identify cancer-related health care encounters, stratified by

the organ system affected.................................................................................................213

B-7 Major oral pharmacologic treatments use for chronic back pain .....................................214

C-1 Summary of proportionality assumption test (refusion risk analysis) ............................217

C-2 Summary of proportionality assumption test (readmission risk analysis) ......................227

12

LIST OF FIGURES

Figure page

2-1 Annotated schematic of a lumbar vertebral segment .........................................................49

3-1 Schematic illustrating the relationship between fusion and refusion events in

administrative claims data ..................................................................................................75

3-2 Timeline of fusion event in relation to the index date, baseline assessment window

and follow-up time .............................................................................................................75

3-3 Drug use assessment windows and their relation to the fusion event ................................76

4-1 Correlates of rhBMP use study population creation flowchart ..........................................83

4-2 Refusion risk analyses study population creation flowchart..............................................97

4-3 Refusion risk cohorts (subpopulations) .............................................................................98

4-4 Readmission and ER visit analyses study population creation flowchart ........................117

4-5 Distribution of the number of LDDD-related ER visits during the first year post-

procedure (hierarchical algorithm cohort) .......................................................................141


procedure (primary diagnosis definition cohort) .............................................................142


procedure (comprehensive definition cohort) ..................................................................143

4-8 Opioid use analyses study population creation flowchart................................................150

4-9 Distribution of opioid analgesic access levels in the three months prior to the index

procedure..........................................................................................................................156

4-10 Distribution of opioid analgesic access levels during the three observation windows

(propensity score matched cohort) ...................................................................................162

4-11 Analysis of cancer risk study population creation flowchart ...........................................170

A-1 Association between race miscoding and age ..................................................................206

A-2 Association between race miscoding and type of insurance plan ....................................207

C-1 Proportionality assumption test for rhBMP-refusion risk assessment model (any

degenerative condition cohort) ........................................................................................218

13

C-2 Proportionality assumption test for rhBMP-refusion risk assessment model (LDDD

cohort) ..............................................................................................................................219

C-3 Proportionality assumption test for rhBMP-refusion risk assessment model (Stenosis

cohort) ..............................................................................................................................220

C-4 Proportionality assumption test for rhBMP-refusion risk assessment model (Listhesis

cohort) ..............................................................................................................................221

C-5 Effect of the rhBMP use on refusion risk as a function of time (any degenerative

condition cohort) ..............................................................................................................222

C-6 Effect of the rhBMP use on refusion risk as a function of time (LDDD cohort) ............223

C-7 Effect of the rhBMP use on refusion risk as a function of time (Stenosis cohort) ..........224

C-8 Effect of the rhBMP use on refusion risk as a function of time (Listhesis cohort) .........225

C-9 Proportionality assumption test for rhBMP-readmission risk assessment model

(hierarchical definition cohort) ........................................................................................228


(primary diagnosis definition cohort) ..............................................................................229


(comprehensive definition cohort) ...................................................................................230

C-12 Effect of the rhBMP use on LDDD- related readmission risk as a function of time

(hierarchical algorithm cohort) ........................................................................................231


(primary diagnosis definition cohort) ..............................................................................232


(comprehensive definition cohort) ...................................................................................233

C-15 ANCOVA model independence of predictors’ assumption test ......................................235

C-16 ANCOVA model homogeneity assumption test (first post-procedure evaluation

window) ...........................................................................................................................236

C-17 ANCOVA model homogeneity assumption test (second post-procedure evaluation

window) ...........................................................................................................................237

14

LIST OF ABBREVIATIONS

ACP American College of Physicians

ALIF Anterior Lumbar Inter-body Fusion

AMA American Medical Association

APS American Pain Society

BIC Bayesian Information Criterion

CHR

CI

Cause-Specific Hazard Ratio

Confidence Interval

CMS Center for Medicare and Medicaid Services

CNS Central Nervous System

CPT-4 Current Procedure Terminology

DDD Degenerative Disc Disease

DLIF Direct Lateral Inter-body Fusion

ER Emergency Room

ESI Epidural Spinal Injection

FDA Food and Drug Administration

FFS Fee for Service

HCPCS Health Care Common Procedure Coding System

HDE Humanitarian Device Exemption.

HR Hazard Ratio

ICBG Iliac Crest Bone graft

ICD-10-CM International Classification of Diseases Clinical Modification, Tenth

Revision

ICD-9-CM International Classification of Diseases Clinical Modification, Ninth

Revision

IQR Interquartile Range

15

IRB Institutional Review Board

LBP Low Back Pain

LCA Latent Class Analysis

LDDD Lumbar Degenerative Disc Disease

LIF Lateral Inter-body Fusion

MPCD

MRI

Multi-Payer Claims Database

Magnetic Resonance Imaging

NDC National Drug Code

NEDS Nationwide Emergency Department Sample

NHAMCS National Ambulatory Medical Care Survey

NHIS National Health Interview Survey

NIS National Inpatient Sample

NSAID Non-Steroidal Anti-Inflammatory Drug

ODI

OME

Oswestry Disability Index

Oral Morphine Equivalent Units

OR

OSCCA

p

Odds Ratio

Oral Squamous Cell Carcinomas

P Value

PLF Posterolateral Fusion

PLIF Posterior Lumbar Inter-body Fusion

rhBMP

RCT

SD

Recombinant Human Bone Morphogenetic Protein

Randomized Control Trial

Standard Deviation

SF-36 Medical Outcomes Study Short Form SF-36 (SF-36) survey

SHR Subdistribution Hazard Ratio

16

SIR Standardized Incidence Rate

SMT Spinal Manipulation Therapy

TLIF Transforaminal Lumbar Inter-body Fusion

XLIF eXtreme Lateral Inter-body Fusion

17

Abstract of Dissertation Presented to the Graduate School

of the University of Florida in Partial Fulfillment of the

Requirements for the Degree of Doctor of Philosophy

COMPARATIVE SAFETY AND EFFECTIVENESS OF TREATMENTS FOR

DEGENERATIVE DISC DISEASE: AN ANALYSIS OF THE LUMBAR SPINAL FUSION

PROCEDURE

By

Irene Berita Murimi

December 2015

Chair: Abraham Hartzema

Major: Pharmaceutical Sciences

Significant controversy exists regarding the utility of using spinal fusion procedures for

the treatment of Lumbar Degenerative Disc Disease (LDDD). Considerable research interest has

thus been directed towards curbing the overuse of this intervention and identifying the factors

that influence procedure success. Recombinant human Bone Morphogenetic Proteins (rhBMPs),

a class of osteobiologics used to promote bone growth during some spinal fusion procedures, are

commonly used to increase the likelihood of achieving solid fusion. Our main objective was to

assess the relative effectiveness and safety of recombinant human Bone Morphogenetic Proteins

during LDDD-indicated fusion procedures.

We examined the association between rhBMP use and post-procedure health care

utilization patterns by examining the effect of the osteobiologic on 1) Refusion Risk, 2) Patterns

of inpatient services, 3) Use of Emergency Room Services. Also analyzed is the suspected cancer

risk linked to the use of these osteobiologics. Using data from the Multi-Payer Claims Database

2007-2010 (MPCD), we were created cohorts of fusion procedure recipients based on the

18

specific needs of the each analysis in which patients who received the osteobiologic were

compared to propensity score matched controls.

Our investigation suggests that the association between the use of the osteobiologic and

the risk of undergoing a subsequent refusion procedure varies based on the condition for which

the original surgery was conducted. Additionally, we were unable to confirm that the use of

rhBMPs during LDDD-indicated spinal fusion procedures led to a decrease in the use of

inpatient and Emergency Room services. From a safety standpoint, we found no evidence to

indicate that the use of these osteobiologics was associated with an increased risk for cancer

diagnosis or that purported linked between rhBMP use and the risk for developing cancer varies

based on the spinal region operated on.

19

CHAPTER 1

INTRODUCTION

Background

Americans spend upwards of $86 billion annually on the diagnosis and treatment of neck

and back pain. Though not all cases of spinal pain are attributable to spinal disc degeneration,

some researchers have found it to be an important contributor.1-5

Deterioration of spinal

structures is a near universal consequence of aging. However in some instances this damage to

the structural integrity of spinal discs is associated with persistent pain -- a condition referred to

as symptomatic Degenerative Disc Disease (DDD).

Several treatment options exist for the management of symptomatic DDD including

physical therapy, chiropractic manipulation, spinal injections and oral analgesics. Of particular

interest is the increased use of surgical interventions as tertiary treatment alternatives in cases

that are unresponsive to non-operative solutions. The fusion or arthrodesis procedure was first

developed in 1911 for the treatment of Pott Disease.6 The operation, which has since undergone

significant refinements, is now used extensively for the treatment of several degenerative spinal

conditions including DDD, scoliosis and stenosis. As the name suggests, the procedure is used

to stimulate bony growth between two adjacent vertebrae leading to a single fused unit. It is

posited that the discogenic pain observed in some DDD patients stems from suboptimal

intervertebral distance and spinal instability; in these cases, the fusion procedure ameliorates the

pain by removing the deteriorated disc and creating a stable bony segment in its place.7

Previous studies comparing the efficacy of this surgical approach to the effectiveness of

non-operative treatment approaches have produced mixed results.8-13

Concerns exist that the

procedure itself may in fact worsen the patient’s health trajectory. For example, a subset of

20

fusion procedure recipients, estimated to be as high as 40% in some studies, fail to attain solid

fusion between the two adjacent vertebral segments.14

This failure to fuse, also known as

pseudarthrosis, is associated with recurrent pain and often necessitates a revision procedure.14-17

Moreover recent scholarship on the safety of the osteogenetic factors employed during fusion

procedures, particularly recombinant human Bone Morphogenetic Proteins (rhBMPs), has raised

further questions regarding the risk-benefit ratio associated with pursuing this surgical

approach.18-24

Need for Study

In the period between 1998 and 2008, the number of spinal fusion surgeries performed in

the United States went up by 137% as the costs associated with the procedure increased 7.9

fold.25

While the dramatic increase in the use of these procedures may be attributable to

technological advances and the shift towards a more aged population; some argue that these

procedures are being overused.26

Part of the controversy stems from the disparate conclusions presented in studies on the

efficacy and safety of fusion procedures. While differences in fusion procedure techniques not

to mention patient, physician, and disease characteristics may explain some of the variability in

surgical outcomes; the exact patterns and reasons for this heterogeneity remain poorly

understood.8,26-29

For example, most of what is known regarding the relative efficacy of fusion

procedures vis–à–vis non-operative treatments is based on three pivotal research projects: 1) a

2001 study from Sweden which concluded that the fusion procedure was more effective than

physiotherapy at reducing low back pain complaints, 2) a 2003 Norwegian project which

inferred that a combination of cognitive and physical exercises was as efficacious as the fusion

procedure at pain reduction and 3) a 2005 UK study that found no evidence to suggest fusion

21

better than intensive rehabilitation at managing chronic low back pain.30-32

The heterogeneity

observed in these seminal papers, both in terms of the study designs used and the conclusions

arrived at, underscores the need for more targeted studies that address the specifics of the

indication, patient population and comparator treatments of interest.

Furthermore, few papers speak to the specifics of DDD; in fact majority of what is known

about fusion procedures is based on studies that have analyzed the numerous degenerative

conditions of the spine en masse.16,33-35

Controversy over coverage for Lumbar Degenerative

Disc Disease (LDDD) - indicated spinal fusion procedures highlight the need for LDDD-specific

analyses of this procedure’s relative merits and risks.36

Purpose of Study

The core objective of this dissertation was to critically assess the effectiveness and safety

of rhBMPs as used in LDDD-indicated lumbar spinal fusion procedures. Firstly, we sought to

identify the patient and procedural correlates of rhBMP use during LDDD-indicated fusion

procedures. This descriptive endeavor was used to characterize the systematic differences

between users and non-users of these osteobiologics.

The second goal of the project was to investigate whether patients who received rhBMPs

during their lumbar fusion procedures did in fact fare better than patients whose fusion

procedures did not include the osteobiologics. The parameters for comparison were 1) the

incidence of refusion procedures, 2) hospitalization and Emergency Room (ER) visit patterns

following the procedure and 3) changes in the patient’s use of opioid analgesics.

Lastly, we attempted to investigate the association between rhBMP use and the risk for

new onset cancer. Although only approved for use in the lumbar spine, as many as 14% of the

fusion procedures that utilized the osteobiologic in 2011 were performed in the cervical region.37

22

Since each region of the spine, with its unique structure, function and positional relationship with

adjoining organs, often presents distinctive surgical risks, 38,39

we also aimed to clarify whether

association between rhBMP exposure and the risk post-procedure cancer diagnosis varied based

on the region of the spine treated.

Study Significance

To our knowledge, this study constitutes the first published attempt at linking the use of

rhBMPs during fusion procedures to opioid analgesic utilization patterns after surgical

intervention and at specifically examining the potential association between rhBMP-augmented

cervical fusion procedures and the risk for cancer. Moreover, given the scarcity of LDDD-

specific investigations, even the analysis of previously explored fusion procedure treatment

outcomes including readmission and refusion procedure rates represent an expansion of the

current literature.

Our decision to quantify LDDD-specific treatment effects fills a void in the existing

literature which has, by and large, endeavored to study degenerative conditions of the spine

collectively. The methodological challenges addressed in the process of defining the DDD

population and characterizing fusion treatment outcomes in a claims based environment have

probable implications not only to the field of medical device epidemiology but also to other areas

of chronic pain treatment analysis. Using data culled from all the three main payers in the United

States, this dissertation accessed a demographically and clinically diverse population that is

arguably generalizable to this country’s LDDD patient population.

This focused analysis of LDDD-related fusion procedures has the potential to support

optimal medical decision making and assist in drafting funding guidelines that are both fiscally

and clinically responsible.

23

Research Questions and Hypothesis

This dissertation is organized into three main parts. Unless otherwise noted, statistical

significance was assessed at a type I error (α) rate of 0.05.

Part I: Correlates of rhBMP Use during LDDD-indicated Lumbar Fusion Procedures

Research question 1: Are the baseline characteristics of patients receiving rhBMPs-

augmented fusion procedures similar to those whose surgeries do not utilize the osteobiologic?

Hypothesis 1: HA: Baseline characteristics of patients receiving rhBMPs-augmented

fusion procedures are significantly different from those whose fusion procedures did not utilize

rhBMPs. HO: Baseline characteristics of patients receiving rhBMPs-augmented fusion

procedures are similar to those of patients whose surgeries did not include the osteobiologic.

Part IIA: Association between rhBMPs Use and Subsequent Refusion Procedures

Research question 2a1: Does the use of rhBMPs during the lumbar fusion procedure

reduce the risk for a revision fusion procedure?

Hypothesis 2a1: HA: The use of rhBMPs during the lumbar fusion procedure is

associated with significantly fewer refusion procedures. HO: Fusion procedures that utilize

rhBMPs are just as likely to necessitate a refusion operation as surgeries conducted without the

osteobiologic.

Research question 2a2: Does the association between rhBMP use during the fusion

procedure and revision procedure vary based on the indication for the initial procedure?

Hypothesis 2a2: HA: The association between rhBMP use and the need for a subsequent

refusion procedure varies based on the indication for the primary operation. HO: The association

between rhBMP use and the risk for a subsequent refusion procedure does not vary based on the

indication for the primary operation.

Part IIB: Effect of rhBMP Use on Post-Discharge Hospitalization Patterns

Research question 2b: Does the use of rhBMPs during the fusion procedure reduce

demand for inpatient health care services?

Hypothesis 2b: HA: Use of rhBMPs during the fusion operation is associated with

significantly lower rates of hospital readmissions than non-rhBMP fusions. HO: The use of

inpatient services following the fusion event is independent of the patient’s rhBMP exposure

status.

Part IIC: Effect of rhBMP Use on Post-Discharge Emergency Room Visit Patterns

Research question 2c: Does the use of rhBMPs during the fusion procedure lead to

fewer LDDD-related emergency care visits?

Hypothesis 2c: HA: Subjects who received rhBMP-augmented fusion procedures are

less likely to seek DDD-related emergency care services following the operation than subjects

24

whose surgeries did not involve osteobiologic HO: The use of emergency care services following

the fusion operation is independent of the rhBMP exposure status.

Part IID: Effect of rhBMP Use on Changes in Opioid Analgesic Use

Research question 2d1: Are patients who receive rhBMP-augmented fusion procedures

more likely to discontinue opioid analgesic therapy than those whose surgeries do not utilize the

osteobiologic?

Hypothesis 2d1: HA: Subjects who received rhBMP-augmented fusions were

significantly more likely to discontinue opioid analgesic therapy following the procedure than

subjects whose operations did not involve rhBMPs. HO: The use of opioid therapy following the

fusion event is independent of the rhBMP exposure status.

Research question 2d2: Does the use of rhBMPs during the fusion procedure lead to

greater changes in the opioid analgesic access rates?

Hypothesis 2d2: HA: rhBMP-augmented fusion procedures were associated with a

greater decrease in the amounts of opioid analgesics accessed than rhBMP-naïve procedures. HO:

Changes in opioid analgesic access rates following the fusion event are independent of the

rhBMP exposure status.

Part III: Safety Analysis of Recombinant Human Bone Morphogenetic Proteins.

Research question 3a: Does the use of rhBMPs increase one’s risk for cancer?

Hypothesis 3a: HA: Patients who receive rhBMPs are more likely to be diagnosed with

cancer than those who did not utilize the osteobiologic. HO: The use of rhBMPs is not associated

with significantly increased risks for cancer diagnosis following the fusion procedure.

Research question 3b: Does the association between rhBMP use and subsequent cancer

diagnosis vary based on the location of the fusion procedure?

Hypothesis 3b: HA: Patients who received rhBMPs in the cervical spine are more likely

to be diagnosed with cancer than those who utilized the osteobiologic in the thoracolumbar

spine. HO: The association between rhBMP use and the post-procedure cancer risk is

independent of the location of the procedure.

25

CHAPTER 2

LITERATURE REVIEW

This chapter presents a brief overview of the anatomy of the human spine and the

scientific literature surrounding the fusion procedure. It is organized into five main parts. The

first portion of the chapter offers a description of the human spine and an abbreviated

characterization of the degenerative disc disease (DDD) condition. It is followed by a brief

presentation on the general mechanics of the spinal fusion procedure and its variations. The third

section presents a summary of the recombinant human Bone Morphogenetic Proteins (rhBMPs)

currently approved for use in the U.S market followed by a review of their utilization patterns

among fusion procedure recipients. The fourth part of this dissertation reviews measures of

treatment effectiveness used in this dissertation, their application in DDD-related studies and the

gaps in the literature that we addressed through this project. Lastly, we discuss the rhBMP safety

literature as it pertains to the suspected cancer risk associated with the use of the osteobiologic.

Part 1: The Spine

The Human Spine

The human spine is made up of 33 small bones known as vertebrae. The nine lowest

vertebrae are fused together to form the sacrum and coccyx while the remaining 24 vertebral

segments are divided into three regions: the cervical (neck), the thoracic (middle) and the lumbar

(lower back) spine. The cervical spine refers to first seven vertebrae that connect the base of the

skull to the rest of the spinal column. Naming of the vertebrae is based on their location. All

vertebral segments in the cervical region begin with the letter C; those in the thoracic region

begin with a T, and those in the Lumbar region start with an L. A number is appended to the

spinal region letter based on the vertebrae’s proximity to the skull. For example, the cervical

26

vertebra that is closest to the skull is called the C1 and the one furthest away is the C7. The

thoracic and lumbar regions are made up of 12 and 5 vertebra respectively.

Thoracic and lumbar vertebrae are made up of 6 distinct regions: the centrum or body, the

pedicle, the facet, the transverse processes, the lamina and the spinous process (Figure 2-1). The

spinal cord runs through the vertebral foramen—a hollow channel that exists between the

vertebral body and the posterior arch. Most adjacent vertebrae join at three points -- on the

vertebral body and on the pair of articular processes of the vertebral bone. The latter joints are

known as facet or zygapophysial joints.

The intervertebral disc is made up a gelatinous center known as nucleus pulpous which is

surrounded by two cartilaginous endplates on the interior and superior surfaces, and a thick

fibrous cartilage layer called the annulus that covers its perimeter.40

The gelatinous composition

of the disc allows for the transfer of mechanical loads through the spinal column thereby

facilitating flexing and bending.40

With the exception of the C1-C2 joint, all adjacent vertebral

bones connect with each other via intervertebral discs which sit on the body of the vertebrae

forming the anterior spinal joint between neighboring vertebrae.

Characterizing Disc Degeneration

Disc degeneration is both a mechanical and biochemical process.40

With age, the

delineation between the annulus and nucleus of the disc becomes less clear as the nucleus turns

less gelatinous and the annulus structures lose their structural definition.40

Biochemically, disc

degeneration is associated with the loss of proteoglycan, a class of proteins that regulates the

hydration of the disc and, by extension, its osmotic pressure.40

Compression of the nerves due to

a decrease in vertebral height and the inflammation of the disc material are some of the

hypothesized pathways through which disc degeneration translates into perceived pain.41

27

There are several distinct conditions that can stem from the degeneration of intervertebral

discs including spinal stenosis and disc herniation. As the annulus deteriorates or tears, contents

of the intervertebral disc can bulge out or slip from their place on the vertebral body resulting in

a herniated disc. In some instances, the deteriorated disc protrudes into the vertebral foramen

leading to the narrowing of the spinal canal which is also known as stenosis. Much of the

controversy regarding the use of surgical intervention in disc degeneration cases stems from a

lack of clarity regarding the mechanism through which the surgery resolves patient symptoms.

In both disc herniation and spinal stenosis, pain primarily stems from the restriction of spinal

nerves. Since treatment involves restoring adequate pathways for the neural network, the use of

surgery to treat these conditions is less controversial than its application in cases of non-specific

degeneration of the disc.42

This dissertation is thus focused on investigating the use of fusion

procedures in cases of lumbar degenerative disc disease (ICD-9-CM: 722.52) where the paucity

of conclusive evidence is most acute.

Part 2: The Lumbar Spinal Fusion Procedure

This section consists of a brief overview the spinal arthrodesis procedure including the

main techniques used to attain vertebral fusion. There are five main types of lumbar fusions

approaches: the Anterior Lumbar Interbody Fusion (ALIF), the Posterior Lumbar Interbody

Fusion (PLIF), the Posterolateral Fusion (PLF), the Lateral Interbody Fusion (LIF) and the

Circumferential Fusion (360o).

The Anterior Lumbar Interbody Fusion (ALIF)

As the name suggests, the ALIF involves access to the fusion site through the abdomen or

the front of the spine. During the procedure the surgeon retracts the abdominal muscles in order

to reach the spinal column. A vascular surgeon is often required to facilitate navigation around

28

the aorta and the venacava. Once reached, the deteriorated disc is then removed and an

osteoinductive material (bone graft or rhBMP) is put in its place to promote bone growth.

On the plus side, the ALIF affords the surgeon efficient exposure to a wide surgical area.

The anterior approach also limits the risk of damage to spinal nerves and back muscles which run

through the posterior side of the vertebrae. On other hand, coming in through the front of the

spine means working around the aorta and the vena cava thus heightening the risk of vascular

injury.43,44

The Lateral Interbody Fusion (LIF)

DLIF stands for Direct Lateral Interbody Fusion. Involving a smaller incision, this

approach is lauded as a safer alternative to open procedures. The fusion site is accessed through

the side rather than the front or the back. Instead of lifting the psoas musculature from the

lateral surface and retracting it back, the DLIF approach works through it by separating the

muscle fibers.45

A specialized retractor, manufactured by Medtronic Inc. (Memphis, TN), is

used to facilitate the surgery through the small incision.46

The problematic disc is removed in a

piecemeal fashion through the incision point and replaced with an osteoinductive material and a

spacer as needed.

XLIF stands for eXtreme Lateral Interbody Fusion. Like the DLIF, the XLIF involves a

lateral approach to access the fusion site through a small incision site. The XLIF uses a

specialized retractor manufactured by Nuvasive® Inc. (San Diego CA) to separate the psoas

muscles.47

Once the intervertebral disc of interest is reached, it can then be removed and replaced

with osteoinductive materials and a spinal implant as needed. Neuromonitoring is conducted

throughout the procedure to ensure that the retractor does not compromise any nearby neural

29

structures.46

Like the DLIF, this minimally invasive procedure is associated with minimal blood

loss, smaller scars and shorter recovery periods.48

The Posterior Lumbar Interbody Fusion (PLIF)

The Posterior Lumbar Interbody Fusion procedure or PLIF is conducted through the back

of the spine. During the procedure, the lamina is removed allowing access to the disc space. If

needed, the facet joints may be trimmed or adjusted to permit access to the disc space.49

The

surgeon has to work around the neural network in order to replace the deteriorated disc with

osteoinductive materials for the fusion.

The posterior approach offers some distinct advantages over the ALIF procedure. Firstly,

by coming in from behind, the surgeon is able to apply multiple solutions such as neural

decompression and rigid fixation, through a single entry point.50

The procedure also carries a

lower risk for vascular complications since both the aorta and the vena cava are located on the

anterior edge the spine.50

When compared to the posterolateral fusion technique, the PLIF has

been shown to produce significantly higher fusion rates.49

On the other hand, the PLIF approach

offers less exposure to the body of the vertebral bone than the ALIF.50

Limited exposure means

less space for inserting a larger, more stabilizing biomechanical implant. The PLIF also carries a

higher risk of injury to the spine’s neural structures which run through the posterior side of the

vertebrae.49,50

The Transforaminal Lumbar Interbody Fusion (TLIF)

The Transforaminal Lumbar Interbody Fusion or the TLIF was introduced in the early

1980s and is considered to be a modification of the PLIF.51

Like the PLIF, the TLIF incision is

made in the back. The entire facet joint is then removed minimizing the need to retract the thecal

sac; limiting the handling of the spinal cord, of which the thecal sac is a component, lowers the

30

risk for neurological injury.49,51

Removal of the facet joint also allows the surgeon to place the

osteoinductive material and spinal implant towards the anterior edge of the disc space for

maximal bone formation.49,52

The lamina can then be used to achieve posterior fusion through

the use of pedicle screws and rods. The main advantage of the TLIF over the PLIF is the

decreased risk of neurological damage.49

The Posterolateral Fusion

As the name suggests, a posterolateral fusion is performed using a posterior approach. In

contrast to the ALIF, PLIF, TLIF, XLIF and DLIF, the posterolateral fusion involves placing the

bone graft material on the transverse processes of the vertebra. Like other posterior approach

fusions, this procedure carries a lower risk for vascular injury and retrograde ejaculation.50

However, fusion rates are reported to be lower after a posterolateral fusion procedure than a

PLIF which is partly explainable by the lower vasculature in the transverse process as compared

to vertebral body.49

The Circumferential Fusion (360º)

A circumferential fusion, also known as the 360º reconstruction, involves both a front and

back incision. It combines an ALIF procedure with the posterolateral fusion.53

The front

incision is used to access the anterior vertebral body, remove the deteriorated disc and place a

fusion cage in its place. Once complete, another incision is then made in the lower back to

facilitate the placement of pedicle screws and rods that provide rigid posterior fixation. With

twice as many incision sites as the other fusion techniques, the circumferential procedure carries

more risk. The front incision increases the risk for vascular complications while the posterior

access point can cause damage to the back muscles and spinal nerves.54

On the other hand, this

procedure allows the patient to benefit from the strengths of both the anterior and posterior

31

approaches. By going through the front, the surgeon is afforded ample space for placing an

inter-body fusion device while the posterior incision allows for nerve decompression and rigid

fixation. Circumferential procedures are reported to have the higher rates of the effective unions

than other lumbar fusion approaches.55

Part 3: Recombinant Human Bone Morphogenetic Proteins

Part 3 of this chapter offers a summary review of recombinant human bone

morphogenetic proteins (rhBMPs) products and the correlates of their use as reported in the

existing literature. Studies published regarding the effectiveness and safety of rhBMPs are

discussed in the sections that follow.

Recombinant Human Bone Morphogenetic Proteins in the U.S Fusion Market

There are two approved rhBMP products in the U.S market namely the Osteoinductive

Protein (OP)-1®

PUTTY and INFUSE™ Bone Graft. A third product, AMPLIFY™ rhBMP-2

Matrix, failed to secure FDA approval.

OP-1® PUTTY

The main component of the OP-1® Putty (Olympus Biotech

®, Kalamazoo, MI) is

genetically engineered rhBMP-7. The protein is sold in 3.3 milligram powdered units in

combination with a Type I Bovine Collagen Matrix and a putty additive which is comprised of

sterile carboxymethylcellulose (CMC).56

At the time of use, the CMC and a 0.9% saline solution

are combined with the protein powder to form an osteoinductive putty-like substance with an

approximate concentration of 1.65mg/ml. The manufacturer recommends that two units of the

putty or 6.6.milligrams of rhBMP-7 be used per intended fused joint.56

OP-1® Putty was first

approved in 2001 for intractable long-bone non-unions.57

Its indications were expanded in 2004

to include posterolateral spinal revision procedures in patients who are ineligible for autologous

32

bone grafts.58

Approvals for both indications were granted under the FDA’s Humanitarian

Device Exemption (HDE) program.57,58

Similar in intent to the Orphan Drug initiative, the HDE

program was designed to encourage the development of devices that can be used to diagnose or

treat conditions affecting less than 4,000 people in the U.S annually. Once approved by FDA,

the Humanitarian Use Devices (HUD) must also be endorsed by the local institutional review

board (IRB) prior to use. The IRB’s consent certifies the clinical appropriateness of using the

device within its jurisdiction.59

INFUSE™ Bone Graft/LT- CAGE™ Lumbar Tapered Fusion Device.

The INFUSE® Bone Graft (Medtronic Sofamor Danek, Memphis, TN) was approved for

use with a specific device--the LT- CAGE™. The manufacturer has since received approval for

two other devices to be used with the InFUSE Bone Graft: the INTER FIX™ Threaded Fusion

Device and the INTER FIX™ RP Threaded Fusion Device. Like the OP-1 Putty, the INFUSE™

bone graft substitute uses Type I Bovine Collagen Matrix as a carrier substrate for the rhBMP.

The main active ingredient in INFUSE™ is genetically engineered rhBMP-2 that is cultured

from a Chinese hamster ovary cell line. The protein is sold in lyophilized powder form which, at

the time of use, is reconstituted in manufacturer provided sterile water to form an rhBMP-2

solution with a concentration of 1.5 mg/ml.60

While this FDA approved concentration is fixed,

the actual volume of rhBMPs placed into a patient is allowed to vary from 4.2 milligrams to 12

milligrams.60

The fusion cages that are marketed with the product are designed to provide

mechanical support to the bone graft substitute. The INFUSE™ system was approved in July

2002 specifically for the treatment of DDD at the L4- S1 region of the spine using the ALIF

procedure.61

33

AMPLIFY™ rhBMP-2 Matrix

Medtronic Sofamor Danek (Memphis, TN), the makers of INFUSE™ Bone Graft/LT-

CAGE™ Lumbar Tapered Fusion Device, sought approval for the AMPLIFY™ rhBMP-2

Matrix. The AMPLIFY™ rhBMP-2 Matrix system was comprised of a 40mg/mL preparation of

rhBMP-2, a Compression Resistant Matrix and a metallic posterior fixation system and was to be

used for single level posterolateral lumbar spinal fusions (L1-S1).62-64

In July 2010, the FDA’s

Orthopedic and Rehabilitation Devices Advisory Panel advised against the product’s approval

citing the significantly higher incidence of new cancer diagnosis in patients exposed to

AMPLIFY™ than in the comparator group.64

Nine of the 239 subjects who received

AMPLIFY™ were diagnosed with cancer, a rate that was not only higher than the comparator

group but also higher than the numbers observed in the InFUSE™ trials.23

This product, though

not approved by the FDA, has influenced many discussions about the safety of rhBMPs.

Contraindications of rhBMP Use

The contraindications for the InFUSE™ system and the OP-1® Putty are very similar.

Both are not recommended for women who are pregnant or planning to become pregnant. The

precise risk profile of rhBMP exposure in pregnant humans remains unstudied. What is known

is that maternal antibodies to rhBMPs can remain in the system for up to 2 years after initial

exposure.65

Some of the concern stems from the fact that several stages of fetal development rely

on the expression of autologous bone morphogenetic proteins raising fears that presence of

exogenous rhBMP-induced antibodies can trigger a deleterious immune response in both mother

and child.65,66

The use of rhBMPs products is also not advised in people who are skeletally

immature, immunocompromised, have an infection, have had a tumor removed at the surgical

34

site or are allergic to any of the materials used to create the mechanical component of the

device.57,58,61

Utilization Patterns and the Correlates of rhBMP Exposure

Most of what is known regarding the use of rhBMPs in the United States has been

garnered from the National Inpatient Sample (NIS) database.27,67-69

The NIS is the single largest

repository of inpatient medical care data in the United States. It includes information on the

diagnoses and procedures undertaken during inpatient stays, admission and discharge status,

hospital characteristics and patient demographic identifiers. Stratified sampling is used to ensure

proportional representation of U.S community hospitals based on geographical region, location,

teaching status, ownership and bed size. Over 1000 community hospitals, or approximately 20%

of the sampling frame, are selected annually to contribute information on their hospital

admissions to the database. Once weighted, the data constitutes a nationally representative

sample of all in-patient stays in U.S community hospitals.70

Although rhBMPs are used in many different applications such as long bone fracture

fixation, vertebral fracture repair and oral surgery, their most common use is in promoting spinal

fusions.67,71

The use of rhBMPs in fusion procedures has increased from 0.69% in 2002 to

27.2% in 2007.27,67,68

As previously noted, rhBMPs were approved for the lumbar ALIF

procedure and for posterolateral revisions under the Humanitarian Device Exemption program.

However of the 340,251 rhBMP-augmented fusion procedures captured in the NIS data between

2003 and 2007, less than 20% were for these on-label indications.67

Most of time, 31.6% to be

precise, these osteobiologics were used in either Posterior Lumbar Interbody Fusion procedures

(PLIF) or Transforaminal Interbody Fusion surgeries (TLIF). Primary posterolateral fusion

35

operations were the second most common procedure type to employ rhBMPs at 20.4% followed

closely behind by spinal procedures in the cervical spine at 13.6%.67

Exposure to rhBMPs is strongly associated with age. Patients over 65 years old are

significantly more likely to receive an rhBMP product during their fusion procedures than

younger adult patients.67

The preferential use of osteobiologics among the very old appears to be

tactical. Osteobiologics have been associated with higher fusion rates than autograft bone

subtrates.23,24,72-74

In this vulnerable population where surgical complications and revision

procedures carry greater risks, the data suggests that surgeons are strategically employing

rhBMPs for their superior osteoinductive properties.

On the other end of the age spectrum, the rhBMP exposure rate is also non-trivial.

Although neither the OP-1®

Putty nor the INFUSE™ system is approved for pediatric

populations, a recent study found that 9.2% of the arthrodesis procedures taking place in children

employ rhBMPs to promote bone growth.75

The main indications for which children receive

rhBMP-augmented fusion procedures are adolescent idiopathic scoliosis, Scheuermann

kyphosis, congenital scoliosis, thoracolumbar fracture and spondylolisthesis.76

However given

the rarity of degenerative disc disease in children, the pediatric population is excluded from this

dissertation research.

Cahill et al.’s (2009) analysis of the 2003 to 2007 NIS data also found women and

Whites to be more likely to receive rhBMPs than males and minorities respectively

(OR(95%CI): 1.12 (1.09, 1.16) for Women vs Men, OR(95%CI): 0.80 (0.75, 0.85) for

Nonwhites vs Whites).27

Also observed was that Medicaid beneficiaries were less likely to be

treated with rhBMPs than Medicare and privately insured patients.27,77

Regional variations in the

use of rhBMPs have also been noted. Ong et al. (2010) found Southern States to have lower

36

rhBMPs use rates than other census regions in the U.S.67

Other less significant predictors of

rhBMP use that have been identified include hospital type and size.67

Part 4: Assessment of Treatment Effectiveness

The central objective of this dissertation was to assess whether patients who receive

rhBMP-augmented fusion procedures fare better than those whose surgeries did not employ the

osteobiologic.

The conventional measures of treatment effectiveness used in low back pain studies are

pain reduction, improved quality of life and functional status and, in the case of fusion

procedures, radiographic evidence of solid fusion.13,23,24,78

Pain is routinely measured using pain

scales, while patients’ health related quality of life and functional status are commonly assessed

using the Medical Outcomes Study Short Form 36 (SF-36) survey and the Oswestry Disability

Index (ODI) respectively. Pain scales, SF-36 and ODI sores are examples of patient reported

outcomes which, by their character and intent, are seldom captured in administrative claims

datasets.23,24

Our research utilized four alternative indicators of effectiveness: initiation of

revision procedures, change in pain medication use and the use of inpatient and emergency room

services.

Health care utilization patterns are a key stone feature of claims-based

pharmacoepidemiology research. Their use is built on the assumption that patients’ medical care

encounters are driven by their health status. Significant research effort is thus continuously

employed to validate the association between the presence of a reimbursement claim in the

patient’s record and his medical conditions as observed in clinical charts.79-82

These validation

studies are aimed at determining the combination of medical claims needed to accurately identify

patients with the condition of interest.82,83

37

Our research was built on an alternative premise which stipulates that use of medical

resources is in itself an adequate marker of an intervention’s success. Under this proposal, we

need not validate the association between the use of pain medications and the patients’

underlying pain since our interest is not in the fact that the intervention leads to a reduction in

pain but rather that it reduces the demand for pain medication. Our reasons for using healthcare

utilization patterns are twofold. Firstly, these measures are a pragmatic approach to measuring

the effectiveness of treatment strategies in a claims-based data environment. Secondly, we assert

that medical utilization patterns are beneficial measures of effectiveness in their own right.

Besides the direct medical costs associated with health care encounters, which are outside the

scope of this dissertation, there are several known burdens placed on a person as they access the

healthcare system. These burdens, such as the need to make adjustments to familial or work

obligations in order to go to the emergency room, conceivably play a crucial role in how patients,

providers and the society evaluates the relative merits of an intervention.

The paragraphs that follow outline the relationship between the spinal fusion procedure

and subsequent medical service utilization patterns as presented in the current literature. The

review was used to situate our study within the apparent gaps in the literature and to identify the

known risk factors and confounders that were included in our models.

Revision Procedures

A revision procedure is one of the key measures of fusion procedure failure. Over 60%

of refusion procedures are prompted by a failure to achieve solid fusion or by a device-related

complication.16,84

Several factors can lower the chances of vertebrae fusing together as intended

including infection of the surgical site, attempts at fusing more than four vertebral levels together

and failure to use stabilizing instrumentation during the procedure.14,84-86

Reported rates of

38

revision procedures vary widely from a low of 2.7% to a high of 36% depending on the

indication for which the surgery was undertaken, the demographic profile of the study

population, the surgical approach used in the original operation and the study design.84,87-89

Revision rates are also commonly used to assess the impact of adjunct fusion procedure

decisions on treatment effectiveness. For example, the Randomized Control Trials (RCTs)

designed to obtain regulatory approval for rhBMPs were primarily aimed at demonstrating that

the osteobiologic was comparable to iliac Crest Bone grafts (ICBG) at promoting bone growth

and fusion.23

A meta-analysis of industry sponsored RCTs found that at 24 months the

proportion of subjects who achieved fusion was higher in the rhBMP-2 groups (61%) than in the

controls (53%) although the difference was not statistically significant (OR (95% CI): 1.05

(0.88, 1.24)).23

A small (N=63), non-industry sponsored, prospective cohort study arrived at a

same conclusion: rhBMP use was associated with higher fusion rates than autograft bone

substrates.18,90

However, the same study also reported that patients who received rhBMPs were

more likely to require revision surgeries.18,90

Unlike ICBG fusion procedures that were mainly

revised due to a failure to fuse, the researchers observed that rhBMP-augmented procedures were

mostly revised because of device complications and more specifically due to graft subsidence.90

The conclusions of this study are interpreted in light of its limitations. Firstly, the authors did

not provide the number of refusion events performed during the study thus limiting our ability to

assess the clinical significance of their observations.77,90

Secondly, the study used data from a

single university clinic which restricts the generalizability of its conclusions. A larger

retrospective cohort study (N=16,822) by Deyo et al. sought to examine reoperation rates among

elderly Medicare beneficiaries who received rhBMP-augmented fusions for the treatment of

lumbar spinal stenosis in 2003 and 2004.91

Over a four year follow-up period, the rate of

39

reoperation in the rhBMP-exposed group was comparable to that observed in the rhBMP naïve

group (10.8% vs. 10.5, p value > 0.5).

Deyo’s study and the published reports on the prospective clinical trials still leave some

unanswered questions. For example, what is the association between rhBMP use and refusion

procedure rates in younger adults in a real world setting? Furthermore, since all their

conclusions were based on non-DDD specific populations, is it possible that the effect of

osteobiologic use on the demand for refusion procedures would vary based on the indication for

the original operation?

Inpatient Services

Early readmissions have long been a marker of the quality of surgical care. It is believed

that prompt returns to inpatient care can be curbed by minimizing procedure complications,

providing proper discharge support and availing sufficient outpatient follow-up.92-94

Rates of

readmissions within 30 days of spinal surgery are reported to vary from 2.5% to 12.3%.91,94-97

Some of the variation in these published rates is attributable to differences in the study

population and research designs. Previous reports have shown that older patients, those with

multiple comorbidities and those who receive more complex procedures are more likely to return

to the hospital within 30 days of discharge.97,98

Reasons for early readmission include infection,

device complications, surgical injury and, gastrointestinal, respiratory, renal and unresolved pain

complaints.92-94,97,98

The association between rhBMP use and early readmission is unclear. In Cahill et al.,

researchers observed that the odds of being readmitted within 30 days was 28% lower among

those who received rhBMPs during their lumbar fusion procedures than among the rhBMP-free

controls (adjusted OR (95% CI): 0.72 (0.54–0.95)).35

In contrast, Deyo et al. found no

40

differences in early readmission rates between similarly defined comparison groups.91

There are

several plausible explanations for the conflicting conclusions arrived at by Deyo and Cahill.

Firstly, the two studies examined different clinical populations in that the Deyo study focused on

spinal stenosis procedures while the Cahill project examined all degenerative conditions of the

spine without sub-specification. Secondly, the Deyo study population which comprised of

Medicare beneficiaries was significantly older than the Cahill’s 18-65 year old retrospective

cohort. Lastly, the two studies differed in their designs: while Deyo controlled for confounding

using an explicit multivariate model, the Cahill study employed the propensity score approach at

adjust for a wider array of potential confounders. The list of potential confounders included in

the models also differed; of special note is the fact that the Deyo model adjusted for patients’

race while the Cahill project did not. Although seemingly reasonable, none of hypotheses we put

forth represent an evidence-based definitive explanation for the variability in conclusions.

Continued research is thus warranted in order to clarify the effect of rhBMP use on early

readmissions.

Despite the plurality of published papers on the question of 30-day readmissions

following spinal surgery, none has specifically addressed this issue within the LDDD-population.

Moreover, the analysis of long term readmission patterns is scarce. To our knowledge, only one

study has examined the rate of readmissions beyond the first 90 days following lumbar spinal

surgery. The study, which was conducted by Modhia et al.(2013), investigated the rate of

hospital readmissions in the first two years post-surgery.99

Based on the analysis of Medicare

beneficiaries who had received fusions for spinal stenosis, the researchers observed that 9.9%

and 15.5% of the study subjects had been readmitted at least once by the end of the first and

second year respectively.99

One in five readmissions were initiated to provide inpatient pain

41

management injections while the rest of involved further surgery, presumably due to a failure to

achieve the clinical objectives of the primary procedure.99

While early readmissions are mainly

prompted by complications of the procedure, delayed returns to inpatient care appear to be

driven by the ineffectiveness of the original surgical intervention.99

Since previous research

suggests that the indication for the fusion procedure is associated with the surgery’s perceived

effectiveness, it is therefore conceivable that the results observed by Modhia among stenosis

patients would not necessarily be mirrored in the LDDD population.100

Emergency Room Visits

Very little is known about the use of emergency room visits for chronic back pain. An

analysis of the 2008 Nationwide Emergency Department Sample (NEDS) estimated that there

were 2,397 back pain related ER visits per 100,000 people in the population.101

Based on the

2008 NEDS, patients presenting to the ER with back complaints were mainly female, privately

insured and living in rural areas.102

A second study used the National Hospital Ambulatory

Medical Care Survey (NHAMCS) to examine the use of ERs for back complaints from 2002 to

2006.103

Approximately 2.3% of all visits to hospital based emergency rooms taking place over

this five year period involved back pain. In majority (63.5%) of these visits, the main diagnosis

coded was unspecified back complaints, a category that includes lumbago, sciatica and

radiculitis.103

Only 80 visits, which after accounting for the NHAMCS sampling strategy equates

to 1.9% of the captured health care encounters, were attributed to an intervertebral disc order.103

What NEDS and NHMACS studies do not provide is the relationship between ER visits

and other health care services used by chronic back pain patients. How often do patients revisit

the ER for back complaints? Both NEDS and NHAMCS are cross-sectional datasets which do

not permit the longitudinal follow-up of patients. The published literature we reviewed also

42

failed to distinguish patients diagnosed with LDDD-related back pain from those with

generalized low back pain, with one notable exception: the NHAMCS study by Friedman et al.

(2010) captured 80 intervertebral disc disorder-related ER visits, however it too made no

attempts at clarifying the encounter characteristics of this subgroup of patients.103

Opioid Analgesic Use

One of the most commonly cited advantages of rhBMP use is the circumvention of

autologous bone graft harvesting and the associated donor site pain.23,104

In a meta-analysis of

rhBMP-related clinical trials, researchers observed that patients treated with rhBMP-augmented

fusion procedures reported higher levels of pain reduction from the 6 month mark onwards than

those who received rhBMP-free fusion operations.24

At 24 months post fusion, the pain score

mean difference between the rhBMP group and its controls was -1.58 on a 20 point scale (95%

CI: -2.65, -0.51) which is, as the authors of the paper noted, arguably clinical insignificant.24

Whether these decreases in pain translate to a reduction in the use of analgesics remains a largely

unanswered question.

Opioids are the second most commonly prescribed drugs for pharmacologic management

of chronic low back pain.105

Previous studies indicate that between 33% and 70% of patients

undergoing spinal surgery are on opioid analgesic therapy prior to the procedure.106

Although

opioids are often used to manage immediate post-operative pain, very little is known about the

use of these analgesics in the year after a spinal fusion procedure. Also missing are analyses that

compare the effects of rhBMP use on subsequent opioid use.

To our knowledge, no study to date has examined on the effect of intraoperative rhBMP

use on post-fusion procedure opioid utilization patterns.

43

Part 3: Safety Analysis of Recombinant Bone Morphogenetic Proteins

There are several reported risks associated with the use of rhBMPs during fusion

procedures including retrograde ejaculation, urinary retention, infection and wound

complications, heterotopic bone formation, back and leg pain, and malignancy.18-20,23,24,107,108

Our dissertation focuses of the purported association between intra-operative rhBMP use and

increased cancer risks. This adverse event is singled out because of the significant risk of

morbidity and mortality linked to its occurrence and highly conflicting information that currently

exists in this area of research.

The literature review is presented in two parts. In the first few paragraphs we discuss the

biological underpinnings that could explain an association between rhBMP use and the risk for

cancer. The second section summarizes prior published papers that have either reported on or

investigated the link between rhBMP use and subsequent cancer diagnosis followed by an

explanation of how our proposed study fits in with the existing literature.

Assessment of Biological Plausibility

Bone morphogenetic proteins were first isolated by Marshall Urist in 1965 and made

known for their ability to promote bone growth at extraskeletal sites.109

There are 21 naturally

occurring BMPs in the human body: BMP-2, BMP-3, BMP-4, BMP-5, BMP-6, BMP7, BMP-

8A/B, BMP-10, BMP-15, GDF-1, GDF-2, GDF-3, GDF5, GDF-6, GDF–7, GDF-9, GDF-10,

GDF-11, GDF-15 and myostatin.110,111

BMPs belong to the transforming growth factor β (TGF-

β) protein super family and, like many other TGF- β proteins, they play a critical role in the

regulation of cell differentiation.66

The association between autologous BMPs and tumor development is believed to be bi-

directional.66

Some studies have found BMP-2, BMP-4, BMP-6 and BMP-7 to exhibit anti-

44

oncogenic properties by inhibiting cell proliferation and neoangiogenesis.66,112-114

On the other

hand, several published papers have reported on the pro-oncogenic potential of BMPs.115-119

For

example, a 2007 study by Hamada et al, suggested that BMP-4 may be an inducer of the

epithelial-mesenchymal transition in human pancreatic carcinoma cell lines which is an

important step in the metastasis cascade.116

The specifics of how the various BMPs differentially

affect the development of disparate neoplasms remain an area of active research.

The plurality of cancer types identified following rhBMP- exposure is one of the more

difficult observations to explain. Thirteen different cancer types were diagnosed during the

rhBMP-2 clinical trials.23

They include skin, breast, colon, larynx, leukemia, lung, pancreatic,

prostate, stomach, testicular, non-Hodgkin’s lymphoma, ovarian and thyroid cancers.23

While

some commonalities exist between the different cancer types, these neoplasms are known to

exhibit distinct developmental pathways. It has been hypothesized that cell lines that already

express BMP-2, the naturally occurring corollary of the InFUSE™ and AMPLIFY™ products,

seem to be more susceptible to exogenous sources of the protein. An in-vitro study by Kokorina

et al examined the effect of rhBMP-2 exposure on human oral squamous cell carcinomas

(OSCCA) in which three cell lines expressed autologous BMP-2 at baseline and another three

did not.120

Once exposed to rhBMP-2, only cell lines that had expressed BMP-2 at baseline

displayed a significant increase in tumor cell invasion activity.120

In a follow-up analysis,

Kokorina and colleagues introduced oral squamous cell carcinoma xenografts to six week old

mouse models.22

Prior to implantation, half of the xeonografts were pretreated with a 100 ng/mL

rhBMP-2 solution for 48 hours while the other half remained rhBMP-2 naïve.22

The mice who

received rhBMP-2 exposed xenograft developed more aggressive tumors and posted worse

survival times than the mice in the control group.22

This Kokorina study gives rise to an

45

alternative hypothesis: the use of the rhBMPs accelerates the progression of subclinical tumors

thus increasing the chances of their detection.

The risk with exogenous BMPs, as acclaimed Orthopedic Surgeon Eugene Carragee

asserts, lies in their concentrations.77

Naturally occurring BMP-2 exists in the human body at

concentrations of 0.1ng/mL.77

At 1.5mg/mL, the FDA approved rhBMP-2 product is 15 million

times more concentrated than naturally occurring levels of the protein in the human body.

Introducing such drastic changes to the levels of BMP-2 in the body can arguably interfere with

the normal functions of the protein in the body.77

This dose dependent argument is further

strengthened by the results of the AMPLIFY™ trial. Twelve of the 17 new cancer cases

diagnosed during the all the rhBMP RCTs were found in patients who used the 40mg/mL

preparation of the protein.121

Overview of rhBMPs and Cancer

Several studies that have examined the association between the use of these commercially

available rhBMPs in the thoracolumbar spine and the risk for new-onset cancer diagnosis have

yielded conflicting results.21,33,77,122,123

In 2004, Wyeth, the then manufacturer of rhBMP-2,

identified 3 pancreatic cancer cases during a routine safety review of the 1008 subjects who had

received rhBMP-2 during their clinical trials.21

With the United States population-based

pancreatic cancer incidence rates as reference, these 3 cases reflected a standardized incidence

rate of 16 (95% CI: 3.3–46.8) and therefore a potential safety signal.21

Two small RCTs

designed to evaluate the efficacy of rhBMP-7 in posterolateral fusions found non-significant

differences between the number of new cancers identified in the intervention group compared to

the control group (12.5% versus 8.3%, p value = 0.7; 5.6% versus 0% p value = 0.56) while the

initial data summaries of the InFUSE™ trials submitted to the FDA reported one cancer case in

46

each of the rhBMP-2 and comparator groups.21,124-127

In contrast, the cancer risk was found to be

higher in the AMPLIFY™ trial which used a more concentrated preparation of rhBMP-2 in

posterolateral fusion applications. Of the 439 subjects in the AMPLIFY™ study, 15(6.3%) of

the subjects who were given the biologic were diagnosed with cancer compared to 5(2.2%)

subjects in the control group (p value = 0.04).3 In a pooled analyses of all rhBMP-2 randomized

clinical trial data, 17 of the 633 subjects in the rhBMP-2 group were diagnosed with cancer

within the first 24 months compared to 6 out of 817 in the comparison group.23

At 24 months, the

odds of having a new cancer diagnosis was approximately 3 times higher among rhBMP-2

exposed subjects than in the comparator population (OR (95% CI): 3.45 (1.98, 6.00)).23

Majority

of the cancer cases occurred in the AMPLIFY™ trial where 9 of the 239 subjects in the rhBMP-2

group were diagnosed with cancer by the 24 month mark.24

A second aggregation of the clinical

trial data further concluded that although the raw numbers were higher, the relative risk for

cancer in the AMPLIFY™ trial was statistically comparable to what was observed during the

InFUSE™ trials (RR (95% CI), 1.98 (0.86 to 4.54), p=0.82).24

In the end, neither pooled

analysis spoke conclusively regarding the potential association between rhBMP-2 exposure and

new cancer diagnosis citing the low incidence (3%) and the heterogeneity in the cancer types

observed as impediments to statistical certainty.23,24

A few observational studies have attempted to reevaluate the association between rhBMP

exposure and cancer incidence. Mines et al. sought to quantify the association between the

rhBMP-2 use and pancreatic cancer in an elderly Medicare population. The study included

93,654 lumbar fusion patients of which 15,460 (16.5%) had received rhBMPs during the

procedure. Over a mean follow-up period of 1.4 years, the frequency of the pancreatic cancer

was 0.05% (8/15460) in the rhBMP group and 0.1% (83/78194) in the comparator population.21

47

After adjusting for age, sex, race, length of follow-up and history of diabetes mellitus, alcohol

abuse and chronic pancreatitis, the researchers found no evidence to support an association

between rhBMP use and pancreatic cancer (adjusted HR (95% CI): 0.70(0.34,1.45)).21

Lad et al. examined the incidence of both benign and malignant tumors following

exposure to rhBMP.77

Unlike the Mines study which used all lumbar fusion recipients

irrespective of the indication for the procedure, Lad’s analysis was restricted to patients who

underwent the procedure for a spinal stenosis indication and used propensity score methods to

establish comparability between the rhBMP and the comparator group. Only benign tumors

were found to be associated with rhBMP use from the propensity score matched analysis of 4698

subjects. Statistically, the incidence of benign neoplasms was significantly higher among the

rhBMP exposed (6.3%vs 4.9%, OR (95% CI): 1.31 ( 1.02, 1.66), p value = 0.04;).77

When

stratified by cancer type and organ, only the diagnosis of benign neoplasms of the central

nervous system appeared to be significantly associated with rhBMP exposure (0.8% vs 0.3%, p

value = 0.03). However, the absolute number of new cases was low (19/2349 vs. 8/2349)

thereby raising questions about the clinical significance of their conclusions.

Another smaller observational study has also been published. Latzman et al conducted a

retrospective chart review of all lumbar fusion procedures that took place at the New York

Harbor Health Care System’s Manhattan Veterans Administration Center between July 2000 and

June 2008.122

The total study population was 124, twenty four of whom received rhBMP-2 with

their procedure. Twelve of the 124 patients were diagnosed with cancer following their

procedure. There was notable variability in the types of neoplasms that developed: basal cell

carcinoma, colon, prostate, lung, rectal, pancreatic and bladder adenocarcinomas. The average

length of time between the fusion procedure and cancer diagnosis was 20 months, with 9 of the

48

12 cancers cases identified within 25 months of the procedure. The rate of new cancer diagnosis

was higher in the rhBMP-2 group (4/24, 17%) than in the control group (8/101, 8%) although the

difference was not statistically significant (p value = 0.12).122

A recent meta-analysis of the existing literature found no evidence to indicate that use of

rhBMPs during fusion procedures was associated with an increased risk for cancer.128

However,

the strong evidence of publication bias detected by the researchers and the significant

heterogeneity between the studies analyzed limits the reliability of the meta-analysis’

conclusions.

Moreover, little is known about the cancer risk associated with using rhBMPs in the

cervical spine. Although only approved for use in the lumbar region, as many as 14% of the

spinal fusion procedures that utilized the osteobiologic in 2011 were performed in the cervical

region.129

To our knowledge, only one published study has included rhBMP-augmented cervical

fusion procedures when assessing rhBMP-related cancer risks.123

The study in question, a

retrospective cohort analysis of 467,916 Medicare beneficiaries in which cervical and

thoracolumbar spinal fusion procedures were investigated collectively, found that the use of

rhBMPs was not associated with an increased risk for cancer diagnosis.123

The fact that certain

rhBMP-related adverse events such as cervical swelling only occur after osteobiologic use in the

cervical spine underscores the need to assess whether the association between rhBMP exposure

and post-procedure cancer diagnosis varies based on the region of the spine treated38,39

49

Tables and Figures

Figure 2-1. Annotated schematic of a lumbar vertebral segment

50

CHAPTER 3

METHODOLOGY

Introduction

This chapter begins by discussing the data source employed in the dissertation followed

by a detailed outline of the study design. For clarity, a separate study plan, including

specifications on the study population and the analytical approaches used, are presented for each

section of the dissertation. To minimize redundancy in the text, the reasons behind commonly

used design elements such as propensity score caliper matching are only presented once.

Data management and analysis was conducted using SAS 9.4®

statistical software (SAS

Institute, Cary, NC) while all graphics, unless otherwise noted, were created using Microsoft

Office 2010® (Microsoft Corporation, Redmond, WA). This research is approved by the

University of Florida’s Institutional Review Board (IRB#201300266 and IRB#201300386) and

by the Food and Drug Administration (FDA)’s Research Involving Human Subjects Committee

(RIHSC# 13-057R).

Data Source

This dissertation used data from the Multi-Payer Claims Database 2007-2010 (MPCD), a

pilot dataset commissioned by the Department of Health and Human Services to support

comparative effectiveness research. Medical claims like those contained in the MPCD offer

convenient access to detailed health care information on large arrays of unique patients. The

utility of this class of data is however limited by its original intent. Administrative claims data is

collected for reimbursement purposes and as such all services that are not paid for by the

insurance plan are seldom, if ever, reliably captured. Moreover, the coding systems used

occasionally lack the specificity to accurately describe the clinical conditions they are purported

51

to represent. Several study design decisions were made to moderate the effects of these

limitations and all the results obtained are discussed in light of the data’s shortcomings.

The MPCD (2007-2010) is comprised of longitudinal reimbursement data from both

public (Medicare and Medicaid) and private payers. The main private insurance data contributor

was United HealthCare® (UHC). Approximately 80% of the privately insured subjects in MPCD

were directly covered by UHC with the remaining 20% coming from smaller data partners who

received incentives from the MPCD data management contractor, OPTUMInsight™.130

The

publicly insured population was contributed by the Centers for Medicare and Medicaid Services’

(CMS) Chronic Conditions Data Warehouse and consisted of a 15% random sample of the

warehouse’s patient population. The resulting database contains de-identified patient level

information on demographic descriptors, enrollment patterns, and on the pharmacy and medical

services reimbursed. A common data model was developed to combine records from the

disparate MPCD data contributors and to facilitate longitudinal follow-up of patients across the

different service years.

A project specific subset of the database comprising of Degenerative Disc Disease

(DDD) patients was created for the purposes of this research. To create the DDD patient

population, all subjects who had at least one health encounter for the condition were extracted.

Diagnosis status was established using International Classification of Diseases, Ninth Edition,

Clinical Modification [ICD-9-CM] codes for disc degeneration (722.4, 722.5, 722.51, 722.52,

and 722.6). The resulting data extract, hereafter referred to as MPCD, was made up of slightly

over 2.9 million unique subjects from all 50 states, the District of Columbia, Guam, Puerto Rico

and the Virgin Islands. The data was made available for this dissertation under the auspices of

an Oak Ridge Institute for Science and Education Research Fellowship Award.

http://orise.orau.gov/

52

Study Design


This section of the dissertation sought to characterize the correlates of rhBMP use during

LDDD-indicated lumbar fusion procedures (Research Question 1).

Study population

The analysis was limited to patients who were aged 21 and older at the time of their first

observed lumbar spinal fusion procedure (see Table B-1 for specific procedure codes). We

required that all patients included in the analysis have pharmacy benefits, and in the case of the

Medicare beneficiaries to have both hospital (Part A) and physician services (Part B) coverage,

to ensure comprehensive ascertainment of services used by the subject. In this and all

subsequent investigations in the dissertation we chose to restrict our analysis to Fee for Service

(FFS) patients. Unlike capitated arrangements, providers and institutions operating under a FFS

model only get paid for services billed. This financial incentive is believed to prompt a more

complete record of the medical care rendered. In order to fully ascertain procedure

characteristics such as the use of the osteobiologic specifically within the lumbar vertebrae, we

further excluded all patients who received a concurrent fusion procedure at another region of the

spine during the same institutional stay. Also excluded were procedures involving spinal

fractures, spinal cord injuries and congenital spinal abnormalities since these conditions are

likely to drive treatment decisions and outcome trajectories.

There are three main approaches to identifying the indication of a spinal fusion procedure

within administrative databases: 1) using the primary diagnosis listed on the claim, 2) using all

the diagnoses in the claim and 3) employing a hierarchical algorithm based on the demonstrated

efficacy of surgery in treating the listed diagnoses. Recent scholarship suggests that use of a

53

hierarchical algorithm was better at maximizing the sensitivity and specificity with which the

indication for the spinal surgery could be identified within administrative datasets (see Appendix

A for more details on the hierarchical algorithm and the associated diagnostic codes).42

For

completeness, we created three LDDD-indicated lumbar fusion procedure study cohorts based on

the three methods used to determine the indication for the surgery.

Exposure ascertainment

Our main exposure of interest was the use intraoperative rhBMPs during spinal fusion

procedures. The ICD-9-CM code 84.52 denotes “insertion of recombinant bone morphogenetic

protein”. At present there is no way to distinguish between rhBMP-7 and rhBMP-2 exposures in

an administrative claims data environment. Also unavailable is information on the precise

amounts of the osteobiologic introduced into patient during each procedure; consequently,

dosing considerations were not explored in this dissertation. Surgeries that utilized rhBMPs, as

denoted by the presence of ICD-9-CM code 84.52 in the fusion procedure claim, represented the

exposed group in this dissertation while the rest of the fusion operations served as potential

comparators.

Analytical approach

We used logistic regression models to assess whether rhBMP users differed from non-

users in terms of socio-demographic (age, sex, insurance coverage, geographic location),

procedure (surgical approach, fusion intent, number of levels fused, concurrent procedures

performed, use of instrumentation and alternative osteogenetic factors) and clinical

characteristics (presence of other spinal conditions on the procedure claim and the patient’s

comorbidity burden as measured using the Charlson-Elixhauser comorbidity index).

54


This section of the dissertation assessed whether the use of rhBMPs during lumbar fusion

procedures was related to a significantly decreased risk for revision fusion procedures (Research

Question 2a1) and evaluated whether the risk for these revision operations varied based on the

indication for the primary procedure (Research Question 2a2).

Study population

We created an inception cohort comprised of patients, aged 21 and older, who had

received a single level, primary lumbar fusion procedure between 2007 and 2010 (see Appendix

B for specific procedure codes). Unless stated otherwise, this and all subsequent analyses in this

dissertation utilized the hierarchical approach to determine the indication for the spinal fusion

procedure.42

The first part of this series of analyses (Research Question 2a1) analyzed patients

who received a lumbar fusion procedure for any of the major degenerative conditions of the

spine including LDDD, Herniated disc, Stenosis and Listhesis.

We excluded patients with less than six months of continuous enrollment in a FFS plan,

those who received a concurrent fusion procedure at another region of the spine and those

exposed to rhBMPs during the baseline ascertainment window. The observation of patients prior

to the exposure of interest is a common pharmacoepidemiology strategy that is used to determine

patient baseline characteristics and to distinguish between new and prevalent users of the

intervention under investigation.131

Also excluded were procedures involving spinal fractures,

spinal cord injuries, scoliosis and congenital spinal abnormalities.

Additionally, three nested cohorts were created to investigate if the association between

rhBMP use and revision fusion surgeries varied based on the indication of the primary fusion

procedure (Research Question 2a2). The three fusion procedure indications examined were

55

LDDD, Stenosis and Listhesis. Stenosis and Listhesis were selected to serve as comparators for

two reasons. Firstly, unlike the controversial use of fusion procedures in non-specific disc

degeneration cases, there is strong evidence supporting the efficacy of this surgical approach for

the treatment of Stenosis and Listhesis.42,132-134

These comparator populations were therefore

used to provide context to the rate of revisions observed following LDDD-indicated fusion

procedures. Secondly, the use of fusion procedures to treat both these conditions has been

widely studied thus allowing us to benchmark our findings against previously published

papers.16,84,91,135

Confounding adjustment

We employed propensity score techniques to mitigate the effects of selection bias and

measured confounding. First described by Paul Rosenbaum and Donald Rubin in 1983, a

propensity score is the probability that a subject receives the exposure of interest conditional on a

battery of pre-specified characteristics.136

Either through matching, stratification or simple

inclusion in the outcome model, propensity scores are used to balance the distribution of

observed characteristics across the different treatment assignment groups. The balancing step

attempts to mimic the RCT design whose validity is built on the comparability between the

exposed and unexposed groups. The more similar the intervention and control groups are, the

better the confounding adjustment.

We used the propensity score to match rhBMP-exposed cases to potential unexposed

comparators. Included in the propensity score estimation model were variables that have been

shown to predict rhBMP use during fusion procedures, or confounders that are known to

influence health care utilization patterns. These were age, sex, insurance coverage type, calendar

year of procedure, geographic location, surgical approach, use of instrumentation, number of

56

spinal levels operated on, the intent of the fusion procedure, the presence of other spinal

conditions on the claim and the patient’s Charlson- Elixhauser combined comorbidity score

(Equation 3-1).27,67,68,137

Logit [P(Y=1)] = α + β1X1 + β2X2 + β3X3 + β4X4 + β5X5 + β6X6 + β7X7 + β8X8 +…+β17X17

(3-1)

Where:

Y: Use of rhBMPs during index fusion procedure (where 1 implies rhBMP used)

ß1-17: Association parameter for the specific variable (X1 –X17)

X1: Age Group (categorized in 5 year intervals. For example: 21-25, 26-30)

X2: Sex (Male, Female)

X3: Type of Insurance used for procedure (Commercial, Medicaid, and Medicare)

X4: Geographical Region (Northeast, South, West, and Midwest)

X5: Calendar Year (2007-2010)

X6: Presence of spinal co-morbidities (Disc Herniation, Stenosis, Listhesis, Osteoporosis,

Inflammatory Spondylopathy)

X7: Subject’s Charlson-Elixhauser combined comorbidity score

X8: Concurrent decompression procedure during the index fusion event (yes, no)

X9: Concurrent laminectomy procedure during the index fusion event (yes, no)

X10: Surgical approach employed (Anterior, Posterior, and Circumferential)

X11: Number of levels fused during procedure (single, multiple)

X12: Fusion Intent (primary, revision)

X13: Used allograft bone substrate during procedure (yes, no)

X14: Used autograft bone substrate during procedure (yes, no)

X15: Used of anterior instrumentation during procedure (yes, no)

X16: Used of posterior instrumentation during procedure (yes, no)

X17: Used biomechanical cages during fusion procedure (yes, no)

We employed the greedy caliper matching algorithm to select controls for the patients

exposed to rhBMPs. In caliper matching, the allowable distance between the case’s and his

control’s propensity scores is pre-specified. The technique is used to limit bad matches that can

occur when the nearest comparable subject has a significantly different propensity score.138

57

While a stringent caliper criteria allows for better matched comparators, it does lower the

chances of a control being found within the source population.138

A caliper width set at 0.2 times

the standard deviation of the logit of the propensity score estimator has been shown to balance

the need for having close comparators without unduly restricting the number of viable controls

that can be selected.139

We used the Standardized Mean Difference (SMD) statistic to confirm that the

distribution of confounders in the rhBMP-exposed and unexposed groups was comparable.

Unlike the T-Test or the Wilcoxon signed rank test, the standardized mean difference is

independent of the sample size thus allowing for consistent application across different sized

samples.140

Standardized mean differences that fall within ±0.20 are, by convention, considered

small and were therefore used as markers of balance in this dissertation.141

The actual

computation was conducted using a SAS MACRO developed by Dongsheng Yang and Jarrod

Dalton.140

Analytical approaches

In the primary analysis (Research Question 2a1), we used Cox proportional hazard

regression to assess whether the use of rhBMPs was associated with lower hazards for refusion

procedures among patients who received a lumbar fusion procedure for any of the major

degenerative conditions of the spine. Patients were thus followed from the date of the index

procedure until either the date of a lumbar refusion procedure, the end of enrollment in a FFS

plan, the end of the study period (12/31/2010) or until death, which ever came first. The model,

as summarized in Equation 3-2, used the rhBMP exposure propensity score to adjust for

confounding.

hi = ho exp[β1X1 + β2X2]

58

(3-2)

Where:

)(ti : Subject i’s hazard of having a revision procedure at time t

)(0 t : Baseline hazard function at time t

β1-2: Association parameter for the specific variable (X1-2)

X1: rhBMP use status (yes, no)

X2: rhBMP Exposure Propensity Score

We performed two sensitivity analyses aimed at assessing the validity of key study

design features used in the primary analysis.

First, we examined the effect of competing risks on our primary calculation of refusion

procedure risk. In time to event analyses, competing risks are defined as events that can either

preclude or alter the probability of observing the outcome of interest.142

As illustrated through

the hypothetical patient 2 in Figure 3-1, patients can theoretically undergo a second primary

fusion procedure at any of the other lumbar spinal joints before the first refusion surgery is

observed. A second intervening primary fusion operation qualifies as a competing risk on two

fronts. Firstly, research has shown that fusion procedures are associated with adjacent segment

disease thus increasing the risk for additional surgeries at both the index and surrounding spinal

joints.143,144

Secondly, our ability to detect a revision of the index fusion procedure within an

administrative claim dataset is influenced by the presence of a second primary fusion operation

since the data structure does not indicate the specific spinal joint involved in the surgery. For

example, an index primary fusion procedure at the L2-L3 joint, followed by a second primary

fusion procedure at the L3-L4 joint and then by a refusion surgery at the L2-L3 joint would

appear identical in an administrative claims dataset as an index primary fusion procedure at the

L2-L3 joint, followed by a second primary fusion surgery at the L3-L4 joint and then by a

59

refusion procedure at the L3-L4 joint. Death, which by its very nature precludes the outcome of

interest from occurring, meets the definition of a competing risk and was thus included in our

analyses. Moreover, prior research has identified death as a significant competing risk for

orthopedic revision procedures especially in studies involving older patients.145

The most common approaches used to study competing risk models are Fine and Gray

Regression and Cause Specific Hazard Regression.146-148

Cause specific hazard regression

models the probability of the event of interest among those who have yet to experience any of

the possible competing outcomes.149

The model assumes that the censoring events are

independent and removes patients from the risk set for future events upon the occurrence of any

of possible outcomes.149

In contrast, the Fine and Gray regression uses a subdistribution

cumulative incidence function to model the probability of the outcome of interest at a given time

t among those who have yet to experience the event of interest and those who have previously

experienced a competing outcome.149

In other words, patients are only removed from the risk set

after experiencing the outcome of interest thus allowing us to directly model the effect of

competing events on our hazard of interest.149

The Cause-Specific Hazard Ratio (CHR) estimates the effect of the exposure on the

hazard of the event of interest while the Subdistribution Hazard Ratio (SHR) offers the effect of

the exposure on the “cumulative probability of the event” of interest.147

In cases where the

exposure has differing effects on the potential outcomes, these two estimates can result in

disparate conclusions.147

All primary conclusions of this dissertation were based on the CHR.

However as recommended by Latouché and colleagues, SHRs were also reported to provide “a

complete understanding of the event dynamics”.150

60

Secondly, we assessed the effect of the propensity score technique used on the calculated

refusion risk. More specifically, we used the cox proportional hazard regression to compare the

refusion risk obtained using the 2:1 propensity score matched cohort in the primary analysis with

results obtained through a radius matched population. In radius matching, all potential controls

that were within the specified caliper distance from an rhBMP-exposed case were included in the

analysis. As before, the caliper width is set at 0.2 times of the standard deviation of the logit of

the propensity score.139

Research question 2a2 investigated whether the association between rhBMP exposure and

the risk for refusion surgeries varied based on the indication of the primary lumbar fusion

procedure. In particular, we compared the link between rhBMP use and the incidence of refusion

procedures among LDDD-, Stenosis- and Listhesis-indicated lumbar fusion procedure cohorts.

Given the parallels between the primary and secondary analyses, the approaches used in this

investigation, including propensity score matching and cox proportional hazard regression were

identical to the methods used in the primary study.


This section of the dissertation assessed whether the use of rhBMPs during fusion

procedures was associated with lower risks for post-discharge hospitalizations (Research

Question 2b).

Study population

Due to data constraints outlined in Appendix A, the study was limited to Medicare

enrollees, aged 66 and older, who received a fusion procedure for a LDDD diagnosis (See

appendix B for specific procedure and diagnostic codes).42

We excluded patients with less than

6 months of continuous enrollment in a Medicare FFS plan prior to the index fusion procedure,

61

those with supplemental non-Medicare insurance during the observation window, those who

received a concurrent fusion surgery at another region of the spine, those exposed to rhBMPs

during the baseline ascertainment window, and patients whose procedure claims included

diagnostic codes for spinal fracture, spinal cord injury or congenital abnormalities. We further

excluded all patients who were discharged to or admitted into another inpatient facility on the

discharge (index) date since such stays are often rehabilitative in nature.151

Figure 3-2 gives a

schematic representation of the study design timeline.

We also created a nested cohort comprised of patients with at least 365 days of

observation before and after the fusion procedure in order to investigate the association between

rhBMP use and the number of readmissions during the first year of follow-up. By requiring a

year for observation of both baseline and outcome health utilization patterns we effectively

excluded all index lumbar fusion procedures that were conducted in 2007 or 2010, and the

patients who died or lost their insurance coverage within a year of their index operation.

As was the case in the analyses of the refusion procedure risk, effects of selection bias

and confounding were mitigated through propensity score matching (Equation 3-1). We

attempted to match each of the rhBMP-exposed cases with a control on the basis of the

propensity score. Although the use of a single control for each rhBMP-exposed case may be

criticized for its limited power, it has been argued that the 1:1 matching scheme provides the

most “credible inference with the least bias”.152

The use of a 1:1 matching scheme also

maximizes the number of cases matched which is a particular concern when dealing with

relatively small sample sizes. The caliper width was set at 0.2 times the standard deviation of the

logit of the propensity score with comparability between the exposed and unexposed groups

assessed using standardized mean differences.139,140

62

Outcome definitions

Readmissions into acute care facilities were identified using both the encounter type and

place of service variables provided in the MPCD data structure. We used three outcome

measures to characterize the use of inpatient services after the index fusion procedure: 1) the rate

of 30 day readmissions, 2) time to the first LDDD-related readmission and 3) the number of

LDDD-related hospitalizations within the first year following the procedure.

Previous studies indicate that early readmissions are mainly prompted by complications

of the procedure hence the inclusion of all-causes of hospitalizations into the analysis of 30 day

readmission rates.94,153

The second and third measures were designed to examine whether

rhBMP-augmented fusion procedures were more effective at ameliorating severe back pain

episodes than non-rhBMP operations. In this case, LDDD-related hospitalizations served as

proxies for exacerbation of back pain symptoms to the point of requiring inpatient treatment. For

a hospitalization to be considered LDDD-related, its claim had to include a diagnostic code for

LDDD (ICD-9-CM: 722.52, 722.6) or for non-specific back pain (ICD-9-CM: 724.2, 724.3,

724.4, 724.5, 724.8, 724.9). While all LDDD hospitalizations (ICD-9-CM: 722.52, 722.6) were

counted towards our outcome measure, encounters that only listed non-specific back pain (ICD-

9-CM: 724.2, 724.3, 724.4, 724.5, 724.8, 724.9), and involved an indicator for accidental

injuries, suicidal or homicidal events (ICD-9-CM: E80X, E81X, E82X, E84X, E88X, E89X,

E90X, E91X, E92X, E95X, E96X) were excluded.


The association between rhBMP use and readmission within 30 days of discharge was

assessed using a multivariate logistic regression. Using the unmatched general cohort, we

63

estimated the unadjusted, and the age and sex adjusted odds ratio of readmission within 30 days

for patients exposed to rhBMPs compared to the controls. The fully adjusted odds ratio for early

readmission was calculated within the propensity score matched cohort (Equation 3-3).

logit (P(Y) =1) = α + β1X1 + β2X2

(3-3)

Where:

Y: Patient’s 30-day readmission status (1= Readmitted, 0= Not Readmitted)




In order to assess whether the timing of the first LDDD-related readmission differs

between rhBMP users and non-users, patients were followed from the date of discharge until

their first readmission, their death, the end of their enrollment in a MPCD FFS plan or the end of

the study period (12/31/2010), whichever came first. Our primary interest was the first LDDD-

related readmission as described previously. Since both death and readmissions for non-LDDD

associated indications alter the probability of observing LDDD-related readmissions, these

censoring criteria were classified as competing risks and assessed using Cause-Specific Hazard

regression. Also reported was the Fine and Gray SHR, as recommended by Latouche and

colleagues.150

Lastly, we used the negative binomial regression model with firth correction to evaluate

the association between receipt of intraoperative rhBMPs and the number of LDDD- related

readmissions during the first year following the index fusion surgery. Unlike a Poisson

regression, the negative binomial distribution does not assume that the mean and the variance of

the distribution are equal thus allowing for over dispersion in the data. 154

The firth correction,

64

on the hand, imposes a penalty on the Maximum Likelihood Estimate to mitigate the bias

induced by small sample sizes.155

The effect of rhBMP use was calculated within the nested propensity score cohort and

was reported as an incidence rate ratio [IRR]. The model, as summarized in Equation 3-4,

adjusted for the propensity for rhBMP exposure and the patient’s use of inpatient acute care

services in the twelve months prior to the index fusion procedure. The use of inpatient services

at baseline was designed to serve as a proxy for the subject’s predisposition toward

hospitalizations. Since patients who are admitted in an acute care facility are precluded from

incurring further readmissions for the duration of their stay, we calculated each patient’s time at

risk and included it in the model as the offset variable.

ln (µ) = α + β1X1 + β2X2 + β3X3 + β4X4+ ln(t)

(3-4)

Where:

µ: Number of LDDD-related hospitalizations in the 12 months post-procedure




X3: Number of hospitalizations in the 12 months prior to index fusion procedure

X4: Number of days spent in an inpatient facility in the 12 months prior to the index fusion

procedure.

t: Number of days spent at risk for admission (365 - total number of days spent under

inpatient care during the 12 months post-discharge).

For completeness, we reexamined the association between rhBMP use and post-discharge

hospitalizations within two supplemental LDDD-indicated fusion procedure populations. These

cohorts were created using the primary diagnosis and comprehensive LDDD case definitions

described in appendix A. This sensitivity analysis retained all other features of the primary

investigation including the eligibility criteria and the statistical approaches for effect assessment.

65


This section of the dissertation assessed whether the use of rhBMPs during lumbar fusion

procedures was associated with fewer LDDD-related Emergency Room (ER) visits in the year

following the procedure (Research Question 2c).

Study population

Given the parallels between our analyses of readmissions and ER visit patterns, the same

study population, which is defined in part IIB, was used for both investigations.

Outcome definitions

ER encounters were identified using revenue codes 0450, 0451, 0452, 0459 and 0981.156

As was the case with our analysis of readmissions, LDDD-related ER visits had to 1) include a

diagnostic code for LDDD (ICD-9-CM: 722.52, 722.6) or for non-specific back pain (ICD-9-

CM: 724.2, 724.3, 724.4, 724.5, 724.8, 724.9), 2) not include a code for accidental injuries,

suicidal or homicidal events (ICD-9-CM: E80X, E81X, E82X, E84X, E88X, E89X, E90X,

E91X, E92X, E95X, E96X) and 3) meet our operational definition for ER encounters (Revenue

Codes: 0450, 0451, 0452, 0459 and 0981).


We used a negative binomial regression model with firth correction to evaluate the

association between receipt of intraoperative rhBMPs and the number of LDDD-related ER visits

during the first year following the procedure. Since patients who were admitted in an acute care

facility were precluded from incurring further ER visits for the duration of their stay, we

calculated each patient’s time at risk and included it in the model as the offset variable. The

model also accounted for the individual’s tendency to utilize ER services by including the

66

number of ER visits made by the patient during the baseline ascertainment period. The effect of

intraoperative rhBMP use on post-procedure ER visits is reported as an incidence rate ratio

(IRR).

ln (µ) = α + β1X1 + β2X2 + β3X3 + β4X4+ ln(t)

(3-5)

Where:

µ: Number of LDDD-related ER visits in the 12 months post-procedure




X3: Number of ER visits in the 12 months prior to index fusion procedure

X4: Number of days spent in an inpatient facility in the 12 months prior to the index fusion

procedure.

t: Number of days spent at risk for ER visits (365- total number of days spent under

inpatient care during the 12 months post-discharge).

We performed a series of sensitivity analyses aimed at checking the robustness of our

conclusions when using alternative case definitions for LDDD-indicated fusion procedures.

These sensitivity analyses were analogous in intent, form and execution to those described in the

section IIB above.


This section of the dissertation investigated whether the use of rhBMP-augmented fusion

procedures was associated with greater changes in the acquisition of opioid analgesics than non-

rhBMP-augmented fusion surgeries (Research Question 2d1

and Research Question 2d2).

Study population

We created an inception cohort comprised of patients, aged 21 and older, who had

received a lumbar fusion procedure primarily for an LDDD diagnosis between 2007 and 2009

67

(see Appendix A for the hierarchical indication algorithm and Appendix B for the specific

procedure and diagnostic codes). In order to fully characterize the association between the index

fusion procedure and opioid utilization patterns during the first year of follow up, we excluded

patients who died, lost their health coverage or underwent any inpatient surgical procedure

within 365 days of the procedure. Also excluded were patients with less than 6 months of

continuous enrollment in a FFS insurance plan prior to the index surgery, those who received a

concurrent fusion procedure at another region of the spine, those who were exposed to rhBMPs

during the baseline assessment period and those procedures involving spinal fractures, spinal

cord injury or congenital abnormalities. Given the unique considerations associated with cancer

pain management, we further excluded patients who had had any cancer-related health care

encounters during the baseline assessment window (see Appendix B for the specific cancer

diagnostic codes used).

The theoretical foundation of this analysis assumed that the changes in opioid refill

patterns stemmed from the effectiveness of the fusion procedure in ameliorating the patients’

pain. Consequently, we restricted the study to those who had filled at least one opioid analgesic

prescription in the three months prior to the index procedure.

We used logistic regression to model the probability of the rhBMP use conditional on

observed patient and procedure characteristics (Equation 3-1). Included in the propensity score

model were variables that are known to predict osteobiologic use and health care service

utilization patterns.27,77

Added to the model were indicators for chronic non-cancer pain

conditions that are commonly managed with opioid analgesics including Sickle Cell Disease,

Rheumatoid Arthritis, Neuropathic Pain, Fibromyalgia and Migraines.157

In order to further

mitigate the effects of selection bias, we included the patient’s baseline opioid access rate in the

68

propensity score estimation model. Comparability between the study arms was sought by

matching each of the rhBMP-exposed cases to an unexposed control on the basis of the

calculated propensity score. The caliper width for the propensity score matching process was set

at 0.2 times the standard deviation of the logit of the propensity score to prevent poor

matches.139,140

Characterization of opioid use

A schematic summary the study design appears in Figure 3-3. We used the three months

prior to the index fusion procedure to establish the patient’s baseline opioid acquisition rate and

two 3-month windows starting at the 3-month and 9-month marks to assess post-procedure

opioid access patterns. These post-procedure assessment windows were used to determine the

association between osteobiologic use and opioid access rates both in the short term and the

moderately longer term.

Two measures were used to characterize the effect of rhBMP use on opioid analgesic

access patterns. The first was a binary variable, (φ), which was created to differentiate opioid

analgesic users from non-users. The second measure was designed to identify any dose changes

undertaken by the patient between the different assessment windows. To facilitate comparisons

between the disparate opioid analgesics products available, all prescriptions were first converted

into oral morphine-equivalent units (OMEUs) (See Table B-7 for the Morphine Equivalent

Conversion Rates). We then calculated the average daily OMEUs associated with the

prescription by dividing the total morphine units by the number of the days supplied as listed on

the prescription claim. This computation assumed that the analgesic was accessed consistently

throughout the prescription’s coverage dates. For patients on multiple concurrent opioid

prescriptions, the daily OMEUs accessed was calculated by summing the morphine units

69

provided by each of the prescriptions during the overlapping coverage dates. Intermittent opioid

prescriptions designed to address acute pain episodes, overlapping prescriptions or delays in

acquiring refills can theoretically lead to day to day variations in the amount of OMEUs

available to the patient. To account for these fluctuations, a patient’s opioid dose during each

ascertainment window was defined as the mode of the daily morphine units accessed over the

three month period. Our outcome of interest was the changes in the quantities of opioids

accessed daily which was calculated by subtracting the estimated daily OMEUs accessed during

the outcome assessment windows from baseline acquisition rate.

In order to facilitate the description of patients based on their opioid acquisition patterns

at baseline, we categorized opioid access rates as follows: low (< 30mg OMEUs), medium (31-

60mg OMEUs), high (61-120mg OMEUs), very high (>120mg OMEUs).158


Descriptive statistics were used to outline the baseline characteristics of each of the

opioid acquisition rate subgroups. Of particular interest was the association between the amounts

of opioids accessed at baseline and the use of rhBMPs during the index procedure.

In the propensity score matched cohort, logistic regression was used to assess the

association between rhBMP use at baseline and the discontinuation of opioid therapy following

the procedure (Equation 3-6).

log((P(φk) / (1- P(φk)) = α + β1X1 + β2X2

(3-6)

Where

φ : Binary Indicator of Opioid Use during the Post-Procedure Assessment Window k


70



k: Post-Procedure Assessment Window (1:3-6 month window, 2: 9-12 month window)

Lastly, we used an Analysis of Covariance (ANCOVA) model to determine whether the

use of rhBMPs was associated with significantly greater changes in the access of opioid

analgesics than non-use of the osteobiologic. Equation 3-7 summarizes the model as applied to

our evaluation.

Yk = α + β1X1 + β2X2

(3-7)

Where

Y : Difference in the amounts of Opioid Analgesics accessed daily (in OMEUs) between the

post-procedure assessment window k and the baseline assessment window.




k: Post Fusion Assessment Window (1:3-6 month window, 2: 9-12 month window)

The ANCOVA model combines the analysis of variance estimation on a categorical

predictor variable with regression on a continuous covariate. The interpretation of this

generalized linear model is built on two central assumptions, namely, the independence of the

predictors and the homogeneity of regression plots. The independence of predictors’ assumption

asserts that the effect of the continuous covariate is statistically equivalent across all levels of the

categorical predictor variable. In our case, we used the T-Test to confirm that the effect of the

propensity score was independent of the patient’s rhBMP exposure status. The homogeneity of

regression slopes assumption was tested by including an interaction term between the categorical

(rhBMP exposure status) and the continuous predictor (propensity score) in the regression. A

71

non-significant interaction term was used as evidence that the regression slopes were statistically

comparable.

Part III: Safety Analysis of Recombinant Human Bone Morphogenetic Proteins

Part III of this dissertation investigated the association between intraoperative rhBMP use

and the rates of post-procedure cancer diagnosis (Research Question 3a) and whether the effect

of the osteobiologic on cancer risk varied based on the location of the fusion procedure

(Research Question 3b).

Study population

We created an inception cohort comprised of patients, aged 21 and older, who received a

fusion procedure while enrolled an MPCD Fee for Service insurance plan (see Appendix B for

specific procedure codes). To distinguish between prevalent and emergent cancer cases, we

excluded patients who received any cancer-related medical services in the six months prior to the

index procedure (Table B-6 provides the specific diagnostic codes used to identify cancer-related

health care encounters). Also excluded were patients with less than six months continuous

enrollment in a FFS plan prior to the index surgery and those who received concurrent fusion

procedures in multiple regions of the spine.

A six month look back window has been shown to allow sufficient time to differentiate

between new onset cancer diagnoses from prevalent cases.159

In an analysis of six different

cancer types, a study by Setoguchi et al found only marginal improvements in the sensitivity

(3%) and specificity (<1%) with which researchers were able to identify incident cancer cases

within administrative claims when the look back window was moved from six months to 2

years.159

Any cancer-related health care charge dated in the six months before the index fusion

72

event was therefore considered an indicator of pre-existing cancer thereby excluding the subject

from this study.

As with previous analyses in this dissertation, we employed propensity score techniques

to adjust for confounding. Included in the propensity score estimation model were variables that

have been shown to predict the use of rhBMPs during fusion procedures and confounders that

were likely to influence cancer detection (Equation 3-1). In order to determine whether the

association between rhBMP use and post-procedure cancer risk varies depending on the region

of the spine treated, the propensity score estimation process and all subsequent analyses were

stratified by the location of the fusion procedure (cervical, thoracolumbar). We then attempted

to match each patient exposed to rhBMPs with up to two unexposed controls using the greedy

matching approach. To maximize comparability between the cases and comparators, we

required that all selected controls be within 0.2 times of the standard deviation of the logit of the

case’s propensity score.139

Outcome ascertainment

The term cancer was used in the study to represent both malignant neoplasms and benign

tumors. The primary case definition, which required a single cancer-related health service

encounter during follow up (see Table B-6 for specific diagnostic codes), was designed to

maximize the sensitivity with which we could detect cancer cases in this data environment.


Patients were followed from the date of the index fusion procedure until they met our

outcome case definition, underwent a subsequent rhBMP-augmented procedure, lost their

healthcare coverage, died or until the end of the study period (December 31, 2010), whichever

came first. We used logistic regression to assess the association between receiving rhBMPs and

73

key clinical, procedure and demographic characteristics, reporting specifically on the differences

between the cervical and thoracolumbar fusion procedure populations.

We calculated the incidence of new cancers per 1000 person years of follow-up and

provided a tabulated summary of the cancer types diagnosed during outcome ascertainment

window. To quantify the association between the osteobiologic and the risk for cancer, we

calculated the unadjusted, age-sex adjusted, and the fully adjusted CHR for cancer among the

rhBMP-exposed compared to the controls. The unadjusted and age-sex adjusted estimates were

computed among all the patients who met our eligibility criteria while the adjusted estimates was

derived from the propensity score matched cohorts of cervical and thoracolumbar fusion

procedure patients (Equation 3-8).

)(ti = )(0 t exp [β1X1 + β2X2]

(3-8)

Where:

)(ti : Subject i’s hazard of receiving a cancer diagnosis at time t





Both the hazard ratios calculated from the cox regression and the SHR estimated using

the Fine and Gray regression models were also provided to gauge the robustness of the study’s

main analytical model.150

We performed a series of sensitivity analyses that assessed the validity of the key study

design features used in the main investigation. Firstly, we tested the assumption that patients

with preexisting cancer would have had at least one cancer-related health care encounter in the

74

six months prior to the fusion procedure by extending the look back window to 12 months and

then to 18 months. As with the original study population, we excluded all patients who received

any cancer-related medical services during the pre-procedure look-back window while retaining

all the other study design and analytical elements of the main analysis.

The primary analyses also assumed that the cancer risk presented by the osteobiologic

was immediately detectable and persistent throughout the observation window. 160

It is a

common assumption that has been employed, albeit implicitly, in prior studies that investigated

this question.21,77

Building on previously published studies on drug-induced cancer risks, we

tested this assumption by imposing a 180 day induction period thus effectively excluding the first

6 months post-surgery from our follow-up time.160,161

Additionally, we examined the effect of using a more stringent cancer case definition on

the calculated association between rhBMP use and the risk for post-procedure cancer diagnosis.

This secondary case definition, which was aimed at maximizing the specificity of the outcome

determination process, required the patient to have two or more cancer diagnostic codes incurred

on at least two different service dates over a two month window. The validity of using two

healthcare encounters in disease case ascertainment has been demonstrated in several previous

publications.159,160,162-164

The rhBMP-related cancer diagnosis risk under each of these new study design features

was calculated using the hazard regression model summarized in Equation 3-8 and then

compared to the results from the original model.

75

Tables and Figures

Figure 3-1. Schematic illustrating the relationship between fusion and refusion events in

administrative claims data

Figure 3-2. Timeline of fusion event in relation to the index date, baseline assessment window

and follow-up time

76

Figure 3-3. Drug use assessment windows and their relation to the fusion event

77

CHAPTER 4

RESULTS


This section of the dissertation sought to characterize the correlates of rhBMP use during

LDDD-indicated lumbar fusion procedures (Research Question 1).

Study population description

We identified 23,060 patients, aged 21 and older, who had received a lumbar fusion

procedure for an LDDD-related indication as defined by the presence of an LDDD diagnostic

code on the procedure claim. We excluded the 207 patients who received a concurrent fusion

procedure at another region of the spine during the same institutional stay and the 1,368

procedures whose claims included diagnostic codes for spinal fractures, injury to the spinal cord

or congenital abnormalities. Out of the remaining 21,714 cases (Comprehensive Definition

Cohort), 5,646 LDDD-indicated lumbar fusion procedure events were assigned to a cohort based

on hierarchical indication algorithm (Hierarchical Definition Cohort), and 12,833 procedures to a

third LDDD study population based on the primary diagnosis on the claim (Primary Diagnosis

Definition Cohort). Figure 4-1 shows the relative sizes of the study cohorts analyzed.

Majority of the patients who received LDDD-indicated spinal fusion procedures,

irrespective of the method used to identify the indication of the surgery, were female, white, over

65 years old, insured by Medicare and sourced from the Southern region of the United States (all

OR > 1, all p values < 0.001). The estimated rate of rhBMP-use (17%) among LDDD-indicated

procedures was consistent across the three populations analyzed. Descriptive statistics on the key

patient and procedure characteristics as they relate to intraoperative rhBMP use appear in Tables

4-1, 4-2 and 4-3.

78

Analytical results

Older age and female sex were both consistently associated with rhBMP use (all OR (95% CI) >

1, all p values <0.05). Post hoc analysis found no evidence to suggest that the sex-related

differences in rhBMP use were attributable to differences in age distribution (all p values <

0.05). Additionally, White patients were significantly more likely to receive rhBMPs than those

with in the Other, Missing and Indecipherable racial categories (all OR (95% CI) < 1). Although

Black patients were also less likely to use the osteobiologic, the difference between Blacks and

Whites was not statistically significant (Hierarchical Definition Cohort OR (95% CI): 0.94

(0.71, 1.24), Primary Diagnosis Definition Cohort OR (95% CI): 0.88 (0.77, 1.02),

Comprehensive Definition Cohort OR (95%CI): 0.99 (0.82, 1.19)).

Predictably, given the large proportion of patients over 65 years old, majority of the

patients analyzed were covered by Medicare. The publicly insured were more likely to use

rhBMPs than those covered by commercial plans (all OR (95% CI) > 1). The association

between the type of insurance coverage and rhBMP use was not completely a function of the

age. In the cohorts analyzed, Medicare beneficiaries in the 61 to 65 age category were

approximately 14 times more likely to receive rhBMPs during fusion procedures than the

similarly aged commercially insured patients (Hierarchical Definition Cohort OR (95% CI):

14.0 (6.3, 31.5); Primary Diagnosis Definition Cohort OR (95% CI): 13.3 (7.8, 22.6);

Comprehensive Definition Cohort OR (95% CI): 10.8 (7.3, 16.0)).

The effect of geography on rhBMP use varied based on the definition used to identify the

indication for the procedure. Analysis of the comprehensive case definition cohort which

included all procedures that listed LDDD as a contributing diagnosis found that surgeries

performed in the Northeastern and Southern States were significantly less likely to involve the

79

osteobiologic than fusion procedures billed in the Midwestern States (p value = 0.02). In

contrast, we found no statistical association between geographic location and rhBMP use among

patients who had LDDD listed as the primary diagnosis (p value = 0.23) or among those assigned

to the LDDD fusion procedure population using the hierarchical algorithm (p value = 0.19)

Revision fusion procedures were more likely to employ rhBMPs than primary fusion

operations; however, the association was not statistically significant in any of the study cohorts

analyzed (Hierarchical Definition Cohort OR (95% CI): 1.33 (0.95, 1.85), p value = 0.10;

Primary Diagnosis Definition Cohort OR (95% CI): 1.26 (0.99, 1.62), p value = 0.06;

Comprehensive Definition Cohort OR (95% CI): 1.19 (0.99, 1.44), p value = 0.07).

Similarly, the observed association between the choice of surgical approach and the use

of intraoperative rhBMPs was consistent across the three cohorts analyzed. Posteriorly

approached lumbar fusion procedures were just as likely to involve the osteobiologic as

anteriorly approached surgeries (Hierarchical Definition Cohort OR (95% CI): 0.95 (0.81, 1.12);

Primary Diagnosis Definition Cohort OR (95% CI): 1.02 (0.90, 1.15); Comprehensive Definition

Cohort OR (95% CI): 1.00 (0.91, 1.11)). On the other hand, circumferential fusion procedures

were significantly less likely to utilize rhBMPs than anteriorly approached procedures

(Hierarchical Definition Cohort OR (95% CI): 0.63 (0.48, 0.83); Primary Diagnosis Definition

Cohort OR (95% CI): 0.63 (0.53, 0.75); Comprehensive Definition Cohort OR (95% CI): 0.65

(0.56, 0.75)).

While the calculated association between rhBMP exposure and number of spinal levels

operated on was not statistically significant in either the hierarchical (OR (95% CI): 1.01 (0.88,

1.16), p value = 0.88) or the primary diagnosis definition cohorts (OR (95% CI): 1.04 (0.95,

1.14), p value = 0.44), we observed that multi-level procedures were linked to higher odds of

80

rhBMP use within the comprehensive case definition cohort (OR (95% CI): 1.1 (1.03, 1.19), p

value < 0.01).

Patients with higher levels of comorbidity as measured by the Charlson-Elixhauser

Comorbidity Index were significantly more likely to have received rhBMPs during fusion

procedures than patients on the lower end of the comorbidity index scale, irrespective of the

method used to identify the indication of the surgery. We estimated that the odds of receiving

intraoperative rhBMPs increased by 22% with each additional point on the comorbidity index

scale (Hierarchical Definition Cohort OR (95% CI): 1.22 (1.08, 1.17); Primary Diagnosis

Definition Cohort OR (95% CI): 1.22 (1.09, 1.15), although the effect was attenuated in the

comprehensive definition cohort (OR (95% CI): 1.11 (1.08, 1.13)).

By design, fusion procedures included in the hierarchical definition cohort did not in

include diagnostic codes for any of the other major degenerative conditions of the spine such as

Herniated Discs, Stenosis, Listhesis and Scoliosis. However, analyses of the primary diagnosis

and the comprehensive definition study cohorts revealed that the type of diagnostic codes listed

in fusion procedure claim was associated with the use of the rhBMPs during the operation.

Specifically, the presence of diagnostic codes for Stenosis, Listhesis or Scoliosis was associated

with significantly higher odds of rhBMP use during the surgery (all OR > 1, all p values <0.05),

while the presence of disc herniation or other non-specific back pain conditions codes was

related to lower odds of receiving the osteobiologic during the procedure (all OR < 1, all p values

<0.05).

Within the hierarchical definition population, exposure to rhBMPs was not significantly

associated with either the use of autograft (OR (95% CI): 1.06 (0.91, 1.24), p value = 0.43) or

allograft bone substrates (OR (95% CI): 1.03 (0.88, 1.21), p value = 0.72) during the fusion

81

procedure. However, the examination of primary diagnosis definition cohort and the

comprehensive definition cohort revealed that procedures that included rhBMPs were

significantly more likely to utilize both autograft and allograft bone material (both OR > 1, both

p value <0.001).

The association between the use of instrumentation and rhBMP exposure also varied

between the three populations. Unlike the use of biomechanical cages which was consistently

associated with rhBMP exposure in all three cohorts analyzed (all OR > 1, all p values <0.001),

the use of anterior and posterior instrumentation was significantly associated with osteobiologic

use when evaluated within the primary diagnosis definition cohort and the comprehensive cohort

(both OR > 1, both p values < 0.05) but not in the hierarchical definition population (p values >

0.1).

We found no evidence of association between the receipt of a concurrent laminectomy

procedure and rhBMP exposure in any of the cohorts analyzed (all p values > 0.3). Having a

concurrent discectomy procedure, on the other hand, was linked to lower odds of using rhBMPs

during the fusion procedure (Hierarchical Definition Cohort OR (95% CI): 0.94 (0.82, 1.09);

Primary Diagnosis Definition Cohort OR (95% CI): 0.83 (0.76, 0.91); Comprehensive Definition

Cohort OR (95% CI): 0.87 (0.89, 0.93)). However in the case of the hierarchical definition

cohort, the association between having a concurrent discectomy and receiving rhBMPs during

the fusion procedure was not statistically significant (p value = 0.40).

Summary

Overall, we observed that the rates of rhBMP use differed based on patient and procedure

characteristics. Female patients, those who were older, on Medicare, living in the Western

states, or those who had higher levels of comorbidity were significantly more likely to receive

82

the osteobiologic than their comparators who did not share these characteristics. In total, seven of

the 18 potential correlates tested (namely, geographical region, number of levels operated on,

anterior, posterior instrumentation, and concurrent discectomy) produced different conclusions

depending on the cohort used to test the association. To clarify, it is not that the direction of point

estimates differed across the three cohorts analyzed but rather that the some associations, when

calculated in the hierarchical definition population or the primary diagnosis population, did not

reach the pre-specified level of statistical significance (α = 0.05). Since p values are intrinsically

linked to sample sizes, these disparities in conclusions are arguably the result of differences in

the population sizes across the three cohorts. The notable exception to this observation is the

relationship between geographical regions and rhBMP use. Case in point: an analysis of

procedures that listed LDDD as a contributing diagnosis (Comprehensive Definition Cohort)

suggests that patients in the South are less likely to utilize the osteobiologic than those residing

in the Midwestern region. Conversely, an analysis of LDDD procedures performed in the

absence of other major degenerative conditions of the spine (Hierarchical Definition Cohort)

indicates that fusion surgeries performed in the South are more likely to utilize rhBMPs than

those in the Midwest Region. Overall, our analysis suggests that the identified correlates of

rhBMP use during LDDD-indicated lumbar fusion procedures are fairly robust irrespective of the

method used to ascertain the indication for which the surgery was performed.

83

Tables and figures

Figure 4-1. Correlates of rhBMP use study population creation flowchart

MPCD FFS plan enrollees, aged ≤ 21 years, with

LDDD diagnostic code on index lumbar fusion

procedure claim

(N= 23,060)

N=

LDDD Fusion Cohort

(Comprehensive Definition)

(N= 21,714)

Exclusion Criteria

Concurrent fusion in another region

of the spine (N=509)

Spinal Fracture /Dislocation (N=371)

Spinal cord injury (N=15)

Congenital Abnormality (N=972)

LDDD Fusion Subpopulation

(Hierarchical Algorithm)

(N=5,646)

N=

LDDD Fusion Cohort

(Primary Diagnosis)

(N=12,833)

84

Table 4-1. Characteristics of LDDD-indicated fusion procedure population (hierarchical

definition cohort)

Characteristic, n (%)

BMP Use

No

(n = 4 707,

83.4%)

Yes

(n = 939,

16.6%)

OR (95% CI) P value

Age

21 - 45 years 1487 (31.6) 176 (18.7) 0.77 (0.64, 0.93) <0.001*

46 - 65 years 2239 (47.6) 344 (36.6) Reference

Over 65 years 981 (20.8) 419 (44.6) 2.78 (2.37, 3.27)

Female Sex 2696 (57.3) 572 (60.9) 1.16 (1.01, 1.34) 0.039*

Race

White 2988 (63.5) 789 (84.0) Reference <0.001*

Black 270 (5.7) 67 (7.1) 0.94 (0.71, 1.24)

Other 183 (3.9) 21 (2.2) 0.43 (0.27, 0.69)

Missing 119 (2.5) 15 (1.6) 0.48 (0.28, 0.82)

Indecipherable 1147 (24.4) 47 (5.0) 0.16 (0.11, 0.21)

Geographical Region

Midwest 1378 (29.3) 253 (26.9) Reference 0.229

Northeast 335 (7.1) 76 (8.1) 1.24 (0.93, 1.64)

South 2242 (47.6) 437 (46.5) 1.06 (0.90, 1.26)

West 741 (15.7) 170 (18.1) 1.25 (1.01, 1.55)

Other 11 (0.2) 3 (0.3) 1.49 (0.41, 5.36)

Insurance

Medicaid 138 (2.9) 45 (4.8) 10.0 (6.71, 15.0) <0.001*

Medicare 1401 (29.8) 687 (73.2) 15.1 (11.9, 19.1)

Commercial 2581 (54.8) 84 (8.9) Reference

Medicare + Medicaid 75 (1.6) 40 (4.3) 16.4 (10.5, 25.5)

Commercial + Medicaid 3 (0.1) 0 (0.0) 0.00 (0.00, 0.00)

Commercial + Medicare 506 (10.7) 82 (8.7) 4.98 (3.62, 6.85)

All of the Above 3 (0.1) 1 (0.1) 10.2 (1.05, 99.5)

Surgical Approach

Anterior 1293 (27.5) 278 (29.6) Reference 0.003*

Posterior 2853 (60.6) 585 (62.3) 0.95 (0.81, 1.12)

Circumferential 561 (11.9) 76 (8.1) 0.63 (0.48, 0.83)

85

Table 4-1. Continued


BMP Use

No Yes OR (95% CI) P value

Multiple Level Procedure 2053 (43.6) 412 (43.9) 1.01 (0.88, 1.16) 0.883

Revision Procedure 176 (3.7) 46 (4.9) 1.33 (0.95, 1.85) 0.096

Concurrent Procedures

Discectomy 2816 (59.8) 548 (58.4) 0.94 (0.82, 1.09) 0.403

Laminectomy 1472 (31.3) 300 (31.9) 1.03 (0.89, 1.20) 0.682

Instrumentation Used

Anterior 983 (20.9) 174 (18.5) 0.86 (0.72, 1.03) 0.103

Posterior 1401 (29.8) 293 (31.2) 1.07 (0.92, 1.25) 0.38

Non-segmental 1456 (30.9) 281 (29.9) 0.95 (0.82, 1.11) 0.541

Biomechanical Cage 3456 (73.4) 743 (79.1) 1.37 (1.16, 1.63) <0.001*

Osteogenetic Factors Used

Allograft Bone Substrate 1242 (26.4) 253 (26.9) 1.03 (0.88, 1.21) 0.723

Autograft Bone Substrate 1324 (28.1) 276 (29.4) 1.06 (0.91, 1.24) 0.432

Year of Procedure

2007 1146 (24.3) 282 (30.0) Reference

<0.001*

2008 1243 (26.4) 249 (26.5) 0.81 (0.67, 0.98)

2009 1239 (26.3) 227 (24.2) 0.74 (0.61, 0.90)

2010 1079 (22.9) 181 (19.3) 0.68 (0.56, 0.84)

Charlson-Elixhauser

Comorbidity Index, Mean(SD),

Median

0.52 (1.5), 0 0.85 (1.9), 0 1.22 (1.08, 1.17) <0.001*

Other Spinal Conditions on Claim

Non-Specific Back Pain 1151 (24.5) 198 (21.1) 0.83 (0.70, 0.98) 0.027*

Spondylopathy 7 (0.1) 0 (0.0) 0.00 (0.00, 0.00) 0.962

Osteoporosis 7 (0.1) 3 (0.3) 2.15 (0.56, 8.34) 0.267

*: p value less than 0.05

86

Table 4-2. Characteristics of LDDD-indicated fusion procedure population (primary

diagnosis definition cohort)


BMP Use

No

(n=10 661,

83.1%)

Yes

(n= 2 172,

16.9%)

OR (95% CI) p value

Age

21 - 45 years 2666 (25.0) 294 (13.5) 0.78 (0.67, 0.90) <0.001*

46 - 65 years 4733 (44.4) 672 (30.9) Reference

Over 65 years 3262 (30.6) 1206 (55.5) 2.60 (2.35, 2.89)

Female Sex 6130 (57.5) 1322 (60.9) 1.15 (1.05, 1.26) 0.004*

Race

White 6888 (64.6) 1852 (85.3) Reference <0.001*

Black 591 (5.5) 157 (7.2) 0.99 (0.82, 1.19)

Other 420 (3.9) 51 (2.3) 0.45 (0.34, 0.61)

Missing 296 (2.8) 24 (1.1) 0.30 (0.20, 0.46)

Indecipherable 2466 (23.1) 88 (4.1) 0.13 (0.11, 0.17)

Geographical Region

Midwest 2982 (28.0) 575 (26.5) Reference 0.118

Northeast 802 (7.5) 158 (7.3) 1.02 (0.84, 1.24)

South 4980 (46.7) 1001 (46.1) 1.04 (0.93, 1.17)

West 1869 (17.5) 432 (19.9) 1.20 (1.04, 1.38)

Other 28 (0.3) 6 (0.3) 1.11 (0.46, 2.70)

Insurance

Medicaid 191 (1.8) 65 (3.0) 12.4 (8.96, 17.2) <0.001*

Medicare 3859 (36.2) 1710 (78.7) 16.2 (13.6, 19.3)





All of the Above 4 (0.0) 3 (0.1) 27.4 (6.07, 124)

Surgical Approach

Anterior 1984 (18.6) 425 (19.6) Reference <0.001*

Posterior 6947 (65.2) 1514 (69.7) 1.02 (0.90, 1.15)


87



BMP Use

No Yes OR (95% CI) p value


Discectomy 6372 (59.8) 1200 (55.2) 0.83 (0.76, 0.91) <0.001*

Laminectomy 4702 (44.1) 976 (44.9) 1.03 (0.94, 1.13) 0.477

Multiple Level Procedure 5302 (49.7) 1100 (50.6) 1.04 (0.95, 1.14) 0.439



Anterior 1898 (17.8) 309 (14.2) 0.77 (0.67, 0.87) <0.001*

Posterior 3817 (35.8) 832 (38.3) 1.11 (1.01, 1.22) 0.027*

Non-segmental 3393 (31.8) 705 (32.5) 1.03 (0.93, 1.14) 0.563

Biomechanical Cage 7559 (70.9) 1658 (76.3) 1.32 (1.19, 1.47) 0.001*


Allograft Bone Substrate 2579 (24.2) 592 (27.3) 1.17 (1.06, 1.30) 0.003*

Autograft Bone Substrate 3256 (30.5) 731 (33.7) 1.15 (1.05, 1.27) 0.004*

Year of Procedure

2007 2287 (21.5) 558 (25.7) Reference <0.001*

2008 2696 (25.3) 568 (26.2) 0.86 (0.76, 0.98)

2009 2980 (28.0) 528 (24.3) 0.73 (0.64, 0.83)

2010 2698 (25.3) 518 (23.8) 0.79 (0.69, 0.90)

Charlson-Elixhauser

Comorbidity Index, Mean(SD) 0.58 (1.57) 0.92 (1.87) 1.22 (1.09, 1.15) <0.001**


Non-Specific Back Pain 1991 (18.7) 360 (16.6) 0.87 (0.76, 0.98) 0.021*

Herniated Disc 2074 (19.5) 325 (15.0) 0.73 (0.64, 0.83) <0.001*

Stenosis 4351 (40.8) 971 (44.7) 1.17 (1.07, 1.29) <0.001*

Listhesis 1721 (16.1) 390 (18.0) 1.14 (1.01, 1.28) 0.038*

Scoliosis 365 (3.4) 102 (4.7) 1.39 (1.11, 1.74) 0.004*

Spondylopathy 10 (0.1) 3 (0.1) 1.47 (0.41, 5.36) 0.556

Osteoporosis 29 (0.3) 8 (0.4) 1.36 (0.62, 2.97) 0.447


88

Table 4-3. Characteristics of LDDD-indicated fusion procedure population (comprehensive

definition cohort)


BMP Use

No

(n=17986,

82.9%)

Yes

(n= 3728,

17.1%)

OR (95% CI) p value

Age

21 - 45 years 3719 (20.7) 415 (11.1) 0.83 (0.74, 0.94) <0.001*

46 - 65 years 7743 (43.1) 1041 (27.9) Reference

Over 65 years 6524 (36.3) 2272 (60.9) 2.59 (2.39, 2.81)

Female Sex 10398 (57.8) 2262 (60.7) 1.13 (1.05, 1.21) 0.001*

Race

White 11675 (64.9) 3193 (85.6) Reference <0.001*

Black 1017 (5.7) 246 (6.6) 0.88 (0.77, 1.02)

Other 659 (3.7) 98 (2.6) 0.54 (0.44, 0.67)

Missing 492 (2.7) 44 (1.2) 0.33 (0.24, 0.45)

Indecipherable 4143 (23.0) 147 (3.9) 0.13 (0.11, 0.15)

Geographical Region

Midwest 4772 (26.5) 1032 (27.7) Reference 0.016*

Northeast 1460 (8.1) 282 (7.6) 0.89 (0.77, 1.03)

South 8528 (47.4) 1679 (45.0) 0.91 (0.84, 0.99)

West 3181 (17.7) 727 (19.5) 1.06 (0.95, 1.17)

Other 45 (0.3) 8 (0.2) 0.82 (0.39, 1.75)

Insurance

Medicaid 240 (1.3) 85 (2.3) 12.7 (9.60, 16.8) <0.001*

Medicare 7347 (40.8) 3005 (80.6) 14.7 (12.8, 16.9)





All of the Above 11 (0.1) 4 (0.1) 13.0 (4.12, 41.3)

Surgical Approach

Anterior 2395 (13.3) 519 (13.9) Reference <0.001*

Posterior 13237 (73.6) 2879 (77.2) 1.00 (0.91, 1.11)


89


Characteristic, n (%) BMP Use

No Yes OR (95% CI) p value


Discectomy 10636 (59.1) 2078 (55.7) 0.87 (0.81, 0.93) <0.001*

Laminectomy 9058 (50.4) 1839 (49.3) 0.96 (0.89, 1.03) 0.251

Multiple Level Procedure 9423 (52.4) 2050 (55.0) 1.11 (1.03, 1.19) 0.004*



Anterior 2311 (12.8) 379 (10.2) 0.77 (0.68, 0.86) <0.001*

Posterior 6894 (38.3) 1583 (42.5) 1.19 (1.11, 1.28) <0.001*

Non-segmental 5894 (32.8) 1203 (32.3) 0.98 (0.91, 1.05) 0.554

Biomechanical Cage 11958 (66.5) 2740 (73.5) 1.40 (1.29, 1.51) <0.001*


Allograft Bone Substrate 4034 (22.4) 1000 (26.8) 1.27 (1.17, 1.37) <0.001*

Autograft Bone Substrate 5824 (32.4) 1349 (36.2) 1.18 (1.10, 1.27) <0.001*

Year of Procedure

2007 3691 (20.5) 864 (23.2) Reference <0.001*

2008 4391 (24.4) 966 (25.9) 0.94 (0.85, 1.04)

2009 5091 (28.3) 934 (25.1) 0.78 (0.71, 0.87)

2010 4813 (26.8) 964 (25.9) 0.86 (0.77, 0.95)

Charlson-Elixhauser

Comorbidity Index,

Mean (SD) 0.63 (1.62) 0.95 (1.88) 1.11 (1.08, 1.13) <0.001*

Spinal Conditions

Non-Specific Back Pain 3801 (21.1) 698 (18.7) 0.86 (0.79, 0.94) <0.001*

Herniated Disc 4394 (24.4) 707 (19.0) 0.72 (0.66, 0.79) <0.001*

Stenosis 9473 (52.7) 2140 (57.4) 1.21 (1.13, 1.30) <0.001*

Listhesis 4186 (23.3) 961 (25.8) 1.14 (1.06, 1.24) 0.001*

Scoliosis 850 (4.7) 248 (6.7) 1.44 (1.24, 1.66) <0.001*

Spondylopathy 19 (0.1) 4 (0.1) 1.02 (0.35, 2.99) 0.977

Osteoporosis 41 (0.2) 17 (0.5) 2.01 (1.14, 3.53) 0.016*


90


This section of the dissertation evaluated the association between intraoperative rhBMP

use and the risk for refusion procedures (Research Question 2a1 and 2a

2).


For the primary analysis (Research Question 2a1), we identified 31,912 patients, aged 21

years and older, who had received a single level primary lumbar fusion procedure for a

degenerative condition of the spine (Figure 4-2). We excluded 6,480 patients who had less than

six months of continuous enrollment in a MPCD-participating FFS Plan, 150 patients who

received a concurrent fusion procedure at another region of the spine, 7 patients who had been

exposed to rhBMPs during the baseline ascertainment window and 2,308 patients who had either

a spinal fracture, spinal cord injury or congenital abnormality diagnostic code on the fusion

procedure claim.

Of the 24,866 patients who met our eligibility criteria, 4,511 (18.1%) had received

intraoperative rhBMPs during the lumbar fusion operation. Table 4-4 presents the key

demographic and clinical characteristics of this population. In general, the use of rhBMPs was

associated with older age, female sex and public insurance coverage (all OR > 1, all p values <

0.001). Additionally, procedures involving intraoperative rhBMPs were more likely to utilize

anterior instrumentation, biomechanical cages and allograft bone grafts than the controls (all p

values < 0.001). Patients who received the osteobiologic were also more likely to have

undergone a concurrent discectomy procedure and to have higher levels of comorbidity as

measured using Charlson-Elixhauser index than their comparators (p values <0.01).

We used a logistic regression model to estimate the probability of receiving rhBMPs

during the lumbar fusion procedure based on observed patient and procedure characteristics and

91

then attempted to match each of the 4,511 rhBMP cases with up to two controls on the basis of

this propensity for exposure. In the end, we matched 4,087 rhBMP cases to two controls and an

additional 348 cases to one control resulting in a propensity score matched cohort of 12,963. As

shown in Table 4-4, this application of the 2:1 greedy matching algorithm with caliper width

restriction was able to balance the distribution of key observed characteristics between the

rhBMP-exposed and unexposed groups (all absolute SMD values ≤ 0.2).

Analytical results

For the primary analyses, the 12,963 patients in the propensity score matched cohort were

followed from the date of the index procedure until the first observed lumbar refusion event

(n=202), or until the end of enrollment in a FFS plan, the end of the study period or death

(n=12,761), whichever came first. After propensity score matching, the mean length of follow-

up in the rhBMP exposed group (19.4 (SD = ±12.2) months) was comparable to average length

of follow-up in the control group (19.5 (SD = ±12.1) months, SMD = 0.007).

Overall, 1.3% (n=59) of the rhBMP cases and 1.7% (n=143) of the controls underwent a

refusion procedure during follow-up which was equivalent to an incidence rate of 10.6 refusion

procedures per 1000 person years in the rhBMP exposed group and 8.4 refusion procedures per

1000 person years among the controls. The average time to the first observed refusion procedure

was 12.7 months in both groups (12.7 months (SD = ±9.1 months among the rhBMP cases and

12.7 (SD = ±8.8) months among the controls, T-Test p value = 0.99). In this propensity score

matched population, rhBMP use was associated with a lower risk for a refusion procedure (HR

(95% CI): 0.79 (0.58, 1.06)), however evidence of the reduction in risk was not statistically

significant (p value = 0.11).

92

In the first sensitivity analysis, we examined the effect of an intervening primary fusion

surgery and the death of a patient on our calculation of the risk for a refusion procedure. As

illustrated through the hypothetical patient 2 in Figure 3-1, a fraction of the patients (n=20,

10.4%) had a second primary fusion procedure before the first refusion surgery was observed.

For this reanalysis, patients were followed from the date of the index procedure until the first

observed lumbar refusion event (n=192), any subsequent primary fusion surgery (n=422), the

end of enrollment in a FFS plan (n=435), the end of the study period (n=11660), or death

(n=254), whichever came first. Patients who received rhBMPs were less likely to be censored

due to a subsequent primary fusion (3.0% vs. 3.4%, p value = 0.09) and, conversely, more likely

to censored due to death (2.1% vs. 1.9%, p value = 0.32) than their selected comparators;

however, these differences were not statistically significant.

Using this competing risk model, we observed 56 (1.3%) refusion events among the

rhBMP cases and 136 (1.6%) in the control group. The calculated association between rhBMP

use and the risk for a refusion procedure from this reanalysis was almost identical to the result

obtained from the primary model (CHR (95% CI): 0.78 (0.5, 1.07), p value = 0.12).

Consequently, the conclusions of this sensitivity analysis were consistent with those arrived at by

the main analysis: While the use of rhBMPs during lumbar fusion procedures was associated

with a reduced risk for refusion procedures, evidence for the observed association was not

statistically significant.

The second reanalysis was aimed at evaluating the effect of the propensity score

technique used on the robustness of our conclusions. The characteristics of the radius matched

population closely mirrored the source population since all 4,511 rhBMP-exposed cases who met

our eligibility criteria were successfully matched and only 4 of the 20,355 potential controls were

93

excluded. Notably, the propensity score radius matching approach was unable to attain

comparability in age and insurance type between the cases and controls (absolute SMD > 0.25);

in response, these variables were included explicitly in the outcome model.

A total of 376 (1.5%) of the 24,862 patients in the radius matched study cohort

underwent a refusion procedure. Patients in the rhBMP group had notably longer follow-up

times (19.4 (SD ± 12.2) months) than those in the control group (17.6 (SD = ±11.8) months,

SMD = 0.147). We observed 316 refusion procedure events among the rhBMP cases and 60 in

the control group. In this study cohort, the effect of rhBMP use on refusion risk was stronger

than what was observed in the primary analysis (HR (95% CI): 0.77 (0.58, 1.03)). However, as

was the case in the primary model, the association did not reach our pre-specified level of

statistical significance (p value = 0.08). Moreover, there were no substantive changes in the

estimate or its related conclusions after we accounted for the competing risks of death or an

intervening primary fusion procedure (CHR (95% CI): 0.76 (0.56, 1.02), p value = 0.07).


We created subpopulations of patients who had received a fusion procedure for LDDD

(n=2,602), Stenosis (n=7,442) and Listhesis (n=10,649) conditions in order to understand how

the underlying condition may affect the relationship between rhBMP use and revision procedure

rates (Research Question 2a2). Chi-square analysis revealed that the rate of rhBMP use was

significantly lower (16.1%) in the LDDD population than in the Stenosis (17.5%) and the

Listhesis (20.4%) groups (p value < 0.001).

Older age, public insurance coverage, higher levels of comorbidity and the inclusion of a

biomechanical cage were consistent correlates of intraoperative rhBMP use across all the three

clinical subpopulations analyzed (all OR > 1, all p value < 0.005). The choice of surgical

94

approach was also associated with the likelihood of receiving the osteobiologic during the fusion

procedure. In all three cohorts, circumferential procedures were significantly less likely to

utilize rhBMPs than anteriorly approached surgeries (LDDD cohort OR (95%CI): 0.63 (0.48,

0.83), Stenosis Cohort OR (95%CI): 0.56 (0.39, 0.81), Listhesis cohort OR (95%CI): 0.52

(0.39, 0.70)).

In the case of some of the associations tested, the correlation between the patient or

procedure characteristic and rhBMP exposure varied based on the indication for surgery: female

sex and geographical location were only associated with rhBMP exposure within the Listhesis

cohort (both p values < 0.05); allograft bone substrate use was linked to osteobiologic use in the

Stenosis and Listhesis populations (both p values < 0.05); and the use of non-segmental

instrumentation was associated with rhBMP use in the Stenosis cohort alone (p value = 0.002) .

We applied 2:1 stratified greedy matching in order to adjust for the identified

confounders. Overall, we created a propensity score matched LDDD cohort of 1,120, a matched

Stenosis cohort of 3,797 and a matched Listhesis cohort of 6,200 (Figure 4-3). Each of these

matching procedures was successful in balancing the distribution of key patient and procedure

characteristics between the rhBMP-exposed and unexposed groups (Tables 4-5, 4-6 and 4-7).

Analytical results

Patients were followed from the time of the procedure until the first observed lumbar

refusion event, the end of coverage by an MPCD FSS plan, the end of the study period

(12/31/2010), or death, whichever came first. In the case of the LDDD cohort, the mean follow-

up time was 20.0 (SD = ±12.6) months while the median was 18.3 months. Both the mean (19.3

(SD = ±12.1) months) and median (18.0 months) durations of follow-up in the Stenosis

population were shorter than in the LDDD cohort. Shorter still was the average length of follow-

95

up in the Listhesis population (Mean: 18.8 (SD = ±12.1) months, Median: 17.4 months). Patients

who received rhBMPs, in all three subpopulations, had statistically comparable lengths of

follow-up as their respective controls (all absolute SMD values < 0.17).

Overall, 5 (1.3%) rhBMP cases and 19 (2.6%) controls in the LDDD study population

underwent a refusion procedure during follow-up yielding a hazard ratio of 0.47 (HR 95% CI:

0.17- 1.26, p value = 0.13). This calculation suggests that the use of rhBMPs in LDDD fusion

procedures was associated with a lower hazard for refusion surgeries but the difference in risk

was not statistically significant. The conclusion proved to be robust even after accounting for

competing risks, namely death and the occurrence of an intervening primary fusion procedure

(CHR (95% CI): 0.44 (0.15, 1.31), p value = 0.14).

While rhBMP use appears to lower the risk for refusion in the LDDD population, the

inverse was observed in the Listhesis cohort. In the propensity score matched Listhesis

population, patients who received the osteobiologic during the index fusion were more likely to

undergo a revision procedure during observation window than their respective controls (1.6%

(n=33) vs. 1.4% (n=56), HR (95% CI): 1.12 (0.73, 1.73), p value = 0.59). Incorporating death

and any subsequent primary fusion procedures as competing events in the model did not change

the original conclusions of this analysis: rhBMP use during Listhesis-indicated fusion

procedures was associated with higher hazards for refusion operations but the difference in risk

was not statistically significant (CHR (95% CI): 1.18 (0.76, 1.82), p value = 0.46).

In contrast, the use of rhBMPs during Stenosis-indicated procedures was significantly

associated with lower hazards for a refusion procedure. In this propensity score matched

subpopulation, 13 (1.0%) patients in the rhBMP-exposed group and 47 (1.9%) patients in the

control group underwent a refusion procedure (HR (95% CI): 0.52 (0.28, 0.96), p value = 0.04).

96

Like the LDDD and Listhesis results described previously, the conclusion on the refusion

procedure risk was robust even after accounting for death and any intervening primary fusion

surgery as competing events (CHR (95% CI): 0.51 (0.27, 0.96), p value = 0.04).

Summary

Results of the all the models used to assess the association between rhBMP use and the

risk for refusion procedures appear in Table 4-8. In summary, our investigation suggests that the

association between the use of the osteobiologic and the risk of undergoing a subsequent refusion

procedure varies based on the condition for which the original surgery was conducted. More

specifically, we observed that while the use of rhBMPs during Stenosis-indicated spinal fusion

procedures was associated with a two-fold decrease in the risk for refusion procedures, no

statistically significant association was observed when the analysis was conducted within cohorts

of LDDD or Listhesis indicated fusion procedure recipients.

From a methodological standpoint, the results of this study revealed that the process of

explicitly accounting for death and any intervening primary fusion procedures as competing risks

do not significantly alter the calculated association between rhBMP use and the risk for

subsequent refusion procedures. Additionally, the use of radius matching arrived at the same

conclusions as the 2:1 greedy matching scheme used in the primary analysis albeit with a larger

sample size and narrower confidence intervals.

97

Tables and figures

Figure 4-2. Refusion risk analyses study population creation flowchart

Spinal Fusions with rhBMPs

(CASES)

(N=4,511)

Spinal Fusion without rhBMPs

(POTENTIAL CONTROLS)

(N=20,355)

Exclusion Criteria

Less than 6 months of continuous

eligibility in a FFS plan (N=6,480)

Concurrent fusion at other regions of

the spine (N= 150)

Prior rhBMP Exposure (N=7)

Spinal Fracture/Dislocation (N=427)

Spinal cord Injury (N=48)

Congenital Abnormality (N=2,752)

4,434 rhBMP-fusion cases were propensity

score matched with 8,529 controls

Patients, ≥ 21 years old, who received a primary (non-revision)

single level lumbar fusion procedure for a degenerative condition

(N = 31,912)

==

==

98

Figure 4-1. Refusion risk cohorts (subpopulations)

Stenosis-indicated procedures

(Fusion with rhBMP: N=1,299

Fusion without rhBMP: N=6,143)

Listhesis-indicated procedures

(Fusion with rhBMP: N= 2,175

Fusion without rhBMP: N=8,474)

Exclusion Criteria

Less than 6 months of continuous eligibility in a FFS

plan (N=6,480)

Concurrent fusion at other regions of the spine (N= 150)

Prior rhBMP Exposure (N=7)


Spinal cord Injury (N=48)

Congenital Abnormality (N=2,752)

LDDD-indicated procedures

(Fusion with rhBMP: N= 419

Fusion without rhBMP: N =2,183)

Propensity Score Matched LDDD

Population

(Fusion with rhBMP: N= 397

Fusion without rhBMP: N= 723)

Propensity Score Matched Stenosis

Population


Fusion without rhBMP: N= 2,512)

Propensity Score Matched Listhesis

Population


Fusion without rhBMP: N= 4,076)

Patients, ≥ 21 years old, who received a primary (non-revision)

single level lumbar fusion procedure for a degenerative condition

(N = 31,464) ==

99

Table 4-4. Baseline characteristics of refusion analysis cohort (any degenerative condition population)

General Cohort Propensity Score Matched Cohort

BMP Use

BMP Use


No

(n =20 355

81.9%)

Yes

(n=4 511,

18.1% )

OR (95% CI) p value

No

(n=8529,

65.8%)

Yes

(n=4334,

34.2%)

SMD

Mean Follow Up, months

(SD) 17.6 (11.8) 19.4 (12.2) - <0.001** 19.2 (12.1) 19.3 (12.2) 0.007

Age

21-45 years 3374 (16.6) 361 (8.0) 0.77 (0.68, 0.87) <0.001** 635 (7.4) 351 (7.9) 0.036

46 - 65 years 7794 (38.3) 1088 (24.1) Reference

2073 (24.3) 1067 (24.1)

Over 66 years 9187 (45.1) 3062 (67.9) 2.39 (2.21, 2.57)

5821 (68.2) 3016 (68.0)

Female Sex 12148 (59.7) 2782 (61.7) 1.09 (1.02, 1.16) 0.014* 5250 (61.6) 2736 (61.7) 0.003

Geographical Region

Midwest 5570 (27.4) 1260 (27.9) Reference 0.002* 2336 (27.4) 1232 (27.8) 0.019

Northeast 1883 (9.3) 386 (8.6) 0.91 (0.80, 1.03)

768 (9.0) 385 (8.7)

South 9510 (46.7) 2021 (44.8) 0.94 (0.87, 1.02)

3866 (45.3) 1985 (44.8)

West 3392 (16.7) 844 (18.7) 1.10 (1.00, 1.21)

1559 (18.3) 832 (18.8)

Insurance Type

Medicaid 235 (1.2) 88 (2.0) 10.6 (8.09, 14.0) <0.001** 173 (2.0) 87 (2.0) 0.028

Medicare 9493 (46.6) 3716 (82.4) 11.1 (9.79, 12.6)

6946 (81.4) 3642 (82.1)


581 (6.8) 274 (6.2)


218 (2.6) 115 (2.6)


0 (0.0) 0 (0.0)


604 (7.1) 311 (7.0)

All of the Above 8 (0.0) 5 (0.1) 17.7 (5.76, 54.5) 7 (0.1) 5 (0.1)

100



BMP Use

BMP Use

Characteristic, n (%) No Yes OR (95% CI) p value No Yes SMD

Surgical Approach

Anterior 1992 (9.8) 486 (10.8) Reference <0.001** 738 (8.7) 454 (10.2) 0.054

Posterior 16877 (82.9) 3811 (84.5) 0.93 (0.83, 1.03)

7376 (86.5) 3766 (84.9)


415 (4.9) 214 (4.8)


Anterior 1743 (8.6) 337 (7.5) 0.86 (0.76, 0.97) 0.017 553 (6.5) 318 (7.2) 0.027

Posterior 2268 (11.1) 472 (10.5) 0.93 (0.84, 1.03) 0.188 932 (10.9) 469 (10.6) 0.011

Non-segmental 13212 (64.9) 3070 (68.1) 1.15 (1.08, 1.23) <0.001** 5859 (68.7) 3019 (68.1) 0.013

Biomechanical Cage 12944 (63.6) 3189 (70.7) 1.38 (1.29, 1.48) <0.001** 5877 (68.9) 3112 (70.2) 0.028


Allograft Bone Substrate 4986 (24.5) 1372 (30.4) 1.35 (1.25, 1.45) <0.001** 2398 (28.1) 1316 (29.7) 0.035

Autograft Bone Substrate 8239 (40.5) 1778 (39.4) 0.96 (0.90, 1.02) 0.190 3418 (40.1) 1750 (39.5) 0.012

Year of Procedure

2007 2585 (12.7) 641 (14.2) Reference <0.001** 1137 (13.3) 620 (14.0) 0.021

2008 5431 (26.7) 1212 (26.9) 0.90 (0.81, 1.00)

2274 (26.7) 1190 (26.8)

2009 6375 (31.3) 1290 (28.6) 0.82 (0.73, 0.91)

2500 (29.3) 1280 (28.9)

2010 5964 (29.3) 1368 (30.3) 0.93 (0.83, 1.03)

2618 (30.7) 1344 (30.3)


Discectomy 11405 (56.0) 2432 (53.9) 0.92 (0.86, 0.98) 0.010* 4525 (53.1) 2391 (53.9) 0.017

Laminectomy 11503 (56.5) 2582 (57.2) 1.03 (0.97, 1.10) 0.374 4987 (58.5) 2552 (57.6) 0.019

101



BMP Use

BMP Use


Charlson-Elixhauser

Comorbidity Index

Mean (SD), Median

0.68 (1.71) 0.96 (1.91) 1.09 (1.07, 1.11) <0.001** 0.99 (1.96) 0.96 (1.91) 0.015

Other Spinal Conditions on

Claim

Non-Specific Back Pain 3837 (18.9) 806 (17.9) 0.94 (0.86, 1.02) 0.125 1537 (18.0) 792 (17.9) 0.006

Degenerative Disc Disease 7097 (34.9) 1355 (30.0) 0.80 (0.75, 0.86) <0.001** 2499 (29.3) 1333 (30.1) 0.017

Herniated Disc 5110 (25.1) 866 (19.2) 0.71 (0.65, 0.77) <0.001** 1683 (19.7) 863 (19.5) 0.007

Stenosis 11954 (58.7) 2851 (63.2) 1.21 (1.13, 1.29) <0.001** 5510 (64.6) 2809 (63.4) 0.026

Listhesis 8474 (41.6) 2175 (48.2) 1.31 (1.22, 1.39) <0.001** 4102 (48.1) 2126 (47.9) 0.003

Spondylopathy 13 (0.1) 3 (0.1) 1.04 (0.30, 3.66) 0.949 4 (0.0) 3 (0.1) 0.009

Osteoporosis 48 (0.2) 17 (0.4) 1.60 (0.92, 2.79) 0.095 38 (0.4) 16 (0.4) 0.013

OR: Odds Ratio of BMP exposure, CI: Confidence Interval, SMD: Absolute Standardized Mean Difference

*: p value of less than 0.05, **: p value of less than 0.001

102

Table 4-5. Baseline characteristics of refusion analysis cohort (LDDD population)


BMP Use

BMP Use


No

(n =2183,

83.9%)

Yes

(n=419,

16.1%)

OR (95% CI) p value

No

(n=723,

65.8%)

Yes

(n=397,

35.5%)

SMD

Mean Follow Up, months (SD) 16.9 (12.1) 20.2 (12.6) - <0.001* 20.0 (12.7) 19.9 (12.6) 0.008

Age

21-45 years 787 (36.1) 83 (19.8) 0.77 (0.64, 0.93) <0.001* 149 (20.6) 80 (20.2) 0.07

46 - 65 years 1024 (46.9) 156 (37.2) Reference

286 (39.6) 152 (38.3)

Over 66 years 372 (17.0) 180 (43.0) 2.78 (2.37, 3.27)

288 (39.8) 165 (41.6)

Female Sex 1244 (57.0) 246 (58.7) 1.16 (1.01, 1.34) 0.513 427 (59.1) 229 (57.7) 0.028

Geographical Region

Midwest 628 (28.8) 120 (28.6) Reference 0.750 182 (25.2) 108 (27.2) 0.066

Northeast 167 (7.7) 34 (8.1) 1.24 (0.93, 1.64)

73 (10.1) 34 (8.6)

South 1033 (47.3) 189 (45.1) 1.06 (0.90, 1.26)

336 (46.5) 185 (46.6)

West 355 (16.3) 76 (18.1) 1.25 (1.01, 1.55)

132 (18.3) 70 (17.6)

Insurance Type

Medicaid 70 (3.2) 22 (5.3) 10.0 (6.71, 15.0) <0.001* 44 (6.1) 20 (5.0) 0.105

Medicare 572 (26.2) 300 (71.6) 15.1 (11.9, 19.1)

487 (67.4) 281 (70.8)

Commercial 29 (1.3) 17 (4.1) 16.4 (10.5, 25.5)

27 (3.7) 16 (4.0)

Medicare + Medicaid 1293 (59.2) 43 (10.3) Reference

80 (11.1) 43 (10.8)


0 (0.0) 0 (0.0)


84 (11.6) 36 (9.1)

All of the Above 1 (0.0) 1 (0.2) 10.2 (1.05, 99.5)

1 (0.1) 1 (0.3)

103



BMP Use

BMP Use


Surgical Approach

Anterior 763 (35.0) 162 (38.7) Reference 0.012* 245 (33.9) 150 (37.8) 0.089

Posterior 1195 (54.7) 233 (55.6) 0.95 (0.81, 1.12)

425 (58.8) 223 (56.2)


53 (7.3) 24 (6.0)


Anterior 568 (26.0) 98 (23.4) 0.86 (0.72, 1.03) 0.259 150 (20.7) 94 (23.7) 0.071

Posterior 198 (9.1) 30 (7.2) 1.07 (0.92, 1.25) 0.207 59 (8.2) 30 (7.6) 0.022

Non-segmental 1069 (49.0) 205 (48.9) 0.95 (0.82, 1.11) 0.987 369 (51.0) 195 (49.1) 0.038

Biomechanical Cage 1722 (78.9) 362 (86.4) 1.37 (1.16, 1.63) <0.001* 598 (82.7) 340 (85.6) 0.08


Allograft Bone Substrate 679 (31.1) 125 (29.8) 1.03 (0.88, 1.21) 0.606 194 (26.8) 119 (30.0) 0.070


Year of Procedure

2007 381 (17.5) 82 (19.6) Reference 0.738 143 (19.8) 74 (18.6) 0.032

2008 631 (28.9) 122 (29.1) 0.81 (0.67, 0.98)

214 (29.6) 117 (29.5)

2009 621 (28.4) 113 (27.0) 0.74 (0.61, 0.90)

189 (26.1) 106 (26.7)

2010 550 (25.2) 102 (24.3) 0.68 (0.56, 0.84)

177 (24.5) 100 (25.2)


Discectomy 1364 (62.5) 254 (60.6) 0.94 (0.82, 1.09) 0.472 4525 (53.1) 2391 (53.9) 0.027

Laminectomy 610 (27.9) 116 (27.7) 1.03 (0.89, 1.20) 0.914 4987 (58.5) 2552 (57.6) 0.025

104



BMP Use

BMP Use


Charlson-Elixhauser

Comorbidity Index, Mean

(SD) 0.49 (1.44) 0.78 (1.73) 1.12(1.06, 1.20) <0.001* 0.87 (1.85) 0.75(1.68) 0.070


Claim


Spondylopathy 3 (0.1) 0 (0.0) 0.00 (0.00, 0.00) 0.976 0 (0.0) 0 (0.0) 0.000

Osteoporosis 4 (0.2) 3 (0.7) 2.15 (0.56, 8.34) 0.074 4 (0.6) 2 (0.5) 0.007

OR: Odds Ratio of BMP exposure, CI: Confidence Interval, SMD: Absolute Standardized Mean Difference, *: p value less than

0.05

105

Table 4-6. Baseline characteristics of refusion analysis cohort (Stenosis population)


BMP Use

BMP Use


No

(n =6143,

82.5%)

Yes

(n=1299,

17.5%)

OR (95% CI) p value

No

(n=2512,

66.2%)

Yes

(n=1285,

33.8%)

SMD

Mean Follow Up, months 18.0 (11.8) 19.6 (12.2) - <0.001* 19.1 (12.0) 19.6 (12.2) 0.036

Age

21-45 years 708 (11.5) 97 (7.5) 0.96 (0.76, 1.23) <0.001* 171 (6.8) 92 (7.2) 0.038

46 - 65 years 2246 (36.6) 319 (24.6) Reference

628 (25.0) 314 (24.4)

Over 66 years 3189 (51.9) 883 (68.0) 1.95 (1.70, 2.24)

1713 (68.2) 879 (68.4)

Female Sex 3462 (56.4) 726 (55.9) 0.98 (0.87, 1.11) 0.757 1389 (55.3) 719 (56.0) 0.013

Geographical Region


Northeast 552 (9.0) 92 (7.1) 0.77 (0.60, 0.99)

165 (6.6) 91 (7.1)

South 3038 (49.5) 647 (49.8) 0.98 (0.85, 1.14)

1283 (51.1) 636 (49.5)

West 1044 (17.0) 233 (17.9) 1.03 (0.86, 1.24)

449 (17.9) 232 (18.1)

Insurance Type

Medicaid 42 (0.7) 24 (1.8) 16.7 (9.52, 29.1) <0.001* 36 (1.4) 22 (1.7) 0.038

Medicare 3252 (52.9) 1080 (83.1) 9.68 (7.49, 12.5)

2098 (83.5) 1072 (83.4)

Commercial 80 (1.3) 32 (2.5) 11.7 (7.22, 18.8)

57 (2.3) 28 (2.2)


141 (5.6) 65 (5.1)


178 (7.1) 97 (7.5)

All of the Above 3 (0.0) 1 (0.1) 9.71 (1.00, 94.7)

2 (0.1) 1 (0.1)

106



BMP Use

BMP Use


Discectomy 3369 (54.8) 681 (52.4) 0.91 (0.80, 1.02) 0.112 1289 (51.3) 676 (52.6) 0.026

Laminectomy 3923 (63.9) 798 (61.4) 0.90 (0.80, 1.02) 0.099 1593 (63.4) 794 (61.8) 0.034

Surgical Approach


Posterior 5423 (88.3) 1152 (88.7) 0.81 (0.63, 1.02)

2241 (89.2) 1142 (88.9)


114 (4.5) 53 (4.1)


Anterior 339 (5.5) 70 (5.4) 0.98 (0.75, 1.27) 0.853 126 (5.0) 68 (5.3) 0.013

Posterior 823 (13.4) 163 (12.5) 0.93 (0.77, 1.11) 0.412 325 (12.9) 163 (12.7) 0.008

Non-segmental 3805 (61.9) 864 (66.5) 1.22 (1.08, 1.38) 0.002* 1646 (65.5) 852 (66.3) 0.016



Allograft Bone 1448 (23.6) 413 (31.8) 1.51 (1.33, 1.72) <0.001* 757 (30.1) 402 (31.3) 0.025

Autograft Bone 2514 (40.9) 541 (41.6) 1.03 (0.91, 1.16) 0.630 1062 (42.3) 536 (41.7) 0.011

Year of Procedure

2007 742 (12.1) 179 (13.8) Reference 0.150 327 (13.0) 174 (13.5) 0.039

2008 1598 (26.0) 348 (26.8) 0.90 (0.74, 1.10)

642 (25.6) 345 (26.8)

2009 1965 (32.0) 380 (29.3) 0.80 (0.66, 0.98)

752 (29.9) 379 (29.5)

2010 1838 (29.9) 392 (30.2) 0.88 (0.73, 1.08)

791 (31.5) 387 (30.1)

107


General Cohort

Propensity Score Matched Cohort

BMP Use

BMP Use


Charlson-Elixhauser

Comorbidity Index,

Mean (SD)

0.75 (1.81) 1.00 (1.98) 1.07 (1.04, 1.10) <0.001* 1.01 (2.01) 1.01 (1.99) 0.004


Claim


DDD 2269 (36.9) 426 (32.8) 0.83 (0.73, 0.95) 0.005 828 (33.0) 422 (32.8) 0.003

Herniated Disc 1417 (23.1) 258 (19.9) 0.83 (0.71, 0.96) 0.012 502 (20.0) 254 (19.8) 0.006

Spondylopathy 5 (0.1) 0 (0.0) 0.00 (0.00, 0.00) 0.968 0 (0.0) 0 (0.0) 0.000

Osteoporosis 11 (0.2) 2 (0.2) 0.86 (0.19, 3.88) 0.844 3 (0.1) 2 (0.2) 0.010

OR: Odds Ratio of BMP exposure, CI: Confidence Interval, SMD: Absolute Standardized Mean Difference,

*: p value of less than 0.05

108

Table 4-7. Baseline characteristics of refusion analysis cohort (Listhesis population)

General Cohort

Propensity Score Matched Cohort

BMP Use

BMP Use


No

(n =8474,

79.6%)

Yes

(n=2175,

20.4%)

OR (95% CI) p value

No

(n=8529,

65.8%)

Yes

(n=4334,

34.2%)

SMD

Mean Follow Up, months 17.9 (11.9) 18.9 (12.1) - <0.001* 18.9 (12.1) 18.8 (12.0) 0.003

Age

21-45 years 696 (8.2) 69 (3.2) 0.71 (0.54, 0.93) <0.001* 111 (2.7) 65 (3.1) 0.034

46 - 65 years 2946 (34.8) 412 (18.9) Reference

794 (19.5) 401 (18.9)

Over 66 years 4832 (57.0) 1694 (77.9) 2.51 (2.23, 2.82)

3171 (77.8) 1658 (78.1)

Female Sex 5478 (64.6) 1467 (67.4) 1.13 (1.03, 1.25) 0.014* 2768 (67.9) 1430 (67.3) 0.013

Geographical Region


Northeast 884 (10.4) 210 (9.7) 0.95 (0.80, 1.13)

399 (9.8) 205 (9.7)

South 3480 (41.1) 858 (39.4) 0.99 (0.88, 1.11)

1621 (39.8) 840 (39.5)

West 1503 (17.7) 457 (21.0) 1.22 (1.07, 1.40)

837 (20.5) 441 (20.8)

Insurance Type

Medicaid 55 (0.6) 11 (0.5) 4.56 (2.32, 8.94) <0.001* 13 (0.3) 11 (0.5) 0.048

Medicare 4592 (54.2) 1866 (85.8) 9.26 (7.62, 11.3)

3467 (85.1) 1819 (85.6)

Commercial 103 (1.2) 43 (2.0) 9.51 (6.36, 14.2)

84 (2.1) 42 (2.0)


253 (6.2) 114 (5.4)


259 (6.4) 138 (6.5)

All of the Above 0 (0.0) 3 (0.1) 0.00 (0.00, 0.00)

0 (0.0) 0 (0.0)

109



BMP Use

BMP Use


Surgical Approach

Anterior 329 (3.9) 115 (5.3) Reference <0.001* 154 (3.8) 94 (4.4) 0.038

Posterior 7552 (89.1) 1952 (89.7) 0.74 (0.59, 0.92)

3698 (90.7) 1923 (90.5)


224 (5.5) 107 (5.0)


Anterior 353 (4.2) 89 (4.1) 0.98 (0.77, 1.24) 0.878 141 (3.5) 77 (3.6) 0.009

Posterior 858 (10.1) 214 (9.8) 0.97 (0.83, 1.13) 0.695 421 (10.3) 213 (10.0) 0.011

Non-segmental 6240 (73.6) 1644 (75.6) 1.11 (0.99, 1.24) 0.064 3096 (76.0) 1608 (75.7) 0.006



Allograft Bone 1861 (22.0) 671 (30.9) 1.59 (1.43, 1.76) <0.001* 1100 (27.0) 630 (29.7) 0.0594

Autograft Bone 3690 (43.5) 897 (41.2) 0.91 (0.83, 1.00) 0.053 1689 (41.4) 881 (41.5) 0.0008

Year of Procedure

2007 959 (11.3) 276 (12.7) Reference 0.051 467 (11.5) 260 (12.2) 0.028

2008 2233 (26.4) 558 (25.7) 0.87 (0.74, 1.02)

1038 (25.5) 548 (25.8)

2009 2673 (31.5) 635 (29.2) 0.83 (0.70, 0.97)

1220 (29.9) 624 (29.4)

2010 2609 (30.8) 706 (32.5) 0.94 (0.80, 1.10)

1351 (33.1) 692 (32.6)


Discectomy 4116 (48.6) 1079 (49.6) 1.04 (0.95, 1.15) 0.388 2033 (49.9) 1054 (49.6) 0.005

Laminectomy 5843 (69.0) 1458 (67.0) 0.92 (0.83, 1.01) 0.086 2775 (68.1) 1438 (67.7) 0.008

110



BMP Use

BMP Use


Charlson-Elixhauser

Comorbidity Index

Mean (SD), Median

0.70 (1.73) 0.96

(1.94) 1.08 (1.05, 1.10) <0.001* 0.96 (2.01) 0.96 (1.99) 0.003

Spinal Conditions

Non-Specific Back Pain 1056 (12.5) 230 (10.6) 0.83 (0.71, 0.97) 0.016* 511 (12.5) 219 (10.3)

0.070

DDD 1508 (17.8) 356 (16.4) 0.90 (0.80, 1.03) 0.118 654 (16.0) 347 (16.3) 0.008

Herniated Disc 1157 (13.7) 247 (11.4) 0.81 (0.70, 0.94) 0.004* 471 (11.6) 243 (11.4) 0.004

Stenosis 5810 (68.6) 1552 (71.4) 1.14 (1.03, 1.27) 0.012* 2942 (72.2) 1524

(71.8) 0.011

Spondylopathy 2 (0.0) 1 (0.0) 1.95 (0.18, 21.5) 0.585 2 (0.0) 1 (0.0) 0.001

Osteoporosis 31 (0.4) 11 (0.5) 1.38 (0.69, 2.76) 0.355 18 (0.4) 11 (0.5) 0.011

OR: Odds Ratio of BMP exposure, CI: Confidence Interval, SMD: Absolute Standardized Mean Difference, SD: Standard

Deviation, *: p value of less than 0.05

111

Table 4-8. Refusion-rhBMP risk analyses summary results

Study Population Analytical Model HR (95% CI) p value

Any Degenerative

Condition of the

Lumbar Spine

Cox Proportional Hazard Regression 0.79 (0.58, 1.07) 0.12

Cause-Specific Hazard Regression† 0.79 (0.57, 1.07) 0.13

Fine and Gray Regression† 0.79 (0.58, 1.07) 0.13

Lumbar Degenerative

Disc Disease




Stenosis

Cox Proportional Hazard Regression 0.52 (0.28, 0.96) 0.04*

Cause-Specific Hazard Regression† 0.51 (0.27, 0.96) 0.04*

Fine and Gray Regression† 0.50 (0.27, 0.95) 0.04*

Listhesis




†: Death and Second Primary Fusion Procedure analyzed as competing events

*: p value of less than 0.05

112


This section of the dissertation evaluated the association between intraoperative rhBMP

use and post-fusion procedure readmission patterns using three measures: 1) 30 day readmission

rate, 2) time to the first LDDD-related readmission, and 3) the number of LDDD-related

readmissions during the first year. (Research Question 2b)


We identified 1,132 Medicare FFS enrollees, aged 66 or older, who had received a

LDDD-indicated lumbar fusion procedure. Out of these, we excluded 128 patients who had less

than six months of continuous enrollment in a Medicare FFS plan prior to the index procedure,

148 patients who held non-Medicare insurance during our observation window, 290 patients who

were discharged or transferred to another inpatient setting, 13 patients who received a concurrent

fusion procedure at another region of the spine, 14 patients who were exposed to rhBMPs during

baseline and 97 procedures that included either a spinal fracture, a spinal cord injury or a

congenital spinal abnormality (Figure 4-4).

Table 4-9 presents key baseline characteristics of the 551 patients who met our eligibility

criteria. Out of these, 203 (36.8%) patients received intraoperative rhBMPs during their lumbar

fusion procedure. This population was mainly White (96.0%), female (56.4%) and sourced from

the Southern region of the United States (51.9%). Notably, multi-level fusion procedures were

more likely to utilize the osteobiologic than single level operations (OR (95% CI): 0.67 (0.47,

0.94), p value = 0.022). Other factors associated with lower odds of rhBMP use include being

over 80 years old at the time of the procedure, undergoing a concurrent laminectomy procedure

and receiving a posteriorly or circumferentially approached surgery (all p values <0.05).

Additionally, fusion procedures billed from the Northeastern States were less likely to include

113

rhBMPs than their comparators from the Midwest region (OR (95% CI): 0.60 (0.39, 0.92)). On

the other hand, the use of instrumentation was associated with significantly higher odds of

receiving the osteobiologic during the fusion procedure (all p values <0.005).

Using a logistic regression model, we estimated the probability of rhBMP use conditional

on observed patient and procedure characteristics. We then used this propensity score to match

176 rhBMP recipients to 176 controls yielding a matched cohort of 352. As shown in Table 4-9,

the application of a 1:1 optimal matching algorithm with caliper width restriction was able to

balance the distribution of key clinical and demographic characteristics between the rhBMP-

exposed and unexposed groups (all absolute SMD values ≤ 0.2).

The creation of the nested cohort, which required at least a year for both baseline and

outcome assessment, effectively excluded the 229 patients whose index procedures were

conducted in 2007 or 2010, and the 9 patients who died within a year of their procedure. Also

excluded were the 17 patients who had less than 365 of continuous enrollment prior to the index

operation and the 8 patients who lost their MPCD FFS coverage during the first year following

the surgery (Figure 4-4).

Given that recurrent readmissions could be associated with one’s mortality risk, we

compared the 1-year death rate in the rhBMP-exposed group (n=2, 1.7%) against with the death

rate in the unexposed group (n=7, 3.5%). Our analysis found no statistically significant

correlation between rhBMP use and early death thus arguably confirming that the exclusion of

these patients did not bias our conclusions (Fisher Exact Test p value = 0.494).

Table 4-10 summarizes patient and procedure characteristics of this nested cohort

(n=295). Like the general cohort from which it was sourced, this nested population is also

predominately White (96.6%), female (58.6%) and under the age of 76 (75.3%). A total of 111

114

(37.6 %) patients had received rhBMPs during the index fusion procedure. Osteobiologic use in

this subpopulation was also observed to vary based on age, geographic location, number of levels

fused and the use of instrumentation (all p values <0.05).

Using the propensity score for exposure we matched 92 of the 111 rhBMP-exposed cases

to 92 controls thus creating a nested cohort of 184 patients. As indicated in Table 4-10, the 1:1

optimal matching algorithm with caliper width restriction was able to achieve comparability

across the key demographic and clinical characteristics of this population (all absolute SMD

values ≤ 0.2).

Analytical results

Overall, 28 (8.6 %) patients in the general, propensity score matched cohort were

readmitted within 30 days of discharge. A marginally lower proportion of patients in the

rhBMP-exposed group were readmitted within 30 days of discharge then their selected

comparators (6.8% vs. 8.0%, OR (95%CI): 0.87 (0.39, 1.96) however, the evidence associating

rhBMP use to the 30-day readmission rate was not statistically significant (p value = 0.94).

Conclusions of the unadjusted, and age and sex adjusted models were consistent with the main

analysis: we found no statistically significant association between rhBMP use and 30 day

readmission rates among the 551 patients who met our eligibility criteria (Unadjusted OR (95%

CI): 0.97 (0.52, 1.80), p value = 0.92; Age and Sex Adjusted OR (95% CI): 1.03 (0.54, 1.94), p

value = 0.93).

In contrast, more of the rhBMP users in the Primary Diagnosis (10.2% vs 9.1%) and the

Comprehensive Definition cohorts were readmitted compared to their respective controls

(Primary Diagnosis Definition Cohort: 10.2% vs 9.1%; Comprehensive Definition Cohort: 9.5%

vs 8.1%). However, like the main analysis, the observed differences were not statistically

115

significant (Primary Diagnosis Definition Cohort OR (95% CI): 1.16 (0.77, 1.74), p value =

0.48; Comprehensive Definition Cohort OR (95% CI): 1.18 (0.87, 1.60), p value = 0.29).

The 352 patients in general propensity score matched cohort were followed from the date

of discharge until their first LDDD-related readmission (n=10), first non-LDDD readmission

(n=132), their death (n=3) or the end of the study period (n=207), whichever came first. The

mean length of follow-up in the rhBMP exposed group (15.5 (SD = ± 11.8) months) was

comparable to average length of follow-up in the control group (15.2 (SD ± 12.2) months, SMD

= 0.02).

Three (1.7%) patients in the rhBMP-exposed group and 7 (4.0%) patients in the control

group were readmitted for an LDDD-related indication over a median follow-up duration of 13.2

months. The average time to these readmissions was 6.9 months in the rhBMP-exposed group

and 14.2 months in the rhBMP-unexposed group. In this propensity score matched population,

osteobiologic use was associated lower hazards for LDDD-related readmissions (CHR (95% CI):

0.44 (0.11, 1.70); however, the strength of the evidence was not statistically significant (p value

= 0.23). Similar conclusions were arrived using the larger primary diagnosis definition (n =

1080, CHR (95% CI): 0.76 (0.36, 1.61), p value = 0.47) and comprehensive definition cohorts (n

= 2088, CHR (95% CI): 0.85 (0.47, 1.53), p value = 0.58).

Only 3 (1.6%) of the 184 patients in the nested, propensity score matched, hierarchical

definition cohort had at least one LDDD-related readmission within a year of the index fusion

procedure; two patients belonged to the rhBMP exposed group and the other to control arm. We

found no evidence to suggest that the use of rhBMPs was associated with the number of LDDD-

related hospitalizations (IRR (95% CI): 1.57 (0.22, 11.4), p value = 0.65). Similar results were

arrived at through both the primary diagnosis definition cohort (2.1% (n=6) in the rhBMP group

116

vs 1.4% (n=4) among the controls, IRR (95% CI): 1.43 (0.42, 4.47), p value = 0.57) and the

comprehensive definition cohort (1.6% (n=9) in the rhBMP group vs 1.4% (n=8) among the

controls, IRR (95% CI): 1.15 (0.48, 2.81), p value = 0.76). Notably, none of the patients

analyzed incurred more than one LDDD-related hospitalization during the year of follow-up.

Summary

Results of the all the models used to assess the association between rhBMP use and the

risk for readmission appear in Table 4-15, 4-16 and 4-17. None of the analyses conducted found

sufficient evidence to reject the null hypothesis, namely, that the use of inpatient services

following the fusion event is independent of the patient’s rhBMP exposure status. The results of

the analyses did however offer insights into the comparability of LDDD patient populations

defined using the hierarchical, primary diagnosis and the comprehensive case definition

approach; to wit: analytical estimates did not vary significantly based on the method used to

identify LDDD-indicated fusion procedures.

117

Tables and figures

Figure 4-4. Readmission and ER visit analyses study population creation flowchart

Medicare FFS enrollees over 66 years old who received an

LDDD-indicated fusion procedure

(N= 1132)

N=

Nested Cohort

Propensity Score Matched Population

(Procedure with rhBMP: N= 92

Procedure without rhBMP: N= 92)

Study Source Population

(Fusion with rhBMP: N=348 Fusion without rhBMP: N=203)

Exclusion Criteria

1. Additional (Non-Medicare) Insurance

(N =148)

2. Less than 6 months of continuous

eligibility (N= 128)

3. Discharged/Transferred to an inpatient

facility (N=290)

4. Concurrent fusion in another region of the

spine (N=13)

5. rhBMP exposure during baseline (N = 3)

6. Spinal Fracture (N= 35), Spinal cord Injury

(N=1), Congenital Abnormality (N= 62)

Nested Cohort

Subjects with at least 1 year continuous

enrollment post-fusion (N=295)

General Cohort

Propensity Score Matched Population

(Procedure with rhBMP: N= 176

Procedure without rhBMP: N=176)

Secondary Exclusion Criteria

1. Procedure performed in 2007 or

2010 (N= 229)

2. Loss of FFS coverage within

365 days (N=8)

3. Died within 365 days post-

fusion (N=9)

4. Less than 365 days of look back

time (N=17)

118

Table 4-9. Baseline characteristics of readmission risk analysis population (hierarchical algorithm definition)


BMP Use

BMP Use

Characteristic, n (%) No

(n =348, 81.9%)

Yes

(n=203,

18.1%)

OR (95% CI) p value

No

(n = 176,

50.0%)

Yes

(n=176,

50.0%)

SMD

Age

66 - 70 years 154 (44.3) 111 (54.7) Reference 0.010* 94 (53.4) 97 (55.1) 0.067

71 -75 years 104 (29.9) 51 (25.1) 0.68 (0.45, 1.03)

44 (25.0) 43 (24.4)

76 -80 years 58 (16.7) 36 (17.7) 0.86 (0.53, 1.39)

31 (17.6) 31 (17.6)

Over 80 years 32 (9.2) 5 (2.5) 0.22 (0.08, 0.57)

7 (4.0) 5 (2.8)

Female Sex 193 (55.5) 118 (58.1) 1.11 (0.79, 1.58) 0.542 101 (57.4) 102 (58.0)

Race

White 335 (96.3) 194 (95.6) Reference 0.710 170 (96.6) 171 (97.2) 0.036

Black 10 (2.9) 8 (3.9) 1.38 (0.54, 3.56)

5 (2.8) 4 (2.3)

Other 3 (0.9) 1 (0.5) 0.58 (0.06, 5.57)

1 (0.6) 1 (0.6)

Geographical Region


Northeast 21 (6.0) 13 (6.4) 0.82 (0.38, 1.78)

14 (8.0) 11 (6.3)

South 197 (56.6) 89 (43.8) 0.60 (0.39, 0.92)

82 (46.6) 86 (48.9)

West 53 (15.2) 43 (21.2) 1.08 (0.64, 1.82)

33 (18.8) 33 (18.8)

Surgical Approach


Posterior 261 (75.0) 133 (65.5) 0.52 (0.34, 0.79)

125 (71.0) 123 (69.9)


11 (6.3) 12 (6.8)


Discectomy 198 (56.9) 134 (66.0) 1.47 (1.03, 2.11) 0.035* 117 (66.5) 114 (64.8) 0.036

119



BMP Use

BMP Use


Laminectomy 137 (39.4) 70 (34.5) 0.81 (0.57, 1.16) 0.254 64 (36.4) 64 (36.4) 0.000

Multiple Level

Procedure 186 (53.4) 88 (43.3) 0.67 (0.47, 0.94) 0.022* 80 (45.5) 81 (46.0)

Revision Procedure 14 (4.0) 6 (3.0) 0.73 (0.27, 1.92) 0.520 9 (5.1) 6 (3.4) 0.084


Anterior 35 (10.1) 38 (18.7) 2.06 (1.25, 3.38) 0.004* 25 (14.2) 32 (18.2) 0.108

Posterior 146 (42.0) 58 (28.6) 0.55 (0.38, 0.80) 0.002* 56 (31.8) 58 (33.0) 0.024

Non-segmental 108 (31.0) 75 (36.9) 1.30 (0.90, 1.87) 0.156 67 (38.1) 64 (36.4) 0.035


Osteogenetic Factors

Allograft Bone 96 (27.6) 67 (33.0) 1.29 (0.89, 1.88) 0.179 49 (27.8) 54 (30.7) 0.062

Autograft Bone 134 (38.5) 70 (34.5) 0.84 (0.59, 1.21) 0.346 66 (37.5) 63 (35.8) 0.035

Year of Procedure

2007 45 (12.9) 36 (17.7) 1.42 (0.83, 2.43) 0.402 31 (17.6) 28 (15.9) 0.046

2008 108 (31.0) 61 (30.0) Reference

56 (31.8) 57 (32.4)

2009 96 (27.6) 57 (28.1) 1.05 (0.67, 1.65)

48 (27.3) 49 (27.8)

2010 99 (28.4) 49 (24.1) 0.88 (0.55, 1.39)

41 (23.3) 42 (23.9)

Charlson-Elixhauser

Comorbidity Index

< 0 78 (22.4) 52 (25.6) 0.98 (0.62, 1.56) 0.208 40 (22.7) 46 (26.1) 0.082

0 103 (29.6) 70 (34.5) Reference

62 (35.2) 59 (33.5)

1-2 115 (33.0) 50 (24.6) 0.64 (0.41, 1.00)

50 (28.4) 47 (26.7)

120



BMP Use

BMP Use


≥ 3 52 (14.9) 31 (15.3) 0.88 (0.51, 1.50)

24 (13.6) 24 (13.6)

Other Spinal Conditions

on Claim

Back Pain 76 (21.8) 41 (20.2) 0.91 (0.59, 1.39) 0.649 43 (24.4) 35 (19.9) 0.011

Osteoporosis 2 (0.6) 1 (0.5) 0.86 (0.08, 9.50) 0.900 1 (0.6) 1 (0.6) 0.000



121

Table 4-10. Baseline characteristics of readmission risk analysis nested population (hierarchical algorithm definition)

General Cohort Propensity Score Matched

Cohort

BMP Use

BMP Use


No

(n = 184,

81.9%)

Yes

(n=111,

18.1%)

OR (95% CI) p value

No

(n = 92,

50.0%)

Yes

(n=92,

50.0%)

SMD

Age

66 - 70 years 71 (38.6) 64 (57.7) Reference 0.008* 50 (54.3) 49 (53.3) 0.169

71 -75 years 63 (34.2) 24 (21.6) 0.42 (0.24, 0.75)

21 (22.8) 22 (23.9)

76 -80 years 36 (19.6) 20 (18.0) 0.62 (0.32, 1.17)

15 (16.3) 18 (19.6)

Over 80 years 14 (7.6) 3 (2.7) 0.24 (0.07, 0.87)

6 (6.5) 3 (3.3)

Female Sex 111 (60.3) 62 (55.9) 0.83 (0.52, 1.34) 0.450 52 (56.5) 52 (56.5)

Race

White 177 (96.2) 108 (97.3) Reference 0.614 89 (96.7) 89 (96.7) 0.000

Black 7 (3.8) 3 (2.7) 0.70 (0.18, 2.77)

3 (3.3) 3 (3.3)

Geographical Region


Northeast 11 (6.0) 3 (2.7) 0.33 (0.08, 1.27)

3 (3.3) 3 (3.3)

South 110 (59.8) 50 (45.0) 0.55 (0.31, 0.95)

49 (53.3) 44 (47.8)

West 21 (11.4) 23 (20.7) 1.31 (0.63, 2.76)

17 (18.5) 15 (16.3)

Surgical Approach

Anterior 27 (14.7) 29 (26.1) Reference 0.056 15 (16.3) 20 (21.7) 0.140

Posterior 140 (76.1) 73 (65.8) 0.49 (0.27, 0.88) 68 (73.9) 64 (69.6)

Circumferential 17 (9.2) 9 (8.1) 0.49 (0.19, 1.29) 9 (9.8) 8 (8.7)

122



Cohort

BMP Use

BMP Use



Discectomy 106 (57.6) 71 (64.0) 1.31 (0.80, 2.12) 0.281 54 (58.7) 59 (64.1) 0.112

Laminectomy 73 (39.7) 39 (35.1) 0.82 (0.51, 1.34) 0.437 31 (33.7) 33 (35.9) 0.046

Multiple Level Procedure Indicator 99 (53.8) 48 (43.2) 0.65 (0.41, 1.05) 0.080 46 (50.0) 44 (47.8)

Revision Procedure Indicator 9 (4.9) 3 (2.7) 0.54 (0.14, 2.04) 0.364 0 (0.0) 3 (3.3) 0.260


Anterior 14 (7.6) 25 (22.5) 3.53 (1.75, 7.13) <0.001** 12 (13.0) 14 (15.2) 0.062

Posterior 80 (43.5) 32 (28.8) 0.53 (0.32, 0.87) 0.013* 34 (37.0) 31 (33.7) 0.068

Non-segmental 52 (28.3) 41 (36.9) 1.49 (0.90, 2.46) 0.121 31 (33.7) 33 (35.9) 0.046

Biomechanical Cage 125 (67.9) 93 (83.8) 2.44 (1.35, 4.41) 0.003* 67 (72.8) 74 (80.4) 0.181



Autograft Bone Substrate 73 (39.7) 31 (27.9) 0.59 (0.35, 0.98) 0.042* 29 (31.5) 31 (33.7) 0.046

Charlson-Elixhauser Comorbidity

Index

< 0 42 (22.8) 26 (23.4) 0.83 (0.44, 1.54) 0.464 25 (27.2) 20 (21.7) 0.129

0 60 (32.6) 45 (40.5) Reference

33 (35.9) 36 (39.1)

1-2 58 (31.5) 27 (24.3) 0.62 (0.34, 1.13)

25 (27.2) 26 (28.3)

≥ 3 24 (13.0) 13 (11.7) 0.72 (0.33, 1.57)

9 (9.8) 10 (10.9)

123



Cohort

BMP Use

BMP Use


Year of Procedure

2008 101 (54.9) 55 (49.5) Reference 0.374 40 (43.5) 47 (51.1) 0.112

2009 83 (45.1) 56 (50.5) 1.24 (0.77, 1.99)

52 (56.5) 45 (48.9)

Spinal Conditions


Osteoporosis 1 (0.5) 0 (0.0) 0.00 (0.00,0.00) 0.987 0 (0.0) 0 (0.0) 0.000


*: p value less than 0.05, **: p value less than 0.001

124

Table 4-11. Baseline characteristics of readmission risk analysis population (primary diagnosis definition)


Cohort

BMP Use

BMP Use


No

(n = 1095,

66.6%)

Yes

(n=549,

33.4%)

OR (95% CI) p value

No

(n = 540,

50.0%)

Yes

(n=540,

50.0%)

SMD

Age

66 - 70 years 452 (41.3) 252 (45.9) Reference 0.034* 250 (46.3) 247 (45.7) 0.016

71 -75 years 343 (31.3) 183 (33.3) 0.96 (0.76, 1.21)

178 (33.0) 180 (33.3)

76 -80 years 214 (19.5) 83 (15.1) 0.70 (0.52, 0.94)

80 (14.8) 82 (15.2)

Over 80 years 86 (7.9) 31 (5.6) 0.65 (0.42, 1.00)

32 (5.9) 31 (5.7)

Female Sex 612 (55.9) 310 (56.5) 1.02 (0.83, 1.26) 0.824 306 (56.7) 306 (56.7)

Race

White 1041 (95.1) 522 (95.1) Reference 0.411 513 (95.0) 514 (95.2) 0.010

Black 31 (2.8) 21 (3.8) 1.35 (0.77, 2.37)

21 (3.9) 20 (3.7)

Other 22 (2.0) 6 (1.1) 0.54 (0.22, 1.35)

6 (1.1) 6 (1.1)

Missing 1 (0.1) 0 (0) 0.00 (0.00, 0.00)

0 (0.0) 0 (0.0)

Geographical Region


Northeast 78 (7.1) 35 (6.4) 0.83 (0.53, 1.29)

36 (6.7) 34 (6.3)

South 508 (46.4) 239 (43.5) 0.87 (0.67, 1.12)

225 (41.7) 235 (43.5)

West 244 (22.3) 131 (23.9) 0.99 (0.74, 1.33)

129 (23.9) 129 (23.9)

Surgical Approach


Posterior 892 (81.5) 395 (71.9) 0.39 (0.28, 0.53)

415 (76.9) 395 (73.1)


41 (7.6) 51 (9.4)

125



Cohort

BMP Use

BMP Use


Multiple Level Procedure 613 (56.0) 256 (46.6) 0.69 (0.56, 0.84) <0.001* 252 (46.7) 256 (47.4)



Discectomy 593 (54.2) 336 (61.2) 1.34 (1.08, 1.65) 0.007* 321 (59.4) 331 (61.3) 0.038

Laminectomy 640 (58.4) 262 (47.7) 0.65 (0.53, 0.80) <0.001* 276 (51.1) 261 (48.3) 0.056


Anterior 81 (7.4) 81 (14.8) 2.17 (1.56, 3.00) <0.001* 60 (11.1) 76 (14.1) 0.089

Posterior 507 (46.3) 204 (37.2) 0.69 (0.56, 0.85) <0.001* 217 (40.2) 204 (37.8) 0.049

Non-segmental 359 (32.8) 221 (40.3) 1.38 (1.12, 1.71) 0.003* 219 (40.6) 218 (40.4) 0.004


Year of Procedure

2007 126 (11.5) 78 (14.2) 1.23 (0.88, 1.73) 0.394 81 (15.0) 76 (14.1) 0.029

2008 321 (29.3) 161 (29.3) Reference

162 (30.0) 161 (29.8)

2009 310 (28.3) 155 (28.2) 1.00 (0.76, 1.31)

148 (27.4) 151 (28.0)

2010 338 (30.9) 155 (28.2) 0.91 (0.70, 1.20)

149 (27.6) 152 (28.1)

Charlson-Elixhauser

Comorbidity Index

< 0 227 (20.7) 114 (20.8) 0.88 (0.66, 1.17) 0.303 107 (19.8) 113 (20.9) 0.036

0 353 (32.2) 201 (36.6) Reference

206 (38.1) 198 (36.7)

1-2 346 (31.6) 158 (28.8) 0.80 (0.62, 1.04)

155 (28.7) 155 (28.7)

≥ 3 169 (15.4) 76 (13.8) 0.79 (0.57, 1.09) 72 (13.3) 74 (13.7)

126



Cohort

BMP Use

BMP Use



Allograft Bone Substrate 282 (25.8) 180 (32.8) 1.41 (1.12, 1.76) 0.003* 177 (32.8) 172 (31.9) 0.020



Claim

Back Pain 176 (16.1) 80 (14.6) 0.89 (0.67, 1.19) 0.429 89 (16.5) 78 (14.4) 0.056

Herniated Disc 142 (13.0) 74 (13.5) 1.05 (0.77, 1.41) 0.771 67 (12.4) 72 (13.3) 0.028

Stenosis 647 (59.1) 295 (53.7) 0.80 (0.65, 0.99) 0.039* 294 (54.4) 292 (54.1) 0.007

Listhesis 254 (23.2) 123 (22.4) 0.96 (0.75, 1.22) 0.720 125 (23.1) 122 (22.6) 0.013

Scoliosis 51 (4.7) 29 (5.3) 1.14 (0.72, 1.82) 0.579 24 (4.4) 29 (5.4) 0.043

Spondylopathy 8 (0.7) 3 (0.5) 0.75 (0.20, 2.83) 0.667 1 (0.2) 3 (0.6) 0.061

Osteoporosis 69 (6.3) 37 (6.7) 1.07 (0.71, 1.62) 0.733 46 (8.5) 34 (6.3) 0.085



127

Table 4-12. Baseline characteristics of readmission risk analysis nested population (primary diagnosis definition)


BMP Use

BMP Use


No

(n = 585,

65.9%)

Yes

(n=304,

34.1%)

OR (95% CI) p value

No

(n = 289,

50.0%)

Yes

(n=289,

50.0%)

SMD

Age

66 - 70 years 223 (38.1) 140 (46.1) Reference 0.008* 136 (47.1) 130 (45.0) 0.114

71 -75 years 190 (32.5) 106 (34.9) 0.89 (0.65, 1.22)

96 (33.2) 103 (35.6)

76 -80 years 128 (21.9) 46 (15.1) 0.57 (0.38, 0.85)

39 (13.5) 44 (15.2)

Over 80 years 44 (7.5) 12 (3.9) 0.43 (0.22, 0.85)

18 (6.2) 12 (4.2)

Female Sex 338 (57.8) 160 (52.6) 0.81 (0.61, 1.07) 0.143 156 (54.0) 153 (52.9)

Race

White 550 (94.0) 296 (97.4) Reference 0.210 282 (97.6) 281 (97.2) 0.099

Black 20 (3.4) 6 (2.0) 0.56 (0.22, 1.40)

5 (1.7) 6 (2.1)

Other 14 (2.4) 2 (0.7) 0.27 (0.06, 1.18)

1 (0.3) 2 (0.7)

Missing 1 (0.2) 0 (0.0) 0.00 (0.00, 0.00)

1 (0.3) 0 (0.0)

Geographical Region


Northeast 48 (8.2) 19 (6.3) 0.63 (0.34, 1.14)

19 (6.6) 19 (6.6)

South 282 (48.2) 135 (44.4) 0.76 (0.54, 1.07)

141 (48.8) 128 (44.3)

West 122 (20.9) 66 (21.7) 0.86 (0.57, 1.28)

62 (21.5) 66 (22.8)

Surgical Approach


Posterior 475 (81.2) 215 (70.7) 0.32 (0.21, 0.49) 220 (76.1) 213 (73.7)

Circumferential 66 (11.3) 27 (8.9) 0.29 (0.16, 0.52) 28 (9.7) 27 (9.3)

128



BMP Use

BMP Use



Discectomy 318 (54.4) 186 (61.2) 1.32 (1.00, 1.76) 0.052 174 (60.2) 173 (59.9) -0.007

Laminectomy 341 (58.3) 139 (45.7) 0.60 (0.46, 0.80) <0.001* 145 (50.2) 138 (47.8) -0.048

Multiple Level Procedure

Indicator 336 (57.4) 144 (47.4) 0.67 (0.50, 0.88) 0.004* 150 (51.9) 140 (48.4)



Anterior 40 (6.8) 49 (16.1) 2.62 (1.68, 4.08) <0.001* 34 (11.8) 37 (12.8) 0.032

Posterior 269 (46.0) 109 (35.9) 0.66 (0.49, 0.87) 0.004* 114 (39.4) 109 (37.7) 0.036

Non-segmental 183 (31.3) 121 (39.8) 1.45 (1.09, 1.94) 0.011* 119 (41.2) 118 (40.8) 0.007


Year of Procedure

2008 304 (52.0) 155 (51.0) Reference 0.782 152 (52.6) 150 (51.9) 0.007

2009 281 (48.0) 149 (49.0) 1.04 (0.79, 1.37)

137 (47.4) 139 (48.1)

Charlson-Elixhauser

Comorbidity Index

< 0 113 (19.3) 51 (16.8) 0.70 (0.47, 1.05) 0.134 44 (15.2) 50 (17.3) 0.084

0 196 (33.5) 126 (41.4) Reference 114 (39.4) 119 (41.2)

1-2 192 (32.8) 86 (28.3) 0.70 (0.50, 0.98) 88 (30.4) 82 (28.4)

≥ 3 84 (14.4) 41 (13.5) 0.76 (0.49, 1.17) 43 (14.9) 38 (13.1)

129



BMP Use

BMP Use





Other Spinal Conditions

Back Pain 103 (17.6) 40 (13.2) 0.71 (0.48, 1.05) 0.088 52 (18.0) 38 (13.1) 0.134

Herniated Disc 65 (11.1) 42 (13.8) 1.28 (0.85, 1.94) 0.241 43 (14.9) 40 (13.8) 0.030

Stenosis 345 (59.0) 159 (52.3) 0.76 (0.58, 1.01) 0.057 154 (53.3) 154 (53.3) 0.000

Listhesis 137 (23.4) 63 (20.7) 0.85 (0.61, 1.20) 0.362 66 (22.8) 60 (20.8) 0.050

Scoliosis 28 (4.8) 20 (6.6) 1.40 (0.78, 2.53) 0.264 21 (7.3) 17 (5.9) 0.056

Spondylopathy 4 (0.7) 1 (0.3) 0.48 (0.05, 4.31) 0.512 0 (0.0) 1 (0.3) 0.083

Osteoporosis 44 (7.5) 15 (4.9) 0.64 (0.35, 1.17) 0.145 14 (4.8) 15 (5.2) 0.016



130

Table 4-13. Baseline characteristics of readmission risk analysis population (comprehensive case definition)


Cohort

BMP Use

BMP Use


No

(n = 2172,

67.4%)

Yes

(n=1054,

32.6%)

OR (95% CI) p value

No

(n =1044,

50.0%)

Yes

(n=1044,

50.0%)

SMD

Age

66 - 70 years 890 (41.0) 460 (43.6) Reference 0.010* 451 (43.2) 456 (43.7) 0.026

71 -75 years 689 (31.7) 359 (34.1) 1.01 (0.85, 1.20)

362 (34.7) 353 (33.8)

76 -80 years 405 (18.6) 173 (16.4) 0.83 (0.67, 1.02)

166 (15.9) 173 (16.6)

Over 80 years 188 (8.7) 62 (5.9) 0.64 (0.47, 0.87)

65 (6.2) 62 (5.9)

Female Sex 1219 (56.1) 590 (56.0) 0.99 (0.86, 1.15) 0.937 588 (56.3) 586 (56.1)

Race

White 2057 (94.7) 998 (94.7) Reference 0.704 991 (94.9) 988 (94.6) 0.053

Black 64 (2.9) 37 (3.5) 1.19 (0.79, 1.80)

37 (3.5) 37 (3.5)

Other 49 (2.3) 19 (1.8) 0.80 (0.47, 1.36)

15 (1.4) 19 (1.8)

Missing 2 (0.1) 0 (0.0) 0.00 (0.00, 0.00)

1 (0.1) 0 (0.0)

Geographical Region


Northeast 197 (9.1) 66 (6.3) 0.60 (0.44, 0.82)

69 (6.6) 66 (6.3)

South 1001 (46.1) 463 (43.9) 0.83 (0.69, 0.99)

456 (43.7) 461 (44.2)

West 464 (21.4) 240 (22.8) 0.93 (0.75, 1.15)

243 (23.3) 235 (22.5)


Discectomy 1191 (54.8) 638 (60.5) 1.26 (1.09, 1.47) 0.002* 624 (59.8) 631 (60.4) 0.014

Laminectomy 1359 (62.6) 559 (53.0) 0.68 (0.58, 0.78) <0.001** 564 (54.0) 559 (53.5) 0.010

131



Cohort

BMP Use

BMP Use


Surgical Approach


Posterior 1856 (85.5) 839 (79.6) 0.41 (0.32, 0.53)

857 (82.1) 838 (80.3)


70 (6.7) 80 (7.7)


Anterior 118 (5.4) 101 (9.6) 1.84 (1.40, 2.43) <0.001* 76 (7.3) 93 (8.9) 0.060

Posterior 1021 (47.0) 449 (42.6) 0.84 (0.72, 0.97) 0.018* 449 (43.0) 449 (43.0) 0.000

Non-segmental 732 (33.7) 398 (37.8) 1.19 (1.02, 1.39) 0.023* 411 (39.4) 397 (38.0) 0.028



Allograft Bone Substrate 581 (26.7) 327 (31.0) 1.23 (1.05, 1.45) 0.011* 311 (29.8) 321 (30.7) 0.021

Autograft Bone Substrate 990 (45.6) 440 (41.7) 0.86 (0.74, 0.99) 0.040* 454 (43.5) 439 (42.0) 0.029

Year of Procedure

2007 255 (11.7) 135 (12.8) 0.98 (0.76, 1.26) 0.089 139 (13.3) 129 (12.4) 0.042

2008 573 (26.4) 310 (29.4) Reference

306 (29.3) 308 (29.5)

2009 619 (28.5) 299 (28.4) 0.89 (0.73, 1.09)

282 (27.0) 298 (28.5)

2010 725 (33.4) 310 (29.4) 0.79 (0.65, 0.96)

317 (30.4) 309 (29.6)

Charlson-Elixhauser Index

< 0 431 (19.8) 214 (20.3) 0.97 (0.79, 1.19) 0.604 210 (20.1) 214 (20.5) 0.046

0 730 (33.6) 375 (35.6) Reference

384 (36.8) 369 (35.3)

1-2 698 (32.1) 324 (30.7) 0.90 (0.75, 1.08)

325 (31.1) 322 (30.8)

≥ 3 313 (14.4) 141 (13.4) 0.88 (0.69, 1.11)

125 (12.0) 139 (13.3)

132



Cohort

BMP Use

BMP Use


Multiple Level Procedure Indicator 1245 (57.3) 570 (54.1) 0.88 (0.76, 1.02) 0.082 555 (53.2) 565 (54.1)




Herniated Disc 378 (17.4) 176 (16.7) 0.95 (0.78, 1.16) 0.621 175 (16.8) 176 (16.9) 0.003

Stenosis 1530 (70.4) 706 (67.0) 0.85 (0.73, 1.00) 0.046* 700 (67.0) 700 (67.0) 0.000

Listhesis 678 (31.2) 321 (30.5) 0.97 (0.82, 1.13) 0.662 320 (30.7) 319 (30.6) 0.002

Scoliosis 130 (6.0) 79 (7.5) 1.27 (0.95, 1.70) 0.103 74 (7.1) 77 (7.4) 0.011

Osteoporosis 1 (0.0) 0 (0.0) 0.00 (0.00, 0.00) 0.971 0 (0.0) 0 (0.0) 0.000



133

Table 4-14. Baseline characteristics of readmission risk analysis nested population (comprehensive case definition)


Cohort

BMP Use

BMP Use


No

(n=1126,

66.1%)

Yes

(n=578, 33.9%) OR (95% CI) p value

No

(n = 571,

50.0%)

Yes

(n=571,

50.0%)

SMD

Age

66 - 70 years 433 (38.5) 244 (42.2) Reference 0.005* 243 (42.6) 241 (42.2) 0.011

71 -75 years 362 (32.1) 208 (36.0) 1.02 (0.81, 1.29)

201 (35.2) 204 (35.7)

76 -80 years 233 (20.7) 97 (16.8) 0.74 (0.56, 0.98)

98 (17.2) 97 (17.0)

Over 80 years 98 (8.7) 29 (5.0) 0.53 (0.34, 0.82)

29 (5.1) 29 (5.1)

Female Sex 639 (56.7) 306 (52.9) 0.86 (0.70, 1.05) 0.134 300 (52.5) 303 (53.1)

Race

White 1064 (94.5) 558 (96.5) Reference 0.294 552 (96.7) 551 (96.5) 0.012

Black 33 (2.9) 13 (2.2) 0.75 (0.39, 1.44)

12 (2.1) 13 (2.3)

Other 28 (2.5) 7 (1.2) 0.48 (0.21, 1.10)

7 (1.2) 7 (1.2)

Missing 1 (0.1) 0 (0.0) 0.00 (0.00, 0.00)

0 (0.0) 0 (0.0)

Geographical Region


Northeast 108 (9.6) 37 (6.4) 0.54 (0.36, 0.83)

43 (7.5) 37 (6.5)

South 534 (47.4) 256 (44.3) 0.76 (0.59, 0.98)

263 (46.1) 254 (44.5)

West 233 (20.7) 127 (22.0) 0.87 (0.65, 1.16)

120 (21.0) 125 (21.9)

Surgical Approach


Posterior 964 (85.6) 458 (79.2) 0.36 (0.25, 0.51)

473 (82.8) 458 (80.2)


41 (7.2) 41 (7.2)

134



Cohort

BMP Use

BMP Use


Surgical Approach


Posterior 964 (85.6) 458 (79.2) 0.36 (0.25, 0.51)

473 (82.8) 458 (80.2)


41 (7.2) 41 (7.2)


Discectomy 624 (55.4) 348 (60.2) 1.22 (0.99, 1.49) 0.059 326 (57.1) 342 (59.9) 0.057

Laminectomy 710 (63.1) 297 (51.4) 0.62 (0.51, 0.76) <0.001** 305 (53.4) 297 (52.0)

Multiple Level Procedure 667 (59.2) 310 (53.6) 0.80 (0.65, 0.97) 0.027* 317 (55.5) 307 (53.8) 0.000

Revision Procedure 49 (4.4) 13 (2.2) 0.51 (0.27, 0.94) 0.031* 13 (2.3) 13 (2.3) 0.000


Anterior 61 (5.4) 58 (10.0) 1.95 (1.34, 2.83) <0.001** 43 (7.5) 53 (9.3) 0.063

Posterior 544 (48.3) 246 (42.6) 0.79 (0.65, 0.97) 0.024* 262 (45.9) 246 (43.1) 0.056

Non-segmental 359 (31.9) 220 (38.1) 1.31 (1.06, 1.62) 0.011* 213 (37.3) 218 (38.2) 0.018

Biomechanical Cage 688 (61.1) 453 (78.4) 2.31 (1.83, 2.91) <0.001** 453 (79.3) 446 (78.1) 0.030


Allograft Bone 311 (27.6) 175 (30.3) 1.14 (0.91, 1.42) 0.250 167 (29.2) 170 (29.8) 0.012

Autograft Bone 531 (47.2) 231 (40.0) 0.75 (0.61, 0.91) 0.005* 239 (41.9) 231 (40.5) 0.028

Year of Procedure

2008 547 (48.6) 290 (50.2) Reference 0.533 300 (52.5) 287 (50.3) 0.057

2009 579 (51.4) 288 (49.8) 0.94 (0.77, 1.15)

271 (47.5) 284 (49.7)

135



Cohort

BMP Use

BMP Use


Charlson-Elixhauser

Comorbidity Index

< 0 209 (18.6) 101 (17.5) 0.83 (0.62, 1.11) 0.312 96 (16.8) 101 (17.7) 0.035

0 392 (34.8) 228 (39.4) Reference

229 (40.1) 224 (39.2)

1-2 371 (32.9) 175 (30.3) 0.81 (0.64, 1.03)

177 (31.0) 173 (30.3)

≥ 3 154 (13.7) 74 (12.8) 0.83 (0.60, 1.14)

69 (12.1) 73 (12.8)



Herniated Disc 186 (16.5) 99 (17.1) 1.04 (0.80, 1.37) 0.749 94 (16.5) 99 (17.3) 0.023

Stenosis 791 (70.2) 381 (65.9) 0.82 (0.66, 1.01) 0.068 371 (65.0) 376 (65.8) 0.018

Listhesis 349 (31.0) 169 (29.2) 0.92 (0.74, 1.15) 0.456 172 (30.1) 168 (29.4) 0.015

Scoliosis 68 (6.0) 47 (8.1) 1.38 (0.94, 2.03) 0.104 44 (7.7) 45 (7.9) 0.007

Osteoporosis 5 (0.4) 3 (0.5) 1.17 (0.28, 4.91) 0.830 2 (0.4) 3 (0.5) 0.027



136

Table 4-15. rhBMP-30 day readmission rate summary results

Procedure Indication

Identification Method Analytical Model

rhBMP-Exposed Controls

OR (95% CI) p

value (No. Readmitted

/ Total)

(No.

Readmitted /

Total)

Hierarchical Algorithm

Unadjusted 17/203 30/348

0.97 (0.52, 1.80) 0.92

Age and Sex Adjusted 1.03 (0.54, 1.94) 0.93

Propensity Score Adjusted 12/176 14/176 0.87 (0.39, 1.96) 0.94

LDDD Diagnostic Code

listed as Primary

Diagnosis

Unadjusted 57/549 87/1095

1.34 (0.95, 1.91) 0.10



LDDD Diagnosis listed at

any position on the

Procedure Claim

Unadjusted 102/1054 181/2172

1.18 (0.91, 1.52) 0.21



137

Table 4-16. Association between rhBMP use and time to the first LDDD-related readmission analysis summary results


Identification Method Analytical Model

LDDD-related Readmissions

per 1000 Person Years of

Follow-Up HR (95% CI)

p

value

rhBMP Exposed Controls


Cause-Specific Hazard

Regression 1.1 2.6 0.44 (0.11, 1.70) 0.23

Fine and Gray Regression 0.44 (0.11, 1.74) 0.24

LDDD Diagnostic Code listed

as Primary Diagnosis


Regression 1.5 2.1 0.76 (0.36, 1.61) 0.47


LDDD Diagnosis listed at any

position on the Procedure Claim


Regression 1.3 1.6 0.85 (0.47, 1.53) 0.58


Table 4-17. Association between rhBMP use and the number of LDDD-related readmissions summary results

Procedure Indication Identification Method

rhBMP Exposed Controls

IRR (95% CI) p value (No. Readmitted /

Total)

(No. Readmitted

/ Total)

Hierarchical Algorithm Cohort 2 / 92 1 / 92 1.57 (0.22, 11.4) 0.65

Primary Diagnosis Definition Cohort 6 / 289 4 / 289 1.43 (0.42, 4.79) 0.57

Comprehensive Definition Cohort 4 / 571 7 / 571 1.15 (0.48, 2.81) 0.76

†: All the patients analyzed had only one LDDD-related readmission during the first year of follow-up.

138


Similar in structure to our analyses of inpatient service utilization patterns, this section of

the dissertation assessed whether the use of rhBMPs during lumbar fusion procedures was

associated with fewer LDDD-related Emergency Room (ER) visits in the year following the

procedure (Research Question 2c).


A detailed description of the study population used in this series of analyses is presented

in part IIB (inpatient services section) of this chapter.

Analytical results

Figures 4-5, 4-6 and 4-7 show the distribution of LDDD-related ER visits incurred during

the first year following the surgery stratified by the method used to identify LDDD-indicated

fusion procedures. Only 7 (3.8%) of the 184 patients in the nested propensity score matched

cohort, defined using the hierarchical indication algorithm, received LDDD-specific ER services

during follow-up. Although a larger proportion of patients in the rhBMP group visited the ER

for LDDD related complaints during the outcome evaluation year (n=5 (5.4%) vs n=2 (2.2%)),

we found no statistical evidence to link rhBMP use to the number of LDDD-related ER visits

during this time frame (IRR (95% CI): 3.25 (0.51, 20.5), p value = 0.21).

The conclusions of this main analysis were robust to changes in the method used to

identify the indication for the lumbar fusion procedure (Primary Diagnosis Definition Cohort

IRR (95% CI): 0.81 (0.41, 1.63), p value = 0.57; Comprehensive Definition Cohort IRR (95%

CI): 1.14 (0.65, 1.98), p value = 0.65).

Summary

The results to this research question appear in Table 4-18. Less than 4% of the

139

population analyzed incurred a LDDD-related ER visits during the first year of follow-up.

Notably LDDD-specific diagnostic codes (ICD-9-CM: 722.52, 722.6) are seldom used in ER

visit claims instead the outcome visits we identified were based on our alternate definition: non-

specific low back pain complaints in the absence of an injury code. More specifically, none of

the nine ER visits in the hierarchical algorithm cohort, two of the 34 ER visits in the primary

diagnosis cohort and two of the 53 ER visits in the comprehensive definition cohorts included an

LDDD-specific code in the encounter claim. We found no evidence to suggest that the use of

rhBMPs was associated with the number of LDDD-related ER visits during the first year of

follow-up, irrespective of the method used to identify LDDD-indicated procedure.

140

Tables and figures

Table 4-18. Association between rhBMP use and the number of LDDD-related ER visits

summary results

Procedure Indication Identification

Method

rhBMP

Exposed Controls

IRR (95% CI) p

value (ER

Visitors /

Total)

(ER Visitors

/ Total)

Hierarchical Algorithm Cohort 5 / 92 2 / 92 2.65 (0.51, 13.7) 0.25

Primary Diagnosis Definition Cohort 15 / 289 18 / 289 0.77 (0.32, 1.87) 0.56

Comprehensive Definition Cohort 27 / 571 26 / 571 0.97 (0.50, 1.88) 0.94

141

Figure 4-5. Distribution of the number of LDDD-related ER visits during the first year post-procedure (hierarchical algorithm cohort)

142

Figure 4-6. Distribution of the number of LDDD-related ER visits during the first year post-procedure (primary diagnosis definition

cohort)

143

Figure 4-7. Distribution of the number of LDDD-related ER visits during the first year post-procedure (comprehensive definition

cohort)

144


This section of the dissertation investigated whether the use of rhBMP-augmented fusion

procedures was associated with greater changes in the acquisition of opioid analgesics than non-

rhBMP-augmented fusion surgeries (Research Question 2d1

and Research Question 2d2).


We identified 5,506 patients, aged 21 years and older, who had received an LDDD-

indicated spinal fusion procedure between 2007 and 2009. Out of these, we excluded 1,713

patients who had less than six months of continuous enrollment in a FFS plan prior to the index

procedure, 418 patients who had any cancer related health care encounters during this baseline

ascertainment window, 54 patients who received a concurrent fusion procedure at another region

of the spine, 2 patients who were exposed to rhBMPs during the previous 6 months, 126 fusion

procedures that involved the dislocation or fracture of the spinal vertebrae, 3 fusion procedure

claims that listed spinal cord injury as a contributing diagnosis, 207 patients with congenital

spinal abnormalities and 2,253 patients with less than 12 months of follow up (Figure 4-8). An

additional 1,376 (63.4%) patients who had not filled any prescriptions for opioid analgesics in

the three months prior to the index fusion procedure were also excluded.

Patient and procedure characteristics of the 796 patients who met our study eligibility

criteria appear in Table 4-19. The population was mainly female (59.6%), privately insured

(54.4%) and under the age of 65 (63.3%). We observed statistically significant differences

between rhBMP-users and non-users in three of the 25 characteristics evaluated. All 159

patients who received an rhBMP-augmented procedure were matched to controls yielding a

propensity score matched cohort of 318 patients. As shown in Table 4-19, the application of 1:1

propensity score optimal matching with caliper width restriction was able to balance the

145

distribution of these patient and procedure characteristics between the rhBMP-exposed and

unexposed groups (all absolute SMD values ≤ 0.2).

Analytical results

Table 4-20 summarizes the relationship between the estimated quantities of opioid

analgesics accessed at baseline and key patient and procedure characteristics. Among the 796

patients who met our eligibility criteria, 181 (22.7%) patients accessed less than 30mg OMEUs

daily, 256 (32.2%) patients accessed between 31mg and 60mg OMEUs daily, 178 (22.4%)

patients accessed between 61mg and 120 mg OMEUs daily and 181 (22.7%) patients accessed

more than 120mg OMEUs daily. As shown in Figure 4-9 and Table 4-21, the distribution of the

estimated daily dose of opioid analgesics accessed at baseline was right skewed among the 796

patients who met our eligibility criteria (Median: 60mg, Mean:141.4mg)

Although the association between the amounts of opioid analgesics accessed during the

baseline assessment window and the use of the rhBMPs during the index lumbar fusion

procedure was not statistically significant (p = 0.90), we observed that a higher proportion of

patients (N=40, 22.1%) in the low and in the very high opioid acquisition groups (N=40, 22.1%)

received the osteobiologic compared to patients in the medium (N=48, 18.8%) and high (N=31,

17.4%) access groups.

Only two of the 19 patient and procedure characteristics tested were associated with the

amounts of opioid analgesics accessed during the baseline assessment window (Table 4-20).

These associations, which were tested using the Kruskal-Wallis test, found that only the patient’s

age and the procedure’s intent were statistically linked to the opioid access rate during the three

months prior to the index procedure. Approximately one in five (N=38, 21.0%) patients who

accessed less than 30mg OMEUs daily were over the age of 65 compared to only 9.4% (N=17)

146

of the patients in the over 120mg OMEUs group (p value = 0.026). In terms of fusion intent, only

one procedure in the low opioid utilization group (N=1, 0.6%) was coded as a revision compared

to 14 (5.5 %) procedures in the moderate opioid utilization group, one procedure (0.6%) in the

high opioid utilization group and 14 (7.7%) procedures in very high opioid utilization group (p

value = 0.02).

We used the interquartile range (IQR) method to identify 33 patients whose opioid excess

rates were statistical outliers and 62 patients whose opioid excess rates were statistical extremes.

The 62 patients who accessed more than 336mg of OMEUs daily at baseline, otherwise known

as the extremes, were more likely to undergo a complex fusion procedure than their typical range

comparators. These complex fusion surgeries were marked by the multiplicity of levels involved,

a circumferential approach and the revision intent of the procedure (Table 4-22). The extremes

group was also significantly younger with only 4.8% of the population over the age of 65

compared to the 17.8% of the typical range group who were over the age of 65. Between the

patients with statistically typical opioid access rates and the extremes were the 33 outliers who

accessed between 229mg and 326mg OMEUs daily during the baseline ascertainment period.

Overall, the outliers were statistically similar to the typical opioid range comparators in all the

key patient and procedure characteristics analyzed (all p values > 0.05, Table 4-23).

Overall, 71 (22.3%) of the 318 patients in the matched cohort did not access opioid

analgesics during the first post-procedure observation window. Users of the osteobiologic

discontinued opioid therapy at rates that were statistically comparable to the controls. Out of

these, 34 belonged to the rhBMP-exposed group and 37 were in the control group yielding an

odds ratio of 0.92 (95% CI: 0.54, 1.56), p value = 0.74). On the other hand, a higher proportion

of the rhBMP-exposed group appeared to have discontinued opioid therapy during the second

147

assessment window compared to the control group (40.3% (n=64) vs 32.1% (n=51)); however

the effect of the osteobiologic on the acquisition of opioid analgesics during this second post-

procedure observation window was not statistically significant (OR (95% CI): 1.40 (0.88, 2.23),

p value = 0.15). Notably, 24 (7.6%) patients who did not access opioid therapies during the 3-6

month post-procedure window filled out at least one opioid analgesic prescription during the 9-

12 month post-procedure evaluation timeframe.

Majority of the patients accessed fewer OMEUs during both the first and second outcome

ascertainment windows than they did at baseline. More specifically, 183 (57.6%) patients

accessed fewer OMEUs during the first post-procedure observation window than during the

baseline assessment period, 29 (9.1%) patients recorded no changes in their opioid analgesic

refill patterns while the remaining 106 (33.3%) patients increased their opioid access rate.

Patients in the rhBMP-exposed group recorded larger decreases in the OMEUs accessed (-28

(SD = ±211.6) vs. -19.8 (SD = ±181.6) but this arithmetic difference was not statistically

significant (T-Test p value = 0.71). The mean decrease in the OMEUs accessed, adjusted for the

propensity score as provided by the ANCOVA model, is comparable to the arithmetic mean

(rhBMP-exposed= -28.4, Controls: -19.5). Similarly, the association between rhBMP use and

changes in OMEUs accessed remained statistically insignificant even after adjusting for the

effect of potential confounders (p value= 0.69).

Likewise, a significant proportion of patients (n= 218, 68.6%) acquired fewer OMEUs

during the second post-procedure than in the three months prior to the index procedure. Out of

the remaining 100 patients, 77 (24.2%) acquired higher opioid analgesic doses during the second

assessment window than at baseline while 23 (7.2%) patients recorded no dose changes between

the two assessment windows. Overall, the arithmetic mean difference between amounts accessed

148

at baseline and during the 9-12 month window following the index fusion procedure was a

decrease of 49.6 OMEUs (SD = ±234.1), with rhBMP-exposed group recording a marginally

larger mean change (-49.6 (SD = ±234.1)) than the control group (-43.6 (SD = 144.4), p

value=0.78). Adjudicated using the ANCOVA model, the association between rhBMP use and

changes in the opioid access between baseline and the second post-procedure assessment

window was statistically non-significant (rhBMP-exposed= 48.4, Controls: -44.7, p value= 0.87).

Summary

Notably, more than half of the patients (63.4%) receiving LDDD-indicated lumbar fusion

procedures had not accessed opioid analgesics in the three months prior to the index operation

and were therefore excluded from this analysis. Our investigation also revealed that the opioid

access rate at baseline was not significantly associated with rhBMP use during the index fusion

procedure with most (32%) of the patients analyzed accessing less than 60mg of OMEUs daily

during the baseline assessment window (Table 4-20).

Table 4-24 summarizes the distribution of the opioid access rates across the different

assessment windows. The opioid access rates in the both the first and second outcome

assessment windows were markedly lower than the baseline rate as indicated by the decrease in

both the median and mean OMEUs between the three evaluation periods.

Apparent discontinuation of opioid analgesic therapy was common during both the first

(n=71, 22.3%) and second (n=115, 36.2%) post-procedure assessment window. Most patient

records also reflected a decrease in the amounts of OMEUs accessed during both 3-6 months and

the 9-12 post-procedure assessment windows. However we found no evidence to suggest that

rhBMP use during the index fusion procedure was associated with either the discontinuation of

opioid analgesic therapy (Baseline-First Outcome Window Discontinuation Rate: 21.4% vs.

149

23.3%, OR (95% CI): 0.92 (0.54, 1.56), p value = 0.74); Baseline-Second Outcome Window

Discontinuation Rate: 40.3% vs. 32.1%, OR (95% CI): 1.40 (0.88, 2.23), p value = 0.12) or with

changes in the estimated opioid analgesic doses accessed during follow-up (Baseline –First

Outcome Window Mean Difference: -28.4 vs. -19.5, p value= 0.69; Baseline-Second Outcome

Window Mean Difference: -48.4 vs. -44.7, p value= 0.87).

150

Tables and figures

Figure 4-8. Opioid use analyses study population creation flowchart

Patients, ≥ 21 years old, who received a lumbar fusion procedure

primarily for LDDD diagnosis

(N = 5,506)

==

==

Spinal Fusions with rhBMPs

(Cases)

(N=159)

Spinal Fusion without rhBMPs

(Potential Controls)

(N=637)

Exclusion Criteria

Less than 6 months of continuous

eligibility in a FFS plan (N = 1713)

Concurrent fusion at other regions of the

spine (N = 54)

Prior rhBMP Exposure (N = 2)


Spinal Cord Injury (N=3)

Congenital Spinal Abnormality (N= 207)

Less than 1 year of follow-up (N= 2253)

Cancer-related encounters during

baseline (N=418)

159 rhBMP-fusion cases were propensity

score matched with 159 controls

Study Source Population

(N= 2,172)

(Fusion with rhBMP: N=393 Fusion without rhBMP: N=1,780)

No Opioid Use during the

preoperative window (N= 1,376)

151

Table 4-19. Baseline characteristics of opioid access patterns analysis cohort



BMP Use

BMP Use

No

(n = 637,

91.9%)

Yes

(n=159,

18.1%)

OR (95% CI) p value

No

(n=159,

50.0%)

Yes

(n=159,

50.0%)

SMD

Age

21 - 45 years 228 (35.8) 39 (24.5) 0.84 (0.55, 1.29) <0.001** 38 (23.9) 39 (24.5) 0.066

46 - 65 years 329 (51.6) 67 (42.1) Reference

72 (45.3) 67 (42.1)

Over 65 years 80 (12.6) 53 (33.3) 3.25 (2.10, 5.03)

49 (30.8) 53 (33.3)

Female Sex 375 (58.9) 99 (62.3) 1.15 (0.81, 1.65) 0.436 94 (59.1) 99 (62.3) 0.064

Geographical Region


Northeast 50 (7.8) 13 (8.2) 1.02 (0.51, 2.03)

12 (7.5) 13 (8.2)

South 303 (47.6) 73 (45.9) 0.94 (0.62, 1.42)

73 (45.9) 73 (45.9)

West 104 (16.3) 27 (17.0) 1.02 (0.60, 1.73)

31 (19.5) 27 (17.0)

Insurance

Medicaid 21 (3.3) 5 (3.1) 14.2 (3.99, 50.2) <0.001** 4 (2.5) 5 (3.1) 0.114

Medicare 182 (28.6) 124 (78.0) 40.5 (17.5, 93.7)

124 (78.0) 124 (78.0)


5 (3.1) 6 (3.8)

Medicare + Medicaid 17 (2.7) 14 (8.8) 49.0 (16.8, 143)

12 (7.5) 14 (8.8)


14 (8.8) 10 (6.3)

Surgical Approach

Anterior 183 (28.7) 45 (28.3) Reference 0.279 38 (23.9) 45 (28.3) 0.104

Posterior 374 (58.7) 101 (63.5) 1.10 (0.74, 1.63)

106 (66.7) 101 (63.5)


15 (9.4) 13 (8.2)

152




BMP Use

Multiple Level Procedure 267 (41.9) 69 (43.4) 1.06 (0.75, 1.51) 0.735 74 (46.5) 69 (43.4)

Revision Procedure 24 (3.8) 6 (3.8) 1.00 (0.40, 2.49) 0.997 8 (5.0) 6 (3.8) 0.061


Discectomy 387 (60.8) 104 (65.4) 1.22 (0.85, 1.76) 0.281 97 (61.0) 104 (65.4) 0.091

Laminectomy 171 (26.8) 47 (29.6) 1.14 (0.78, 1.68) 0.492 49 (30.8) 47 (29.6) 0.027


Anterior 143 (22.4) 33 (20.8) 0.90 (0.59, 1.39) 0.645 29 (18.2) 33 (20.8) 0.064

Posterior 196 (30.8) 55 (34.6) 1.19 (0.82, 1.72) 0.354 58 (36.5) 55 (34.6) 0.039

Non-segmental 190 (29.8) 47 (29.6) 0.99 (0.67, 1.44) 0.947 47 (29.6) 47 (29.6) 0.000

Biomechanical Cage 483 (75.8) 131 (82.4) 1.49 (0.95, 2.33) 0.079 129 (81.1) 131 (82.4) 0.033




Year of Procedure

2007 159 (25.0) 39 (24.5) 0.89 (0.57, 1.39) 0.638 38 (23.9) 39 (24.5) 0.110

2008 232 (36.4) 64 (40.3) Reference

57 (35.8) 64 (40.3)

2009 246 (38.6) 56 (35.2) 0.83 (0.55, 1.23) 64 (40.3) 56 (35.2)



Spondylopathy 1 (0.2) 0 (0.0) 0.00 (0.00, 0.00) 0.985 0 (0.0) 0 (0.0) 0

Osteoporosis 0 (0.0) 0 (0.0) - - 0 (0.0) 0 (0.0) -

153



Index, Mean (SD)

0.42

(1.27)

0.69

(1.50) 1.15 (1.02, 1.29) 0.027*

0.67

(1.56)

0.69

(1.50) 0.008

Other Chronic Pain Conditions

Sickle Cell Disease 0 (0.0) 0 (0.0) - - 0 (0.0) 0 (0.0) -

Rheumatoid Arthritis 13 (2.0) 3 (1.9) 0.92 (0.26, 3.28) 0.902 3 (1.9) 3 (1.9) 0.000

Neuropathic Pain 56 (8.8) 18 (11.3) 1.32 (0.76, 2.32) 0.327 17 (10.7) 18 (11.3) 0.020

Migraines 42 (6.6) 7 (4.4) 0.65 (0.29, 1.48) 0.308 6 (3.8) 7 (4.4) 0.032

Fibromyalgia 66 (10.4) 16 (10.1) 0.97 (0.54, 1.72) 0.913 18 (11.3) 16 (10.1) 0.041

OR: Odds Ratio of BMP exposure, CI: Confidence Interval, SMD: Absolute Standardized Mean Difference, SD: Standard

Deviation


154

Table 4-20. Baseline characteristics of patients on opioid analgesic therapy prior to index

procedure, stratified by estimated Oral Morphine Units (OMEUs) accessed daily *

Characteristic, n (%) Low Medium High

Very

High p

value (n=181 ) (n=256) (n=178) (n=181)

rhBMP Used During

Procedure 40 (22.1) 48 (18.8) 31(17.4) 40(22.1) 0.903

Age

21-45 years 55 (30.4) 78 (30.5) 67 (37.6) 67 (37.0)

0.026*

46-65 years 88 (48.6) 135 (52.7) 76 (42.7) 97 (53.6)

Over 66 years 38 (21.0) 43 (16.8) 35 (19.7) 17 (9.4)

Female Sex 123 (68.0) 144 (56.3) 102 (57.3) 105 (58.0) 0.085

Geographic Region

Midwest 49 (27.1) 77 (30.1) 51 (28.7) 49 (27.1) 0.363

Northeast 14 (7.7) 20 (7.8) 10 (5.6) 19 (10.5)

South 97 (53.6) 111 (43.4) 90 (50.6) 78 (43.1)

West 21 (11.6) 48 (18.8) 27 (15.2) 35 (19.3)

Insurance Type

Medicaid 6 (3.3) 6 (2.3) 6 (3.4) 8 (4.4) 0.151

Medicare 65 (35.9) 94 (36.7) 68 (38.2) 79 (43.6)

Medicare +Medicaid 6 (3.3) 14 (5.5) 3 (1.7) 8 (4.4)

Commercial 89 (49.2) 124 (48.4) 85 (47.8) 65 (35.9)

Commercial +Medicare 15 (8.3) 18 (7.0) 16 (9.0) 21 (11.6)

Surgical Approach

Anterior 56 (30.9) 70 (27.3) 61 (34.3) 41 (22.7) 0.185

Posterior 111 (61.3) 151 (59.0) 102 (57.3) 111 (61.3)

Circumferential 14 (7.7) 35 (13.7) 15 (8.4) 29 (16.0)

Concurrent Surgical

Procedures

Discectomy 102 (56.4) 173 (67.6) 106 (59.6) 110 (60.8) 0.836

Laminectomy 53 (29.3) 65 (25.4) 52 (29.2) 48 (26.5) 0.811


Anterior 43 (23.8) 50 (19.5) 47 (26.4) 36 (19.9) 0.822

Posterior 53 (29.3) 83 (32.4) 47 (26.4) 68 (37.6) 0.265

Non-segmental 53 (29.3) 80 (31.3) 51 (28.7) 53 (29.3) 0.836

Biomechanical Cage 137 (75.7) 195 (76.2) 145 (81.5) 137 (75.7) 0.647

155


Characteristic, n (%) Low Medium High

Very

High p

value (n=181 ) (n=256) (n=178) (n=181)


Allograft Substrate 49 (27.1) 81 (31.6) 47 (26.4) 54 (29.8) 0.904

Autograft Substrate 48 (26.5) 101 (39.5) 55 (30.9) 59 (32.6) 0.627

Year of Surgery

2007 35 (19.3) 63 (24.6) 49 (27.5) 51 (28.2) 0.064

2008 74 (40.9) 100 (39.1) 65 (36.5) 57 (31.5)

2009 72 (39.8) 93 (36.3) 64 (36.0) 73 (40.3)

Charlson-Elixhauser

Comorbidity Score, Mean

(SD)

0.81 (1.86) 0.64 (1.65) 0.40 (1.17) 0.80 (1.39) 0.801


Claim

Non-specific Back Pain 33 (18.2) 52 (20.3) 44 (24.7) 44 (24.3) 0.089

Spondylopathy 1 (0.6) 0 (0.0) 0 (0.0) 0 (0.0) --

*: Opioid analgesic access subgroups were stratified as follows: low (≤ 30mg), medium

(31mg-60mg), high (61mg -120mg) and very high (>120mg).

156

Figure 4-9. Distribution of opioid analgesic access levels in the three months prior to the index procedure

157

Table 4-21. Distribution of opioid analgesic access levels in the three months prior to the index

procedure (summary statistics)

Statistic General Cohort

Propensity Score Matched

Cohort

rhBMP Exposure Status rhBMP Exposure Status

No Yes No Yes

Sample Size 637mg 159mg 159mg 159mg

Minimum 1mg 5mg 3mg 5mg

Lower Quartile 35mg 30mg 38mg 30mg

Median 60mg 60mg 63mg 60mg

Upper Quartile 110mg 135mg 140mg 135mg

Maximum 6000mg 3840mg 6000mg 3840mg

Outlier Access Range 222.5mg -

335mg 292.5mg - 450mg

293mg -

446mg

292.5mg -

450mg

Extreme Access Range > 335mg > 450mg > 446mg > 450mg

Mean 135.8mg 163.8mg 173.3mg 163.8mg

Standard Deviation 357.7mg 413.4mg

511.4mg 413.4mg

158

Table 4-22. Baseline characteristics of typical versus outlier range opioid access rate groups


Typical Range Outlier Range

p value n=701 n=33

OMEU = 1mg -

229mg

OMEU = 229mg -

346mg

rhBMP Used 139 (19.8) 6 (18.2) 0.816

Age

21-45 years 230 (32.8) 10 (30.3) 0.601

46-65 years 346 (49.4) 18 (54.5)

Over 65 years 125 (17.8) 5 (15.2)

Female Sex 420 (59.9) 22 (66.7) 0.439

Geographic Region

Midwest 199 (28.4) 8 (24.2) 0.404

Northeast 53 (7.6) 4 (12.1)

South 338 (48.2) 13 (39.4)

West 111 (15.8) 8 (24.2)

Insurance Type

Medicaid 22 (3.1) 1 (3.0) 0.338

Medicare 262 (37.4) 17 (51.5)

Commercial 328 (46.8) 11 (33.3)

Medicare +Medicaid 27 (3.9) 0 (0.0)

Commercial +Medicare 62 (8.8) 4 (12.1)

Surgical Approach

Anterior 207 (29.5) 10 (30.3) 0.955

Posterior 419 (59.8) 19 (57.6)

Circumferential 75 (10.7) 4 (12.1)

Concurrent Surgical Procedures

Discectomy 437 (62.3) 18 (54.5) 0.367

Laminectomy 194 (27.7) 9 (27.3) 0.960

Multi-Level Fusion Indicator 284 (40.5) 18 (54.5) 0.109

Refusion Procedure Indicator 20 (2.9) 2 (6.1) 0.291


Anterior 155 (22.1) 8 (24.2) 0.773

Posterior 213 (30.4) 13 (39.4) 0.273

Non-segmental 213 (30.4) 7 (21.2) 0.261

Biomechanical Cage 545 (77.7) 24 (72.7) 0.500

159


Characteristic, n (%) Typical Range Outlier Range p value


Allograft Substrate 198 (28.2) 10 (30.3) 0.798

Autograft Substrate 229 (32.7) 12 (36.4) 0.659

Year of Surgery

2007 172 (24.5) 9 (27.3) 0.820

2008 267 (38.1) 10 (30.3)

2009 262 (37.4) 14 (42.4)


Score, Mean (SD) 0.44 (1.31) 0.54 (1.42) 0.655


Non-specific Back Pain 155 (22.1) 6 (18.2) 0.594

Spondylopathy 1 (0.1) 0 (0.0) 0.828


160

Table 4-23. Baseline characteristics of typical versus extreme range opioid access rate groups


Typical Range Extreme Range

p value n=701 n= 62

OMEU=1mg - 229mg OMEU > 346mg

rhBMP Used 139 (19.8) 14 (22.6) 0.604

Age

21-45 years 230 (32.8) 27 (43.5) 0.012*

46-65 years 346 (49.4) 32 (51.6)

Over 65 years 125 (17.8) 3 (4.8)

Female Sex 420 (59.9) 32 (51.6) 0.202

Geographic Region

Midwest 199 (28.4) 19 (30.6) 0.656

Northeast 53 (7.6) 6 (9.7)

South 338 (48.2) 25 (40.3)

West 111 (15.8) 12 (19.4)

Insurance Type

Medicaid 22 (3.1) 3 (4.8) 0.524

Medicare 262 (37.4) 27 (43.5)

Commercial 328 (46.8) 24 (38.7)

Medicare + Medicaid 27 (3.9) 4 (6.5)

Commercial + Medicare 62 (8.8) 4 (6.5)

Surgical Approach

Anterior 207 (29.5) 11 (17.7) 0.008*

Posterior 419 (59.8) 37 (59.7)

Circumferential 75 (10.7) 14 (22.6)

Concurrent Surgical Procedures

Discectomy 437 (62.3) 36 (58.1) 0.506

Laminectomy 194 (27.7) 15 (24.2) 0.556

Multi-Level Fusion Indicator 284 (40.5) 34 (54.8) 0.028*

Refusion Procedure Indicator 20 (2.9) 8 (12.9) <0.001*


Anterior 155 (22.1) 13 (21.0) 0.835

Posterior 213 (30.4) 25 (40.3) 0.105

Non-segmental 213 (30.4) 17 (27.4) 0.626

161


Characteristic, n (%) Typical Range Extreme Range p value


Anterior 155 (22.1) 13 (21.0) 0.835

Posterior 213 (30.4) 25 (40.3) 0.105

Non-segmental 213 (30.4) 17 (27.4) 0.626

Biomechanical Cage 545 (77.7) 45 (72.6) 0.352


Allograft Substrate 198 (28.2) 23 (37.1) 0.141

Autograft Substrate 229 (32.7) 22 (35.5) 0.651

Year of Surgery

2007 172 (24.5) 17 (27.4) 0.510

2008 267 (38.1) 19 (30.6)

2009 262 (37.4) 26 (41.9)


Claim

Non-specific Back Pain 155 (22.1) 12 (19.4) 0.615

Spondylopathy 1 (0.1) 0 (0.0) 0.766


162

Figure 4-10. Distribution of opioid analgesic access levels during the three observation windows (propensity score matched cohort)

163

Table 4-24. Distribution of opioid analgesic access levels during the three observation windows (propensity score matched cohort

summary statistics)

Statistic

Baseline 3-6 months 9-12 months

rhBMP Exposure Status rhBMP Exposure Status rhBMP Exposure Status

No Yes No Yes No Yes

Sample Size 637mg 159mg 637mg 159mg 637mg 159mg

Minimum 1mg 5mg 0mg 0mg 0mg 0mg

Lower Quartile 38mg 30mg 10mg 10mg 0mg 0mg

Median 63mg 60mg 57mg 60mg 38mg 30mg

Upper Quartile 110mg 135mg 110mg 100mg 94mg 90mg

Maximum 6000mg 3840mg 6800mg 5040mg 6000mg 2880mg

Outlier Access Range

293mg -

446mg 292.5mg - 450mg

260mg -

410mg

235mg -

370mg 235mg - 376mg

225mg -

360mg

Extreme Access Range > 446mg > 450mg > 410mg > 370mg > 300mg > 360mg

Mean 173.3mg 163.8mg 145.3mg 144.0mg 129.8mg 114.2mg

Standard Deviation 511/4mg 413.4mg 558.3mg 477.4mg 493.1mg 356.0mg

164

Part III: Effect of Intraoperative rhBMP Use on Cancer Risk

This section of the dissertation examined the incidence of cancer diagnosis after spinal

fusion procedures and its relation to the use of intraoperative rhBMPs (Research Question 3a and

3b).


A total of 116,577 adult patients in the MPCD data extract underwent a spinal fusion

procedure between 2007 and 2010. More than a third of these index fusion procedures

(n=45028, 38.6%) were performed in the cervical spine. Figure 4-11 is a flowchart summarizing

the steps taken to create the study population. Overall, we excluded 14,930 (33.4%) cervical and

23,104 (31.9%) thoracolumbar fusion procedure patients from further analyses. Reasons for

exclusions were as follows: less than six months of continuous enrollment in a FFS plan prior to

the index procedure (n=24,458), presence of a cancer diagnostic code in the patient’s record

during baseline assessment window (n=13,984), concurrent fusion procedures performed at

multiple regions of the spine (n=1,091) and exposure to rhBMPs during the six months prior to

index procedure (n=5).

Notably, patients with preexisting cancer, as indicated by the presence of a cancer-related

diagnostic code in their record, were more likely to receive rhBMPs during their index fusion

surgery (14.2% vs. 11.4%, OR (95% CI): 1.23 (1.17, 1.30), p value <0.001). The association

between preexisting cancer and subsequent rhBMP use during the fusion procedure manifested

in both cervical and in thoracolumbar surgical cohorts (Cervical: 13.5% vs. 10.6%, OR (95%

CI): 1.32 (1.16, 1.50), p value <0.001; Thoracolumbar 14.3% vs. 11.9%, OR (95% CI): 1.23

(1.17, 1.30), p value <0.001).

165

Tables 4-25 and 4-26 present the baseline characteristics of the 78,866 patients who met

our eligibility criteria. Cervical spinal fusion procedures were less likely to utilize rhBMPs

(n=1275, 4.3%) than operations in the Thoracolumbar spine (n=8803, 18.0%). In both the

cervical and thoracolumbar fusion procedure populations, users of the osteobiologic were more

likely to be older, publicly insured and to have higher levels of morbidity as measured using the

Charlson-Elixhauser Comorbidity Index (all OR >1, p values < 0.05). Fusion procedures

involving multiple levels, those aimed at revising previous procedures, and those that employed

instrumentation were also significantly more likely to utilize the osteobiologic (all ORs >1, p

values < 0.05). The use of the rhBMPs also varied with geography. Surgical claims originating

from the Northeastern and Southern states were significantly less likely to use rhBMPs when

compared to procedures billed in the Midwestern region (all ORs <1, p values <0.05). Notably,

female patients were significantly more likely to receive rhBMPs during thoracolumbar fusion

procedures than their male counterparts (OR (95% CI): 1.16 (1.11, 1.22), p value <0.01) yet no

statistically significant gender-based differences were observed among the cervical spine

procedures analyzed (OR (95% CI): 1.07 (0.96, 1.20), p value = 0.22).

For the cervical fusion population, we matched 1,274 cases with 2,545 controls resulting

in a matched cohort of 3,819 patients. Similarly, we created a matched cohort of 25,816

thoracolumbar fusion patients which comprised of 8,746 rhBMP cases and 17, 070 controls. As

shown in Tables 4-25 and 4-26, the propensity score matching process was able to balance the

distribution of the modeled covariates between the rhBMP-exposed and unexposed groups (all

absolute SMD values ≤ 0.2).

Analytical results

For our primary analysis, patients were followed from the date of the index procedure

166

until the first post-procedure cancer diagnosis (Cervical: n=705 (18.5%), Thoracolumbar: n=

5062 (19.6%)), any subsequent exposure to the osteobiologic (Cervical: n=81 (2.1%),

Thoracolumbar: n=366 (1.4 %)), death (Cervical: n=119 (3.1%), Thoracolumbar: n=419 (1.6%),

the end of enrollment in a FFS plan (Cervical: n=134 (3.5%), Thoracolumbar: n= 751 (2.9%)),

or the end of the study period (Cervical: n=2,780 (72.8 %), Thoracolumbar: n= 19,218 (74.4%)),

whichever came first.

Patients who received rhBMPs were just as likely to be censored due to an end in FFS

insurance coverage (Cervical: 3.3% vs. 2.7%, OR (95% CI): 1.15 (0.81, 1.66), p value =0.42;

Thoracolumbar: 3.0% vs. 2.9%, OR (95% CI): 1.07 (0.92, 1.24), p value =0.41) as their

respective controls. Similarly, the observed death rate in both the Cervical and Thoracolumbar

cohorts was not significantly associated with rhBMP exposure during the index fusion procedure

(Cervical: 2.9% vs. 3.4%, OR (95% CI): 0.79 (0.53, 1.19), p value =0.26; Thoracolumbar: 1.7%

vs. 1.6%, OR (95% CI): 1.02 (0.83, 1.25), p value =0.83). On the other hand, patients who had

received rhBMPs during the index fusion procedure were significantly more likely to be

censored due to the use of rhBMPs during subsequent operations than their selected comparators

(Cervical: 2.9% vs. 1.7%, OR (95% CI): 1.70 (1.10, 2.65), p value <0.02; Thoracolumbar: 2.2%

vs. 1.0%, OR (95% CI): 2.11 (1.71, 2.59), p value <0.01). At approximately 20 months, the

mean duration of follow-up was comparable between the rhBMP exposed and unexposed groups

(Cervical: 20.6 (SD = ±12.4) months vs 20.5 (SD = ±12.4) months, SMD = 0.007;

Thoracolumbar 19.9 (SD = ±12.2) months vs 19.8 (SD = ±12.3) months, SMD = 0.012).

We identified 705 cancer cases in the cervical procedure cohort and 5062 cancer cases in

the thoracolumbar surgery population. The average and median durations to the first cancer-

related health care encounter were estimated at 11.3 months and 8.9 months respectively. Table

167

4-27 gives a summary of the cancer cases identified, classified by the affected organ system

listed on the first post-procedure cancer claim. Skin cancer was by far the most common type

diagnosed in both the cervical and thoracolumbar fusion procedure cohorts (55%), followed by

prostate (6.9%), breast (7.9%) and gastrointestinal (5.9%) tumors.

In the propensity score matched cervical population, the incidence of post-procedure

cancer diagnosis was lower in the rhBMP-exposed group (incidence rate: 117.4 per 1000 person

years of follow up, 215 cancer cases) than in the control group (incidence rate: 135.8 per 1000

person years of follow up, 490 cancer cases) but the difference was not statistically significant

(CHR (95% CI): 0.87 (0.74, 1.02); p value = 0.08). Although the incidence of post-procedure

cancer diagnosis was higher in the thoracolumbar fusion cohort (142.9 per 1000 person years of

follow up) than the cervical fusion population (129.6 per 1000 person years of follow up), the

conclusion of both analyses was the same: the correlation between the intraoperative rhBMP use

and the incidence of post-procedure cancer diagnoses was not statistically significant. In the

case of the propensity score matched thoracolumbar cohort, the incidence of post-procedure

cancer diagnoses in the rhBMP-exposed group (incidence rate: 145.5 per 1000 person years of

follow up, 1740 cancer cases) was only slightly higher than in the comparator group (incidence

rate: 141.7 per 1000 person years of follow up, 3322 cancer cases) yielding a statistically non-

significant CHR of 1.03 (95% CI: 0.97-1.09, p value = 0.40).

In unadjusted analysis, patients who received rhBMPs during their index thoracolumbar

fusion procedure were significantly more likely to be diagnosed with cancer during the follow up

period than their unexposed counterparts (CHR (95% CI): 1.19 (1.12, 1.25), p value <0.001).

However, after adjusting for the age and sex of the patient, the association between

intraoperative rhBMP use in this region of the spine and post-procedure cancer diagnosis risk

168

shifted towards the null and was no longer statistically significant (CHR (95% CI): 1.04 (0.99,

1.10), p value = 0.16). Similarly, the risk of receiving a cancer diagnosis following the cervical

fusion procedure was statistically comparable between the rhBMP-exposed and unexposed

groups that met our eligibility criteria (Unadjusted CHR (95% CI): 1.10 (0.96, 1.26, p value =

0.18), Age-Sex Adjusted CHR (95% CI): 0.90 (0.78, 1.04), p value 0.14).

In assessing the effect of the study design on the conclusions, we first tested the impact of

extending the baseline cancer ascertainment window from 6 months to 12 months and then to 18

months. When using the 12 month look back window, the adjusted cause-specific hazard ratio of

being diagnosed with cancer was 0.89 (95% CI: 0.67, 1.18, p value =0.43) following an rhBMP-

augmented procedure in the cervical spine and 0.99 (95% CI: 0.92, 1.06, p value = 0.72)

following an rhBMP-augmented procedure in the thoracolumbar spine. As was the case with the

12 month look back analysis, the use an 18-month baseline assessment window yielded results

and conclusions that were similar to those obtained in the primary analysis (Cervical CHR (95%

CI): 0.80 (0.56, 1.15), p value=0.23; Thoracolumbar CHR (95% CI): 1.03 (0.96, 1.11), p

value=0.43).

Secondly, we tested the impact of imposing of a 180-day induction period between the

index date and the beginning of follow-up. The exclusion of the first 180 days post-procedure

from the follow-up time did not significantly alter the risk estimate obtained during the primary

analysis nor change the conclusions of the study (Cervical adjusted CHR (95% CI): 0.90 (0.76,

1.08), p value 0.26; Thoracolumbar adjusted CHR (95% CI): 1.04 (0.98, 1.11), p value =0.22).

Finally, we assessed the association between intraoperative rhBMP use and risk of post-

procedure cancer diagnosis using a more stringent outcome case definition. In both the cervical

and the thoracolumbar cohorts analyzed, the use of two cancer diagnostic codes incurred over a

169

two month window yielded similar results (Cervical CHR (95% CI): 0.89 (0.71, 1.10), p

value=0.28; Thoracolumbar CHR (95% CI): 1.07 (0.99, 1.16), p value=0.10) to the primary

analyses which had only required a single cancer diagnostic code for case ascertainment.

Summary

Complete summary results of both the primary and secondary analyses appear in Tables

4-28 and 4-29. In summary, we found no evidence to suggest that the use intraoperative rhBMPs

during spinal fusion procedures increased a patient’s risk of receiving a cancer diagnosis

following the operation (Cervical Cohort CHR (95%CI): 0.87 (0.74, 1.02); p value = 0.08;

Thoracolumbar Cohort CHR (95% CI): 1.03 (0.97, 1 .09), p value = 0.40; Pooled CHR (95%CI):

0.96 (0.81, 1.11)). The use of rhBMPs during cervical fusion procedures was associated with

lower hazards for subsequent cancer diagnosis when compared to rhBMP use in the

thoracolumbar spine; the calculated difference in hazard ratios was however not statistically

significant (CHR (95% CI): 0.85 (0.71, 1.01), p value = 0.07).

From a methodological standpoint, we observed that results of both Cox regression

analysis and Fine and Gray Subdistribution hazard regression were consistent with the findings

from Cause-Specific Hazard Model, from which all the conclusions of this study were derived

(Tables 4-28 and 4-29). Additionally, the sensitivity analyses testing key study design features,

including the effect of longer baseline ascertainment windows, the use of a more stringent cancer

outcome case definition and the inclusion of a 6-month induction period, supported the

robustness of the primary conclusions (Tables 4-28 and 4-29).

170

Tables and figures

Figure 4-11. Analysis of cancer risk study population creation flowchart

Patients, ≥ 21 years old, who

received a spinal fusion procedure

(N= 116,577)

Index fusion performed in the cervical

spine

(N=45,028)

Index fusion performed in the

thoracolumbar spine

(N= 72,036)

Exclusion Criteria

Less than 6 months continuous enrollment in

FFS plan

Concurrent fusion at other regions of the

spine

≥ 1 Cancer-related health care encounter

during baseline

rhBMP use during baseline

(N=10,503)

(N= 487)

(N= 4,936)

(N= 1)

(N=14,258)

(N=1,091)

(N=9,133)

(N=4)

Cervical Fusion General Cohort

(N=29,962)

Procedures with rhBMPs (N=1,275)

Procedures without rhBMPs (N=28,687)

Thoracolumbar Fusion General Cohort

(N=48,904)




Cervical Fusion Cohort

(N=3,819)




Thoracolumbar Fusion Cohort

(N=25,816)



171

Table 4-25. Baseline characteristics of patients in the primary cervical fusion procedure study population


BMP Use

BMP Use

No

(n=28687,

95.7%)

Yes

(n=1275,

4.3%)

OR (95% CI) P Value

No

(n=2545,

66.6%)

Yes

(n=1274,

33.4%)

SMD

Mean Follow Up, months 17.8 (12.1) 20.6 (12.3) -- <0.001* 20.5 (12.4) 20.6(12.4) 0.007

Age

21-45 years 4858 (16.9) 95 (7.5) 0.65 (0.52, 0.81) <0.001* 177 (7.0) 95 (7.5) 0.066

46 - 65 years 14476 (50.5) 435 (34.1) Reference

882 (34.7) 435 (34.1)

Over 66 years 9353 (32.6) 745 (58.4) 2.65 (2.35, 2.99)

1486 (58.4) 744 (58.4)

Female Sex 15519 (54.1) 712 (55.8) 1.07 (0.96, 1.20) 0.221 1373 (53.9) 711 (55.8) 0.037

Geographical Region

Midwest 6436 (22.4) 334 (26.2) Reference <0.001* 659 (25.9) 334 (26.2) 0.033

Northeast 2686 (9.4) 89 (7.0) 0.64 (0.50, 0.81)

188 (7.4) 89 (7.0)

South 14961 (52.2) 617 (48.4) 0.79 (0.69, 0.91)

1256 (49.4) 616 (48.4)

West 4604 (16.0) 235 (18.4) 0.98 (0.83, 1.17)

442 (17.4) 235 (18.4)

Insurance Type

Medicaid 447 (1.6) 16 (1.3) 6.68 (3.84, 11.6) <0.001* 40 (1.6) 16 (1.3) 0.089

Medicare 12469 (43.5) 1052 (82.5) 15.8 (12.3, 20.2)

2148 (84.4) 1051 (82.5)


89 (3.5) 66 (5.2)


93 (3.7) 50 (3.9)


0 (0.0) 0 (0.0)


175 (6.9) 91 (7.1)

All of the above 20 (0.1) 0 (0.0) 0.00 (0.00, 0.00)

0 (0.0) 0 (0.0)

Multi-Level Procedure 17585 (61.3) 839 (65.8) 1.21 (1.08, 1.37) 0.001* 1687 (66.3) 839 (65.9)

172




BMP Use

No Yes OR (95% CI) P Value No Yes SMD

Revision Fusion Indicator 584 (2.0) 49 (3.8) 1.92 (1.43, 2.59) <0.001* 90 (3.5) 49 (3.8) 0.016

Surgical Approach

Axial 536 (1.9) 143 (11.2) 8.30 (6.81, 10.1) <0.001* 255 (10.0) 142 (11.1) 0.038

Anterior 25311 (88.2) 814 (63.8) Reference

1657 (65.1) 814 (63.9)

Posterior 2274 (7.9) 282 (22.1) 3.86 (3.35, 4.44)

560 (22.0) 282 (22.1)


73 (2.9) 36 (2.8)


Discectomy 21061 (73.4) 716 (56.2) 0.46 (0.41, 0.52) <0.001* 1456 (57.2) 716 (56.2) 0.020

Laminectomy 2905 (10.1) 289 (22.7) 2.60 (2.27, 2.98) <0.001* 563 (22.1) 289 (22.7) 0.014


Anterior 22170 (77.3) 750 (58.8) 0.42 (0.37, 0.47) <0.001* 1523 (59.8) 750 (58.9) 0.020

Posterior 2223 (7.7) 309 (24.2) 3.81 (3.33, 4.36) <0.001* 588 (23.1) 309 (24.3) 0.027

Non-segmental 616 (2.1) 95 (7.5) 3.67 (2.93, 4.59) <0.001* 171 (6.7) 94 (7.4) 0.026



Allograft Bone 11384 (39.7) 469 (36.8) 0.88 (0.79, 0.99) 0.038* 880 (34.6) 468 (36.7) 0.045

Autograft Bone 4577 (16.0) 268 (21.0) 1.40 (1.22, 1.61) <0.001* 525 (20.6) 267 (21.0) 0.008

Year of Procedure

2007 4010 (14.0) 223 (17.5) Reference 0.005* 426 (16.7) 222 (17.4) 0.019

2008 8116 (28.3) 358 (28.1) 0.79 (0.67, 0.94)

719 (28.3) 358 (28.1)

2009 8604 (30.0) 365 (28.6) 0.76 (0.64, 0.90)

732 (28.8) 365 (28.6)

2010 7957 (27.7) 329 (25.8) 0.74 (0.62, 0.88)

668 (26.2) 329 (25.8)

173




BMP Use

No Yes OR (95% CI) P Value No Yes SMD


< 0 5743 (20.0) 226 (17.7) 1.15 (0.98, 1.36) <0.001* 463 (18.2) 226 (17.7) 0.030

0 12341 (43.0) 421 (33.0) Reference

849 (33.4) 420 (33.0)

1-2 7631 (26.6) 380 (29.8) 1.46 (1.27, 1.68)

767 (30.1) 380 (29.8)

≥ 3 2972 (10.4) 248 (19.5) 2.45 (2.08, 2.88)

466 (18.3) 248 (19.5)

Spinal Conditions

Non -Specific Back Pain 13191 (46.0) 614 (48.2) 1.09 (0.98, 1.22) 0.128 1258 (49.4) 614 (48.2) 0.025

Degenerative Disc

Disease 5034 (17.5) 224 (17.6) 1.00 (0.86, 1.16) 0.985 453 (17.8) 224 (17.6) 0.006

Herniated Disc 12479 (43.5) 362 (28.4) 0.51 (0.45, 0.58) <0.001* 709 (27.9) 362 (28.4) 0.012

Stenosis 10282 (35.8) 497 (39.0) 1.14 (1.02, 1.28) 0.022* 1019 (40.0) 497 (39.0) 0.021

Listhesis 425 (1.5) 34 (2.7) 1.82 (1.28, 2.60) 0.001* 69 (2.7) 34 (2.7) 0.003

Scoliosis 66 (0.2) 5 (0.4) 1.71 (0.69, 4.25) 0.250 7 (0.3) 5 (0.4) 0.020

Fracture/Dislocation 1124 (3.9) 138 (10.8) 2.98 (2.47, 3.59)

<0.001*

* 255 (10.0) 137 (10.8) 0.024

Spinal Cord Injury 466 (1.6) 28 (2.2) 1.36 (0.93, 2.00) 0.117 52 (2.0) 28 (2.2) 0.011

Congenital Anomaly 213 (0.7) 13 (1.0) 1.38 (0.79, 2.42) 0.263 27 (1.1) 13 (1.0) 0.004

Spondylopathy 35 (0.1) 8 (0.6) 5.17 (2.39, 11.2) <0.001* 14 (0.6) 8 (0.6) 0.010

Osteoporosis 46 (0.2) 2 (0.2) 0.98 (0.24, 4.03) 0.976 6 (0.2) 2 (0.2) 0.018

174

Table 4-26. Baseline characteristics of patients in the primary thoracolumbar fusion procedure study population


BMP Use

BMP Use


No

(n=40 101,

95.3%)

Yes

(n=8803,

4.7%)

OR (95% CI) P Value

No

(n=17070,

66.1%)

Yes

(n=8746,

33.9%)

SMD

Mean Follow Up, months

(SD) 17.9 (12.1) 19.9 (12.3) - <0.001* 19.8 (12.3) 19.9 (12.2) 0.012

Age 21-45 years 5861 (14.6) 603 (6.8) 0.78 (0.71, 0.86) <0.001* 1104 (6.5) 595 (6.8) 0.023

46 - 65 years 15346 (38.3) 2023 (23.0) Reference 3966 (23.2) 2015 (23.0) Over 66 years 18894 (47.1) 6177 (70.2) 2.48 (2.35, 2.62) 12000 (70.3) 6136 (70.2)

Female Sex 24251 (60.5) 5636 (64.0) 1.16 (1.11, 1.22) <0.001* 10909 (63.9) 5603 (64.1) 0.003

Geographical Region Midwest 10623 (26.5) 2639 (30.0) Reference <0.001* 5003 (29.3) 2602 (29.8) 0.019

Northeast 3991 (10.0) 759 (8.6) 0.77 (0.70, 0.84) 1457 (8.5) 759 (8.7) South 18889 (47.1) 3727 (42.3) 0.79 (0.75, 0.84) 7418 (43.5) 3717 (42.5)

West 6598 (16.5) 1678 (19.1) 1.02 (0.96, 1.10) 3192 (18.7) 1668 (19.1)

Insurance Type Medicaid 404 (1.0) 139 (1.6) 10.7 (8.61, 13.2) <0.001* 270 (1.6) 137 (1.6) 0.016

Medicare 19490 (48.6) 7339 (83.4) 11.7 (10.6, 12.8) 14167 (83.0) 7287 (83.3) Commercial 14409 (35.9) 465 (5.3) Reference 963 (5.6) 465 (5.3) Medicare + Medicaid 506 (1.3) 228 (2.6) 14.0 (11.6, 16.7) 425 (2.5) 225 (2.6) Commercial + Medicaid 3 (0.0) 0 (0.0) 0.00 (0.00, 0.00) 0 (0.0) 0 (0.0) Commercial + Medicare 5268 (13.1) 622 (7.1) 3.66 (3.23, 4.14) 1227 (7.2) 622 (7.1) All of the above 21 (0.1) 10 (0.1) 14.8 (6.91, 31.5) 18 (0.1) 10 (0.1)

175



Characteristic, n (%) BMP Use BMP Use

No Yes OR (95% CI) p value No Yes SMD

Multi-Level Procedure 20001 (49.9) 4524 (51.4) 1.06 (1.01, 1.11) 0.010* 8842 (51.8) 4506 (51.5)

Revision Procedure 1713 (4.3) 453 (5.1) 1.22 (1.09, 1.35) <0.001* 849 (5.0) 448 (5.1) 0.007

Surgical Approach


Posterior 33363 (83.2) 7580 (86.1) 1.03 (0.95, 1.12) 14867 (87.1) 7541 (86.2) Circumferential 3443 (8.6) 495 (5.6) 0.65 (0.57, 0.74) 991 (5.8) 495 (5.7)

Instrumentation Used Anterior 3057 (7.6) 513 (5.8) 0.75 (0.68, 0.83) <0.001* 871 (5.1) 506 (5.8) 0.030

Posterior 14800 (36.9) 3601 (40.9) 1.18 (1.13, 1.24) <0.001* 7040 (41.2) 3584 (41.0) 0.005

Non-segmental 14418 (36.0) 3192 (36.3) 1.01 (0.97, 1.06) 0.587 6246 (36.6) 3165 (36.2) 0.008


Year of Procedure 2007 5362 (13.4) 1291 (14.7) Reference <0.001* 2422 (14.2) 1275 (14.6) 0.015

2008 10811 (27.0) 2434 (27.6) 0.94 (0.87, 1.01) 4673 (27.4) 2417 (27.6) 2009 12342 (30.8) 2538 (28.8) 0.85 (0.79, 0.92) 4956 (29.0) 2527 (28.9) 2010 11586 (28.9) 2540 (28.9) 0.91 (0.85, 0.98) 5019 (29.4) 2527 (28.9)


< 0 7874 (19.6) 1587 (18.0) 1.10 (1.03, 1.17) <0.001** 3086 (18.1) 1580 (18.1) 0.005

0 16139 (40.2) 2965 (33.7) Reference 5738 (33.6) 2949 (33.7) 1-2 11696 (29.2) 2902 (33.0) 1.35 (1.28, 1.43)

5600 (32.8) 2876 (32.9)

≥ 3 4392 (11.0) 1349 (15.3) 1.67 (1.56, 1.80)

2646 (15.5) 1341 (15.3)

176



Characteristic, n (%) BMP Use BMP Use

No Yes OR (95% CI) p value No Yes SMD

Osteogenetic Factors Used Allograft Bone 8674 (21.6) 2501 (28.4) 1.44 (1.37, 1.51) <0.001* 4557 (26.7) 2457 (28.1) 0.031

Autograft Bone 14396 (35.9) 3365 (38.2) 1.10 (1.05, 1.16) <0.001* 6540 (38.3) 3338 (38.2) 0.003


Discectomy 21395 (53.4) 4378 (49.7) 0.87 (0.83, 0.91) <0.001* 8401 (49.2) 4344 (49.7) 0.009

Laminectomy 23466 (58.5) 5169 (58.7) 1.01 (0.96, 1.06) 0.728 10226 (59.9) 5153 (58.9) 0.020

Spinal Conditions Non -Specific Back Pain 10572 (26.4) 2073 (23.5) 0.86 (0.82, 0.91) <0.001** 4245 (24.9) 2063 (23.6) 0.03

Degenerative Disc Disease 14426 (36.0) 2818 (32.0) 0.84 (0.80, 0.88) <0.001** 5429 (31.8) 2798 (32.0) 0.004

Herniated Disc 14310 (35.7) 2583 (29.3) 0.75 (0.71, 0.79) <0.001** 5033 (29.5) 2576 (29.5) 0.001

Stenosis 24666 (61.5) 5824 (66.2) 1.22 (1.17, 1.28) <0.001** 11419 (66.9) 5794 (66.2) 0.014

Listhesis 14300 (35.7) 3510 (39.9) 1.20 (1.14, 1.25) <0.001** 6882 (40.3) 3487 (39.9) 0.009

Scoliosis 1979 (4.9) 574 (6.5) 1.34 (1.22, 1.48) <0.001** 1109 (6.5) 567 (6.5) 0.001

Fracture/Dislocation 1394 (3.5) 351 (4.0) 1.15 (1.02, 1.30) 0.019* 655 (3.8) 346 (4.0) 0.006

Spinal Cord Injury 63 (0.2) 7 (0.1) 0.51 (0.23, 1.10) 0.087 14 (0.1) 7 (0.1) 0.001

Congenital Anomaly 2589 (6.5) 515 (5.9) 0.90 (0.82, 0.99) 0.035* 985 (5.8) 515 (5.9) 0.005

Spondylopathy 46 (0.1) 11 (0.1) 1.09 (0.57, 2.11) 0.793 19 (0.1) 11 (0.1) 0.004

Osteoporosis 186 (0.5) 62 (0.7) 1.52 (1.14, 2.03) 0.004** 120 (0.7) 62 (0.7) 0.001

177

Table 4-27. Incidence of cancer by organ system of the first tumor diagnosed

Organ System affected by

Cancer, n (%)

Cervical Propensity Score Matched Cohort Thoracolumbar Propensity Score

Matched Cohort

BMP Use BMP Use

Yes

(n= 215)

No

(n=490)

Total

(n=705)

Yes

(n=1740)

No

(n=3322)

Total

( n=5062)

Bones/soft tissue 0 (0.0) 3 (0.6) 3 (0.4) 21 (1.2) 35 (1.1) 56 (1.1)

Brain 4 (1.9) 2 (0.4) 6 (0.9) 14 (0.8) 40 (1.2) 54 (1.1)

Breast 20 (9.3) 38 (7.8) 58 (8.2) 125 (7.2) 215 (6.5) 340 (6.7)

Colon 3 (1.4) 8 (1.6) 11 (1.6) 32 (1.8) 64 (1.9) 96 (1.9)

Endocrine 4 (1.9) 3 (0.6) 7 (1.0) 24 (1.4) 47 (1.4) 71 (1.4)

Gynecological 5 (2.3) 8 (1.6) 13 (1.8) 26 (1.5) 61 (1.8) 87 (1.7)

Head and neck 12 (5.6) 24 (4.9) 36 (5.1) 59 (3.4) 155 (4.7) 214 (4.2)

Lung 3 (1.4) 2 (0.4) 5 (0.7) 8 (0.5) 16 (0.5) 24 (0.5)

Skin (Melanoma) 0 (0.0) 4 (0.8) 4 (0.6) 21 (1.2) 32 (1.0) 53 (1.0)

Skin (Non- Melanoma) 102 (47.4) 269 (54.9) 371 (52.6) 978

(56.2)

1832 (55.1) 2810 (55.5)

Non-colon Gastrointestinal 15 (7.0) 28 (5.7) 43 (6.1) 97 (5.6) 201 (6.1) 298 (5.9)

Pleura/mediastinum 1 (0.5) 1 (0.2) 2 (0.3) 3 (0.2) 5 (0.2) 8 (0.2)

Prostate 24 (11.2) 40 (8.2) 64 (9.1) 145 (8.3) 244 (7.3) 389 (7.7)

Testes/Male Genitourinary 0 (0.0) 1 (0.2) 1 (0.1) 5 (0.3) 8 (0.2) 13 (0.3)

Urinary Tract 5 (2.3) 24 (4.9) 29 (4.1) 73 (4.2) 160 (4.8) 233 (4.6)

Non-specific site 9 (4.2) 22 (4.5) 31 (4.4) 57 (3.3) 100 (3.0) 157 (3.1)

Secondary Tumors (Lymph

Node, secondary cancer of

unspecified site)

2 (0.9) 4 (0.8) 6 (0.9)

15 (0.9) 22 (0.7) 37 (0.7)

Multiple Organs 6 (2.8) 9 (1.8) 15 (2.1) 37 (2.1) 85 (2.6) 122 (2.4)

178

Table 4-28. rhBMP-cancer risk analyses summary results (cervical procedure cohort)

Design Features Statistical Approach Hazard Ratio

Estimate Lower

CI

Upper

CI

p

value

Primary

Analysis

Single Cancer Claim,

6 month lookback

Cause-Specific Hazard Regression 0.897 0.763 1.053 0.184

Cox Hazard Regression 0.904 0.771 1.060 0.214

Fine and Gray Regression 0.887 0.755 1.042 0.144

Secondary

Analysis

Single Cancer claim,

12 month lookback





18 month lookback




Two Cancer Claims,

6 Month lookback





6 month lookback,

6 month induction

Period




179

Table 4-29. rhBMP-cancer risk analyses summary results (thoracolumbar procedure cohort)

Design Features Statistical Approach Hazard Ratio

Estimate

Lower

CI

Upper

CI

p

value

Primary

Analysis


6 month lookback




Secondary

Analysis


12 month lookback





18 month lookback




Two Cancer Claims,

6 Month lookback





6 month lookback,

6 month induction

Period




180

CHAPTER 5

DISCUSSION

The overarching goal of this project was to compare the effectiveness of rhBMP-

augmented fusion procedures with fusion operations that do not use the osteobiologic. The first

section of this work explored the correlates of rhBMP use during lumbar fusion procedures. The

second part evaluated the association between intraoperative rhBMP use and post-procedure

health care service utilization patterns. More specifically, it sought to determine if the use of the

osteobiologic was associated with a reduced demand for inpatient and ER services, refusion

procedures and opioid analgesics. The third main objective was to ascertain whether the use of

the osteobiologics was associated with an increased risk for cancer during follow-up. For

clarity, the implications, strengths and limitations of each section of this dissertation are

presented separately followed by an evaluation of the systemic inferences garnered from this

project.


Based on our analysis of large samples of LDDD-indicated fusion procedures, we

observed that female patients, those who were older, on Medicare, living in the Western states,

or those who had higher levels of comorbidity were significantly more likely to receive rhBMPs

during their surgeries than their selected comparators who did not share these characteristics.

Some of our findings, such as the observed sex differences between rhBMP users and

non-users, are consistent with the previously published reports.27,129

A hypothesis, posited and

tested after the completion of the study, found that the association between sex and rhBMP use

remains even after adjusting for the effect of age. We were unable to find a biological hypothesis

in the literature that could explain the observed variation of rhBMP use between the sexes.

181

Unlike our study, Cahill et al.’s analysis of the 2006 National Inpatient Sample found that

patients who received rhBMPs during spinal fusion procedures were not significantly different

from their comparators in terms of age, race, and insurance status. Several factors can explain the

differences in our conclusions including the differences in predictor variable definition. Case in

point: The Cahill study categorized race as White, Minorities, Other or Unknown while our study

included White, Black, Other, Missing and Indecipherable race categories.27

Our study found no

statistically significant differences in rhBMP use rates between Blacks and White while Cahill

and colleagues observed that both Non-Whites were significantly less likely to have received

rhBMPs than Whites.27

These differences in the definitions of the compared groups limit our

ability to directly compare the conclusions of these studies.

Moreover, it is unclear what proportions of the observed racial differences are

attributable to either biological manifestations of the spinal condition, the socioeconomic context

in which health care in delivered in this country or to the deficiencies in the data. The Tt allele of

Taq I, a Vitamin D receptor gene which is associated with the incidence and severity of disc

degeneration, is more prevalent among Whites (43%) than Blacks (31%) and Asians (8%).165-169

Following this line of argument, one may hypothesize that differential use rhBMPs between the

racial groups is driven by differences in disease severity. Race as a socioeconomic construct has

also been linked to statistically significant disparities in orthopedic surgical intervention and,

conceivably, in the use of surgical innovations such as rhBMPs.170,171

An analysis of 5,690

patients presenting with degenerative lumbosacral pathologies within the National Spine

Network found that racial minorities were less likely to be offered a surgical treatment option

than their White comparators.172

Lastly, the fact that race information capture in reimbursement

data is rarely accurate means that race based observations obtained in both this and the Cahill

182

study are clinically tenuous.173-175

The differences in the patient populations analyzed may also explain the divergent

conclusions regarding the effect of age, insurance status and race. Cahill’s work, which

examined all spinal fusion procedures conducted in 2006 irrespective of the spinal region of the

operation and the indication for the procedure, is in contrast to our focused analysis of the

correlates of rhBMP use during LDDD-indicated lumbar fusion procedures.27

These differences

underscore the need to provide both indication and procedure specific analyses of utilization

patterns.

One of the main strengths of this study was its focused examination of the LDDD

population. The associations discussed in this analysis offer insights into the use of the LDDD

diagnostic code in clinical settings, if not the condition itself. Secondly, the use of the three

alternative definitions for LDDD enabled the identification of robust correlates of rhBMP use

during LDDD-indicated lumbar fusion procedures. Thirdly, by situating the study in a large,

geographically diverse administrative claims data environment and using limited exclusion

criteria, we were able to provide conclusions that are arguably generalizable to the adult LDDD

population in the United States.

Conversely, the study has two main limitations. Firstly, the claims data environment

lacks sufficient clinical depth to fully characterize the LDDD condition. In particular this study

is highly susceptible to confounding by disease severity given the absence of informative

imaging data. Secondly, the dataset lacked sufficient and reliable race information. With

approximately 30% of the study population lacking in useable race data (Appendix A), the study

was unable to definitively adjudicate the effect of the race on rhBMP utilization patterns. Other

potential factors that are likely to influence rhBMP use, including physician preference, hospital

183

volume, smoking and obesity, were not captured in this data and therefore not evaluated.176-178

Studies are needed to determine how factors such as smoking and obesity which have yet

to be studied in large observational datasets influence the use of rhBMPs in real world settings.

The results of this study underscore the need to not only identify the axes of difference but to

also investigate the reasons why such correlations exist. More specifically, clarity in needed to

ascertain whether the differences observed, be it in the fact that procedures in the female patients

are more likely to utilize the osteobiologic or that some racial minorities are less likely to receive

rhBMPs during their procedures than Whites, are a consequence of reasoned clinical judgement

or a product of systematic inequalities that need to be remedied.


Part IIA of the dissertation used time to event analyses to assess the relationship between

rhBMP use during fusion procedures and the risk of undergoing a subsequent refusion surgery

during follow-up. The results of our investigation suggest that the association between rhBMP

use and refusion procedure risk varies based on the indication of the primary procedure.

With a median follow-up time of 18 months, the rate of refusion procedures in this study

ranged from 1.4% in the Listhesis population to 2.1% in the LDDD cohort. Unlike the previously

published observational studies which have dealt almost exclusively with general reoperation

rates, our study sought to specifically examine the incidence of revisions through refusion

procedures only. This difference in specificity may explain why the observed revision rates in

this dissertation are marginally lower than then the 2% - 4% reoperation rate commonly cited in

the literature.33,91,179,180

To our knowledge, this is the first published attempt at systematically evaluating the

indication specific effects of rhBMP use on refusion procedure risks. We found no evidence of

184

an association between rhBMP use and the risk for refusion procedures among patients who

received a LDDD- or Listhesis-indicated lumbar fusion surgery. In contrast, rhBMP use during

Stenosis-indicated fusion procedures was associated with a twofold decrease in the risk for

refusion operations. More noteworthy than the difference in findings between the LDDD and

the Stenosis cohorts are the divergent conclusions obtained in the Stenosis and Listhesis

populations. While the use of fusion procedures in LDDD cases is controversial, the literature is

generally supportive of surgical intervention in the treatment of both Listhesis and Stenosis.132-

134,181 Reasons for the observed indication-specific effects are unclear. Possible explanations,

which may range from physiological differences in the problem being corrected through the

fusion procedure to variations in clinical practice that affect patient selection and monitoring

across the different indications, are as yet unexplored.

The strengths of this study include the use of sensitivity analyses to confirm the

robustness of the study design and the use of propensity score matching to adjust for measured

confounding and selection bias. Unlike hip arthroplasty studies which found death to be a

significant competing risk for revisions of hip replacements,145,182

the same was not observed in

this investigation. If we use the revision arthroplasty literature as reference then the adjustment

for death as a competing risk, while theoretically sound, is not statistically and clinically

impactful when assessing revision risks over short ( < 3 years) durations of follow up.145,182

The study also had several limitations; primary of which is the issue of missing and

incomplete data. Firstly, we failed to account for the confounding effect of race on the refusion

procedure risk. Although previous studies have found statistically significant links between a

patient’s race and the receipt of osteobiologics during fusion procedures, the association between

race and the timing of the refusion procedures remains unexplored.21,27

Moreover, insights from

185

disparities research suggest that an association, should it exist, would mean that racial minorities

are more likely to delay revision procedures resulting a bias away from the null. Other potential

sources of residual confounding whose direction and magnitude are unclear include smoking,

obesity, disease severity, and provider and hospital factors.177,183,184

The lack of clinical depth is also a critical factor in assessing the validity of our analysis.

The billing mechanism used in administrative claims is unable to distinguish between the

different spinal joints within the lumbar spinal region. From an analytical perspective,

researchers relying solely on claims data are unable to confirm that the revision procedure

observed was conducted to correct the index operation of interest. To mitigate the effect of this

detail, we used a competing risk model to quantify the effect of intervening primary fusion on

the refusion risk estimate. Although approximately 10% of patients analyzed underwent a

second primary fusion procedure before a revision surgery was observed, the estimate of revision

procedure risks did not differ from the primary analysis. From a clinical perspective the results

of our study call for increased transparency regarding the calculation of revision procedure risks

within an administrative claims environment.

Future studies should attempt to replicate the main finding of this investigation, namely

that the association between the rhBMP use and refusion procedure risks varies based on the

indication of the index procedure. Ideally such follow-up studies would be conducted within a

clinically rich data environment in which the specific vertebra that are operated on can be

confirmed and confounders such as race, smoking and the patient’s weight can be adjusted for.

Additionally, the acceptability of this finding may, and we assert should, be contingent on a

sound biological hypothesis that can explain the indication specific effects observed.

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Our analyses were unable to confirm an association between rhBMP use and the post-

discharge hospitalization utilization patterns using three measures 1) 30 day readmission rate, 2)

time to the first LDDD-related readmission, and 3) the number of LDDD-related readmissions

during the first year.

Although comparable from a clinical perspective, the 8% to 9% 30 day readmission rate

observed in this study were statistically lower than the 12% rate reported by Deyo and

colleagues.91

Like our analyses, the Deyo study examined the 30 day readmission rates of

Medicare beneficiaries albeit using a much larger sample size (n=16,822) of Stenosis-indicated

lumbar fusion procedures conducted in 2003 and 2004.91

One main factor that may explain why

the rate of early readmissions during the second half of the last decade would differ from

estimates based fusion procedures performed shortly after FDA’s approval of the InFUSE™

(rhBMP-2) product in 2002: Considerable research and clinical effort has been focused on

reducing early readmission rates in the last decade hence the difference observed between the

two studies could be a result of improvements in patient selection, surgical technique refinement,

case management and discharge planning over time. The variation in the readmission rates may

also be attributable to differences in the clinical populations examined. Deyo’s study used

Stenosis-indicated procedures while ours analyzed LDDD-related surgeries. What is consistent

between these two analyses, and others that have published in the intervening years, is the

absence of evidence linking rhBMP use to the risk for early readmissions irrespective of the

indication of the procedure and the demographic profile of the study population.33,91

This study represents an extension of the existing literature beyond the evaluation of 30

day readmission rates to the study of how rhBMP use is related to long term, condition specific

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hospitalization patterns. Given the small sample sizes and low event rates obtained, our analysis

of the timing and the number of LDDD-indicated readmissions yielded inconclusive results.

Case in point: Our study estimated that the effect of rhBMP use on the number of LDDD-related

hospitalizations during the first year could range from a reduction of 78% to an increase of

1040%.

Despite the limited inferences garnered from this study, the execution of this project did

provide insights that should prove informative to subsequent analyses. Firstly, the number and

duration of all-cause readmissions prior to surgery was a strong and consistent predictor of both

the timing and the number of LDDD-related readmissions during follow-up. This finding is

consistent with the current literature that has found prior hospitalizations to be a significant risk

factor for subsequent readmissions across different clinical and demographic populations.185-187

Secondly, if we accept the premise that readmissions for the low back pain are indicators of

treatment failure then attempts at measuring the effectiveness of surgical intervention may

require extended follow-up periods. In this study, the average time between an LDDD-indicated

fusion procedure and the first LDDD-related readmission was 12 months. Lastly, we observed

that a significant proportion (25.6%) of patients were discharged or transferred to another

inpatient facility following the index procedure hospitalization presumably for rehabilitative and

transitional care.97

While the conventional approach is to exclude these patients from the

analysis of readmission rates, the large numbers of patients discharged into secondary inpatient

care settings suggest the need for further exploration into this subset of the surgical population.

Future studies that overcome the shortcomings of this project are also highly

recommended. In particular, we suggest that this study be replicated using large sample sizes that

can detect even smaller effect sizes. Due to data constraints, the results of our study only speak

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to effects of rhBMP use on the hospitalization patterns of Medicare beneficiaries who were over

the age of 65 at the time of their LDDD-indicated fusion procedure. Therefore, the question of

whether the effect of rhBMP use on readmission patterns varies across the age spectrum remains

unanswered. Future studies should also examine whether discharge planning, a known risk

factor for early all cause readmissions, also impacts long term, condition specific hospitalization

patterns.188-190


Our attempt at assessing the association between rhBMP use and the number of LDDD-

related ER visits during the first year yielded inconclusive results. Based on our main analysis

using the hierarchical algorithm cohort, the effect of osteobiologic use on the number of LDDD-

related ER visits could range from a reduction of 49% to an increase of 1270% hence the

indeterminate conclusion of the study. While the conclusions of the sensitivity analyses using

alternative methods of identifying LDDD-indicated fusion procedures also failed to confirm an

association between rhBMP use and the outcome of interest, the larger sample sizes resulted in

much smaller confidence intervals. In the case of the primary diagnosis cohort, the effect of

rhBMP use on the incidence of LDDD-related ER visits is believed to range from a 68%

reduction to an 87% increase while the effect estimated from the comprehensive definition

cohort could range from a decrease of 50% to an increase of 88%.

Like the readmission analyses discussed in Part IIB, this study was limited with the

relatively small sample sizes and the associated lack of precision in the effect estimates. Other

limitations of the study include the lack of information on the disease severity which is likely to

influence the health utilization trajectory and the failure to account for the potentially

confounding effect of discharge planning. Furthermore, the results of our study only speak to the

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relationship between rhBMP use and the ER visits patterns of Medicare beneficiaries who were

over the age of 65 at the time of their LDDD-indicated fusion procedure which may not hold true

in younger adults.

Future studies should build on insights garnered from this first published attempt at

linking rhBMP use to the demand for condition specific ER visits. A critical observation of this

study is that LDDD-specific codes are seldom recorded in ER visit claims. As noted in the

results section, only 4% (n=2) of the LDDD-related visits in the comprehensive definition cohort

included an LDDD-specific diagnostic code while the rest of the outcome events were identified

using non-specific low back pain diagnostic codes. This finding was consistent with a previously

published analysis of the National Hospital Ambulatory Medical Care Survey which also

observed that the listing of specific back pathologies in ER encounter records is rare.103

Future

studies are needed to determine the validity of using both LDDD-specific and non-specific low

back pain diagnostic codes to define condition-specific ER visits.103

A reanalysis of this research

question using a large, more age diverse study population is also proposed with the aim of

arriving at more precise estimates of the osteobiologics’ effects on condition-specific ER visit

patterns.


In an evaluation of patients who received LDDD-indicated lumbar fusion procedures, we

found that the use of rhBMPs was not significantly associated with changes in the opioid access

patterns during the first year following the procedure. More specifically, we observed that

rhBMP recipients both reduced their opioid analgesic access rates and discontinued opioid

therapy at levels that were statistically comparable to their selected comparators.

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The study population, albeit small in comparison to conventional claims-based research

projects, was large enough to detect an effect size of at least 0.18 with a type 1 error rate of 0.05

and 80% power. Based on Jacob Cohen’s effect size cutoffs for F-tests like the ANCOVA, an

effect size of 0.18 can be described as small to moderate.141,191,192

Consequently, the conclusions

of this study do not rule out the possibility that the effect of rhBMP use on changes in the opioid

access rates is smaller than the 0.18 detectable by this study sample.

The main strength of this investigation is the use of two outcome evaluation windows

which permitted the assessment of opioid analgesic use at both the short term (3 to 6 months)

and the relatively longer term (9 to 12 months). As the results of our analysis indicate, opioid

access patterns do vary with time. In particular, we observed that a smaller proportion of

patients were on opioid therapy during the 9 to 12 month window (63.8%) than during the 3 to 6

month post-procedure time frame (77.7%). The decrease in the number of patients filling opioid

analgesic prescriptions supports the foundational assumption of this investigation; to wit: the

fusion procedure acts to ameliorate the patient’s pain thus reducing their demand for opioid

analgesics. With the passage of time, the patients receive relief from both the underlying LDDD

condition and the surgery hence the greater decrease in opioid refills at 9 months after the

operation.

To our knowledge, this is the first published attempt at linking rhBMP use to changes in

the demand for opioid analgesics before and after fusion procedures. Our findings are however

consistent with early RCTs which found that patients who received the osteobiologics reported

lower levels of back pain than their selected controls.23 However the reported difference in pain

scores between the rhBMP-exposure groups (-1.58 on a 20 point scale (95% CI: -2.65, -0.51)),

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like the difference in opioid analgesics acquisition rates in this study, was neither statistically nor

clinically significant.23

The results and conclusions of this study are only interpretable within the context of its

assumptions and limitations. Firstly, we assumed that all the opioids accessed by the patient

were captured within the dataset. While the completeness of data is a central assumption in all

claims-based research, its appropriateness when examining opioid access patterns is subject to

heightened scrutiny. Aberrant opioid acquisition methods including diversion from friends and

family, doctor and pharmacy shopping have been well documented in the literature.193-195

Records of patients who acquire analgesics through these alternative routes would be unavailable

in a claims dataset like the MPCD since these transactions are mainly conducted on a cash

basis.196

The impact of this potentially missing information is mitigated by the design of our

study which is focused not on absolute opioid analgesics access rates but rather on within-person

differences before and after surgical intervention.

Secondly, in order to facilitate comparisons between different opioid analgesic types, all

opioid prescriptions accessed were converted into OMEUs. Significant debate exists about the

accurate conversion rate for the opioid antagonists in the market.197

To illustrate the challenge,

consider the process of converting Methadone doses into OMEUs. There are at least three

different conversion ratios cited in the literature each representing disparate approaches to the

Morphine to Methadone relationship.198-200

Depending on the approach used, a 90mg oral

morphine daily dose is equivalent to either 19mg, 24mg, 25mg or 30mg of Methadone.199

The

audience should be aware of the effect of these variations on the point estimates obtained in this

study.

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Another limitation of the study is the failure to adjust for confounding effect of race. The

general consensus in the literature is that racial minorities are both less likely to receive opioid

analgesic prescription for chronic non-cancer pain and to receive the osteobiologic rhBMPs

during lumbar spinal fusion procedures.27,201,202

An accurate estimation of the race effect is

intractable in this dataset due to the extensive pattern of missing values in the race variable.

Although efforts were made to account for other common non-cancer pain conditions that

are managed using opioid analgesics including Rheumatoid Arthritis, Sickle Cell Disease,

Neuropathic Pain, Migraines and Fibromyalgia, the list was not exhaustive. Theoretically, the

presence of other, unadjusted for chronic pain conditions can attenuate the effect of the fusion

procedure on ameliorating the patient’s perceived pain and, by extension, on their need for

opioid analgesics. These chronic pain conditions such as pelvic pain may provide the impetus

for continued opioid use even after the fusion procedure’s success.

Future investigations into the effect of rhBMP use on the demand for pain medication

should be focused on the 63% of patients who were excluded from this study because of their

failure to access opioid analgesics at baseline. Understanding the dynamics of pain management

among these non-users could offer insights into how opioid analgesics are used to manage

surgical pain immediately after the surgery and its effects on the pain trajectory of fusion

procedure recipients. Further analyses are also needed to determine if the effect of the

osteobiologic on the demand for opioid analgesic varies based on the indication of the fusion

procedure.

Part III: Effect of Using Intraoperative rhBMPs on Cancer Risk

An analysis of this large and diverse patient sample was unable to confirm that the use of

rhBMPs was associated with an increased risk for cancer. Moreover, through stratified analysis

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of cervical and thoracolumbar fusion procedures we found no evidence to suggest that the

association between rhBMP use and the post-procedure cancer risks varies based on the spinal

region operated on.

Our main finding was consistent with a recently published meta-analysis which also

found no statistical association between intraoperative rhBMP use and the risk for new onset

cancer diagnosis.203

The results of our study do not preclude the possibility that a subset of

patients is faced with a higher risk for cancer diagnosis following rhBMP-augmented fusion

procedures. So far, only one published study has reported a significantly higher cancer risk

following rhBMP exposure.108

The study in question, which used a 40mg/ml preparation of the

rhBMP-2 during a multi-center randomized control trial, observed a fivefold increase in cancer

diagnoses among patients treated with the osteobiologic compared to the selected controls.108

The results of AMPLIFY™ rhBMP/CRM combination product RCT, of which the 40mg/ml

rhBMP-2 preparation was a part, supports the theory that the rhBMP-related oncogenic effect is

dose dependent.77

Despite the FDA’s decision not to approve this high concentration rhBMP-2

combination product, concerns exist that the currently marketed rhBMP products are being used

at higher doses than was tested in the initial RCTs.108

The study has several strengths worth noting. Given the study’s use of a large and diverse

population and broad eligibility criteria, we assert that the results are generalizable to adult

patients receiving spinal fusion procedures in the United States. Secondly, the models used

propensity score matching to efficiently adjust for a wide array of potential confounders without

significant losses in statistical precision. Thirdly, the robustness of the study’s conclusions are

supported by a series of sensitivity analyses that were designed to gauge the effect of changing

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the baseline ascertainment window, using an alternative cancer case definition, and including a 6

month induction period prior to beginning of follow-up.

Like all analyses, this investigation was also faced with some limitations. As noted in

previous sections of this dissertation, there is a grave lack of clinical depth in administrative

databases such as the MPCD. For example, the cancer billing codes used to ascertain outcome

status are often indistinguishable from diagnostic and monitoring health care encounters during

which suspected cancer diagnoses are ruled out.204

Administrative claims also lack information

on the staging of cancers at the time of diagnosis. Staging information, had it been available,

would inform both the timeline between osteobiologic exposure and tumor manifestation and the

clinical significance of our findings.

The second main concern was the short follow-up time. On average, patients in the

primary study cohorts were followed for approximately 20 months. Without clarity about the

biological mechanism responsible for the rhBMP-related cancer risks, such a short duration of

follow-up leaves open the possibility of a long latent period between exposure to the

osteobiologic and the manifestation of the increased cancer risk.

Thirdly, the results of this investigation may be susceptible to the effects of unmeasured

confounders such as smoking, weight, race and a family history of cancer.184

176,177,205

Cigarette

smoking which is a significant risk factor for several cancers is also known to reduce fusion

rates.176,183

Conceivably, rhBMPs are used with higher frequency among smokers in order to

counteract the effects of smoking on bone growth.177

If smokers are, as hypothesized, more

likely to receive rhBMPs and also more likely to be diagnosed with cancer then the cancer risk

associated with rhBMP use is accentuated by smoking.

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The results of the current study highlight important avenues for future research. Firstly,

further studies are needed to assess the off-label use of high doses of the osteobiologic and its

potential link to heightened cancer risks. Moreover, no study to date has examined the effect of

cumulative exposures to the osteobiologic. If the dose-dependent hypothesis is to be believed

then the following question warrants asking: should there be a lifetime rhBMP exposure limit?

General Discussion

A key finding of this dissertation is that LDDD-indicated fusion procedures, as captured

in administrative claims databases, are rare. Out of the 2.135 million patients in the MPCD

extract who had a least one LDDD-related health care encounter, only 26,879 had a fusion

procedure that listed LDDD as a contributing diagnosis. Fewer still were the 7,420 patients who

had a fusion procedure that involved an LDDD diagnosis in the absence of any of the other

major degenerative conditions of the spine such as Disc Herniation, Stenosis, Listhesis and

Scoliosis.

The infrequency of LDDD- indicated procedures meant substantially smaller sample

sizes than is conventional in claims-based research thus negating one the main advantages of

using administrative claims databases: the ability to detect small effect sizes through large study

populations. The consequences of the small study cohorts were felt most acutely in parts IIB and

IIC of this dissertation which analyzed the effects of rhBMP use on readmission and ER visit

patterns using sample sizes of between 184 and 2088.

So are few LDDD-indicated procedures being performed or are these procedures being

underreported? Controversy regarding the use of surgical intervention for chronic low back pain

conditions is not new.26,36,206

The response by CMS and by Blue Cross Blue Shield of North

Carolina insurance exemplify the two divergent approaches to the controversy that have since

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shaped the use of spinal fusion procedures in the United States.207

In 2006, Medicare convened a

meeting to discuss the appropriateness of funding spinal fusion procedures for the treatment of

degenerative conditions. The main item discussed was a systematic review of the existing

literature which concluded that for DDD the “evidence for lumbar spinal fusion does not

conclusively support the short and long term benefits compared to non-surgical treatment”.208

Despite the deficiency in the data, CMS appears to pay for DDD- indicated spinal fusion

procedures.36

On the other hand, private payers such as Blue Cross Blue Shield of North

Carolina and Aetna have issued policy memoranda unequivocally classifying the use of fusion

procedures solely for Degenerative Disc Disease as medically unnecessary.207,209

Could the differential funding contexts in which fusion procedures are performed explain

why Medicaid beneficiaries in the 61 to 65 age group were 14 times more likely to receive

rhBMPs than a similarly aged commercially-insured population (Part I of dissertation)?

Although admittedly Medicare patients are more likely to be on the upper end of this 5 year age

group than their commercially insured comparators, we ask: are reimbursement pressures related

to differential reporting of diagnostic codes on fusion procedure claims? Such variability, should

it exist, has far reaching implications to the study of LDDD in secondary data sources. For

example, discrepancies in reporting across the payers would hinder our ability to directly

compare younger adults, who are mainly insured by commercial payers, with the elderly who are

covered by Medicare. Confirmation of this hypothesis represents an emerging frontier in

administrative claims validation studies.

The alternative explanation is that publicly insured patients are simply having more

LDDD-indicated fusion procedures. This hypothesis is supported by a recent study by the Martin

and colleagues (2014) which found that broader coverage policies were linked to more frequent

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use of fusion procedures among patients on Worker’s Compensation programs.210

Consequently,

if the promise of Multi-Payer datasets is to be fully realized, then such data sources should be

designed to include more granular insurance plan information that can be used to effectively

adjust for variations in coverage decisions.

The second main takeaway from this project is that indication matters. Despite our noted

concerns regarding the accuracy and completeness with which procedure indications are

captured, conclusions of this dissertation lend support for more indication specific studies. Using

a validated hierarchical algorithm for identifying the indication for spinal surgical procedures,

our research found that the association between rhBMP use and refusion risk varied based on the

indication listed on the procedure claim (Part IIA).42

Although numerous research papers,

practice guidelines and funding memoranda have provided indication–specific commentaries of

the fusion procedure itself, the literature is largely silent on indication-specific rhBMP

effects.33,132-134,181,207,209,211

The third noteworthy observation is that implantable device epidemiology differs from

classical pharmacoepidemiology. In particular, the dimensions and utility of new user designs

within medical device epidemiological studies warrant further investigation. The new or incident

user design is employed to mitigate the effects of the healthy user bias which presupposes that

some patients will discontinue treatment early on due to adverse events or perceived lack of

effectiveness leaving behind only those for whom the treatment is deemed beneficial.131

Failure

to account for the healthy user bias favors the intervention particularly in cases where the risk

profile of the treatment varies with time. The common approach in observational

pharmacoepidemiology studies is to assert a 180-day look back period during which previous use

of the treatment of interest is assessed.131

With prescription refills commonly dispensed in 30, 90

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and even 180 day supplies, the 180 day look back period has strong foundational basis in clinical

practice. What then defines a new user of medical devices? The definition of new users of

implantable medical devices is challenged by the variability in the duration between exposures.

The problem is magnified within the realm of spinal orthopedics where multiple vertebral

segments exist allowing for multiple exposures over time. As patients return for subsequent

surgeries, their past experience with the device can conceivably create a class of healthy users of

the intervention. We need a better understanding of the magnitude and characteristics of

recurrent users of medical devices and sound study designs to mitigate any biases that they may

represent.

In conclusion, the results of this dissertation suggest that health care service utilization

outcomes for patients receiving rhBMPs during LDDD-indicated spinal fusion procedures are

comparable to those of rhBMP non-users. This work extends the literature by commenting on

the association between rhBMP use and both the patterns of accessing opioid analgesics during

the first year following the procedure and the risk for cancer following rhBMP use in the cervical

spine. The specific results and conclusions are however contextualized by the project’s

limitations. Case in point: Although we were unable to reject the null hypotheses relating rhBMP

use to readmission and ER visit patterns, the wide confidence intervals obtained from our

analyses leave open the possibility of undetected, clinically meaningful differences that should

be explored through larger sample sizes. Moreover, this investigation did not account for

multiple testing and its results should be therefore be interpreted accordingly. Several future

avenues for research have been discussed previously. Of particular importance is the need to

clarify whether the differences observed, be it in the indication-specific refusion risks discussed

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in Part IIA or the geographical variation in the use of rhBMPs in Part I, are due to biological

imperatives or inequalities in health care system that require remediation.

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APPENDIX A

MPCD DATA STRUCTURE

A common data model was developed to combine records from the different MPCD data

contributors. The resulting database consists of four files: a services and demographics file, a

diagnosis file, a procedure file and a member and enrollment file. The use of administrative

claims data, and the MPCD in particular, presented unique challenges to the implementation of

this dissertation research. In the paragraphs that follow, we outline the four specific data driven

issues we encountered, the solutions we implemented and the implications of these chosen

approaches on this research and on claims-based research in general.

Identification of Spinal Fusion Procedures

Assessment of Provider and Institutional Claims

By convention, surgical interventions include two separate claims. The first claim is

tendered by the provider while the second is submitted by the facility for the institutional costs

relating to the procedure. In addition to indicating the fusion procedure, the institutional and

provider claims each provide distinct auxiliary information about the surgery undertaken. For

example, the distinction between primary and revision fusion procedures can only be made

through ICD-9-CM codes which are used almost exclusively in institutional claims.

A preliminary review of the MPCD data revealed that 36,264 (21.3%) of 170,202 spinal

fusion procedure episodes in the dataset lacked either a fusion-specific professional charge or

institutional claim. Table A-1 below gives the specific breakdown of the incomplete fusion

procedure cases. Only 25.5% of the institutional fusion codes that lacked a complimentary

fusion-specific professional charge were found to have a concurrent non-fusion specific provider

claim (Table A-2). The problem was more acute among the professional fusion claims that

lacked a complimentary fusion-specific institutional charge in that only 11.2% of these

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encounters were found to have a concurrent, non-fusion specific institutional claim (Table A-2).

Of the many possible explanations for the missing claims, the most troubling is the presence of

alternative payment streams. Claims based research is built on the assumption that the records at

hand are a complete and accurate representation of the patient’s engagement with the health care

system. In order to maximize the internal validity of the results in this dissertation, we restricted

our analysis to fusion events that contain both a provider and an institutional claim. The

restriction criterion was also extended to exclude any subjects with a history of incomplete

fusion events since these claims compromise our ability to fully adjudicate the subject’s health

care utilization pattern.

Assessment of Multiple Provider Charges

Multiple fusion-specific provider claims, dated several days apart, within the same

institutional claim were observed in the data. There are three main potential explanations for this

pattern of codes:

Pre- or Post- surgical care

A planned, multi-stage procedure performed on two or more distinct occasions

An unplanned return to the operating room occasioned by complications of the

original procedure.

Although not always provided, Healthcare Common Procedure Coding System (HCPCS)

Level II Modifiers are codes can be used to differentiate each of above scenarios. However, in a

significant proportion (27.6%, n=43,805) of the provider claims used to identify lumbar fusion

procedures, these modifier codes were marked as “N/A” or invalid.

Only 1.5% (n= 2,582) of the 170,202 fusion procedure events identified in the dataset had

more than one provider claim in the window. Given the low frequency of this pattern of records

and our inability to conclusively distinguish between a planned second procedure and a return to

operative table due to complications, we excluded all 2,549 subjects involved from our project.

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In summary, for a fusion event to be included in our analysis it had to 1) be comprised of

both an institutional and provider claim and 2) consist of only a single fusion provider service

date.

Indications for the Spinal Procedure

There are three approaches to identifying the indication of a spinal fusion procedure

within administrative databases: 1) using the primary diagnosis listed on the claim, 2) using all

the diagnoses in the claim and 3) employing a hierarchical algorithm based on the demonstrated

efficacy of surgery in treating the listed diagnoses.

The hierarchical approach, as set out by Martin et al (2014), involves mutually exclusive

diagnostic categories which are ordered based on how well surgical intervention is believed to

treat these conditions.42

Since there is stronger evidence supporting the use of surgery in treating

Scoliosis than in treating non-specific back-pain, Scoliosis is listed higher in the hierarchy than

non-specific back pain. The complete list of the Martin’s degenerative spinal conditions

hierarchy is as follows: (1) Scoliosis (strongest evidence), (2) spondylolisthesis, (3) spinal

stenosis, (4) herniated disc (with or without myelopathy), (5) nonspecific back pain (including

degenerative disc disease) and (6) muscle sprains/strains (weakest evidence).42

To illustrate the

application of this algorithm, consider a patient with the following diagnostic codes in their

lumbar fusion procedure claim: ICD-9-CM 724.2 (Low Back Pain), 722.2 (Herniated Disc) and

724.02 (Stenosis). Given that there is stronger evidence supporting the use of surgery in Stenosis

cases than in cases of herniated discs and low back pain, this surgery is classified as a Stenosis-

indicated procedure.

In order to isolate patients diagnosed with Degenerative Disc Disease, we adjusted the

Martin et al hierarchical algorithm by dividing the non-specific back pain category into the

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following: Degenerative Disc Disease and other non-specific back pain conditions. This new

hierarchy, and the ICD-9-CM diagnostic codes that defined each category therein, is presented in

Tables B-2, B-3, B-4 and B-5.

The hierarchical algorithm was used as the main method of identifying LDDD-indicated

fusion procedures in this dissertation. Additionally Part I, IIB, IIC, conducted a series of

secondary analyses aimed at assessing the effect of the method used to identify the indication for

the surgery on the conclusions of the main analysis. The primary diagnosis definition cohort

consisted of any procedure that listed LDDD as the primary indication while comprehensive

definition cohort was comprised of any procedure that listed a LDDD diagnostic code at any

position on the fusion procedure claim.

Identification of Inpatient Encounters and Emergency Room Visits

Parts IIB and IIC of this dissertation relied on the accurate identification of inpatient and

ER encounters. According to the supplied data dictionary, the MPCD included an “Encounter

Type” variable aimed at distinguishing between inpatient stays, emergency department

encounters, ambulatory visits, and other ambulatory encounters. A preliminary review of the

variable revealed that 20.8% of the institutional claims were missing for this variable (Table A-

3). The missing data problem occurs almost exclusively within the commercially insured records,

where 68.9% of the claims have missing values in the encounter type variable field (Table A-4).

Notably, the Emergency Room designation within the Encounter Type variable is never used in

this data extract which suggests an error in either the data dictionary, the data transfer process or

both. An alternative approach to identifying ER visits was therefore employed.

We explored three different approaches to identifying acute inpatient stays and ER visits

in light of the problematic encounter type variable. The first approach considered was Multiple

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Imputation (MI) in which the missing value is estimated based on the patient’s profile. The

second approach would require the researcher to define the logical cluster of claim features that

are likely to indicate acute care inpatient stays or ER encounters. Like the multiple imputation

approach, this method is based on the other variables on the claim including procedure and

diagnostic codes used, the duration of the encounter, and the place of service codes. These

methods rely on the concept of missing at random which is an untestable assumption in the

absence of supplemental information about the population.212

The solution selected for this dissertation was exclusion. In order to limit the effects of

outcome misclassification bias and to maximize the internal validity our study, we restricted the

study to Medicare beneficiaries for whom the encounter type, place of service, and the admission

and discharge date variables necessary for identifying the acute inpatient encounters are well

populated. The Medicare data also uses the revenue code field which offers an efficient approach

to identifying ER visits within administrative databases (Tables A-3 – A-5).156

Other researchers

who have studied hospitalization patterns using the MPCD data have also restricted their analysis

to the Medicare population.213

Admittedly, the decision to limit Parts IIB and IIC of the

dissertation to the Medicare population has reduced the power our analyses and limited the

generalizability of our conclusions.

Racial Categorization

The MPCD data structure allows for five racial categories: Asian, Black, Hispanic, White

and Other. Since the race variable was imported directly from the data contributors, the absence

of consistent race definitions across the different data partners introduces variability in

classification and challenges to our ability for cross data comparisons. One main concern is that

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options such as Asian and Hispanic were not universally adopted at the individual data

contributor level.

The race variable also exhibits significant miscoding. In approximately 18.4% of the

subjects race was provided as an unintelligible symbol (‘!’). A further 2.6% of the subjects had

their race listed as unknown and less than 0.1% had completely missing race information.

As shown in Figure A-1, levels of race miscoding varied by age (OR= 0.9, p <0.001) and gender

with men being slightly more likely to have their racial identifier miscoded than women (OR=

1.08, C.I= 1.07-1.09).

At 31%, the highest levels of race miscoding are observed in Connecticut while the

lowest was in Puerto Rico where only 1.8% of the race codes were unintelligible. More

importantly, the unintelligible race code, “!”, occurs exclusively among commercially insured

subjects, which could suggest a difference in race coding practices or a data transfer error (Figure

A-2).

We employed three different approaches to handling the race miscoding problem. For

reasons outlined previously, Parts IIB and IIC of this dissertation excluded all commercially

insured patients which coincidently permitted us to consistently assess and adjust for the effect of

race on readmission and ER visit patterns among the publicly insured population that remained.

The second approach, employed for Part I of the dissertation, included all racial groups in the

analysis including the indecipherable and missing categories and examined the association

between listed race and the likelihood of receiving rhBMPs during LDDD-indicated fusion

procedures. The result, while tenuous in characterizing the effect of the patient’s actual race on

rhBMP exposure, speaks to the relationship between the racial data available to researchers and

206

the use of the osteobiologic. The third approach excluded racial considerations from Parts IID

and III of this dissertation and discussed this decision as limitation of the study.

Figure A-1. Association between race miscoding and age

0

5

10

15

20

25

30

0 -

5

6 -

10

11 -

15

16 -

20

21 -

25

26 -

30

31 -

35

36 -

40

41 -

45

46 -

50

51 -

55

56 -

60

61 -

65

66 -

70

71 -

75

76 -

80

81 -

85

Per

cen

tage

Age Range

Age Distribution by Race Coding

Irregular

Regular

207

Figure A-2. Association between race miscoding and type of insurance plan

Table A-1. Distribution of fusion procedure claims, stratified by the claim setting

Fusion Procedure Codes Present Total

n (%)

Provider Only 24,882 (14.6)

Institutional Only 11,382 (6.7)

Both Provider and Institutional 133,938 (78.7)

Table A-2. Assessment of complimentary concurrent codes

Fusion Procedure Codes Present Complementary Concurrent

Codes Present n (%)

Provider Only 2752 (11.2)

Institutional Only 2898 (25.5)

0

10

20

30

40

50

60

70

80

90

100

Medicaid Medicare Commercial

Per

cen

tag

e Race Distribution by Insurance Type

!

Asian

Black

Hispanic

Other

Unknown

White

208

Table A-3. Analysis of the encounter type variable, stratified by insurance type

Encounter Type Insurance Plan Total

n (%) Missing Commercial Medicaid Medicare

-1 (Missing)

n 4974 1543637 0 0 1,548,611

(20.8) Row % 0.32 99.68 0 0

Column % 46.23 68.9 0 0

AV

(Ambulatory

Visit)

n 4375 303973 24270 3063031 3,395,649

(45.6) Row % 0.13 8.95 0.71 90.2

Column % 40.66 13.57 41.01 59.69

IP (Inpatient

Hospital Stay)

n 831 303042 30168 1700936 2,034,977

(27.3) Row % 0.04 14.89 1.48 83.59

Column % 7.72 13.53 50.98 33.14

IS ( Non-

Acute

Institutional

Stay)

n 215 61081 4743 199149 265,188

(3.6) Row % 0.08 23.03 1.79 75.1

Column % 2 2.73 8.01 3.88

OA (Other

Ambulatory

Visit)

n 365 28790 0 168777 197,932

(2.7) Row % 0.18 14.55 0 85.27

Column % 3.39 1.28 0 3.29

Total

n (%) 10760

(0.1)

2240523

(30.1)

59181

(0.8)

5131893

(69.0)

209

Table A-4. Analysis of the place of service variable, stratified by insurance type

Place of Service

Insurance Plan Total


-1 (Missing)

n 4 205 0 0 209

(0.0) Row % 1.91 98.09 0 0

Column % 0.04 0.01 0 0

-3 (Invalid Value

Submitted)

n 9 0 1790 0 1799

(0.02) Row % 0.5 0 99.5 0

Column % 0.08 0 3.02 0

A (Acute

Inpatient Health

Care Facility)

n 2157 512942 30786 1928247 2474132

(33.2) Row % 0.09 20.73 1.24 77.94

Column % 20.05 22.89 52.02 37.57

B (Acute Care

Outpatient

Facility)

n 5892 1039365 18630 2680649 3744536

(50.3) Row % 0.16 27.76 0.5 71.59

Column % 54.76 46.39 31.48 52.24

C (Independent

Laboratory)

n 498 248699 632 0 249829

(3.4) Row % 0.2 99.55 0.25 0

Column % 4.63 11.1 1.07 0

D (Inpatient

Hospice)

n 25 4490 0 7345 11860

(0.2) Row % 0.21 37.86 0 61.93

Column % 0.23 0.2 0 0.14

E (Inpatient

Mental

Health/Chem Dep

Facility)

n 6 4440 0 0 4446

(0.1) Row % 0.13 99.87 0 0

Column % 0.06 0.2 0 0

G (Long-Term

Care Facility)

n 73 27408 1207 205166 233854

(3.1) Row % 0.03 11.72 0.52 87.73

Column % 0.68 1.22 2.04 4

H (Non Acute-

Care Outpatient

Facility)

n 4 0 0 5525 5529

(0.1) Row % 0.07 0 0 99.93

Column % 0.04 0 0 0.11

I (Office/Clinic) n 431 117724 617 96542

215314

(2.9) Row % 0.2 54.68 0.29 44.84

Column % 4.03 5.26 1.05 1.99

210

Table A-4. Continued

Place of Service

Insurance Plan Total


K (Other

Outpatient Place

Of Service)

n 1053 164008 2920 208419 376400

(5.1) Row % 0.28 43.57 0.78 55.37

Column % 9.79 7.32 4.93 4.06

UNK (Unknown)

n 608 121242 2599 0 124449

(1.7) Row % 0.49 97.42 2.09 0

Column % 5.65 5.41 4.39 0

Total

n (%) 10760

(0.1)

2240523

(30.1)

59181

(0.8)

5131893

(69.0)

Table A-5. Analysis of the revenue code variable, stratified by insurance type

Revenue Code Insurance Plan Total


Valid n 76,169 4,460,157 124,047 12,530,488 17,190,861

Row % 0.44 25.94 0.72 72.89 (84.2)

Column % 88.23 58.46 75.44 100

Missing n 10,163 3,169,074 40,378 115 3,219,730

Row % 0.32 98.43 1.25 0 (15.8)

Column % 11.77 41.54 24.56 0

Total

n (%)

86,332 7,629,231 164,425 12,530,603 20,410,591

(0.4) (37.4) (0.8) (61.4)

211

APPENDIX B

CASE DEFINITIONS AND RELATED BILLING CODES

Table B-1. Procedure codes used to identify spinal fusion surgeries, stratified by region and

fusion intent

Spinal Region Fusion Intent Codes

Cervical Primary ICD-9-CM: 81.01, 81.02, 81.03

CPT-4: 22548, 22551, 22554, 22590, 22600, 22595

Revision ICD-9-CM: 81.31, 81.32, 81.33

Thoracolumbar Primary

ICD-9-CM: 81.04, 81.05

CPT-4: 22532, 22556, 22610

Revision ICD-9-CM: 81.34, 81.35

Lumbar Primary

ICD-9-CM: 81.06, 81.07, 81.08

CPT-4: 22533, 22558, 22612, 22630, 22633

Revision ICD-9-CM: 81.36, 81.37, 81.38

Unspecified Region

Primary ICD-9-CM: 81.00

Revision ICD-9-CM: 81.30, 81.39

Table B-2. Diagnostic codes used to identify degenerative conditions of the lumbar spine

Condition Codes

Hierarchical

Category

Scoliosis ICD-9-CM: 737.3X 737.10, 737.11, 737.19,

737.20, 737.29, 737.43, 737.8, 737.9 1

Listhesis ICD-9-CM: 738.4, 756.11, 756.12 2

Stenosis ICD-9-CM: 724.00, 724.02, 724.03, 724.09 3

Herniated discs ICD-9-CM: 722.10, 722.2, 353.9, 355.0, 355.9,

722.70, 722.73, 724.3 4

Degenerative Disc Disease ICD-9-CM: 722.5, 722.52, 722.6, 5

Back Pain (includes

unspecified disc disorders

and Lumbago)

ICD-9-CM: 722.90, 722.93, 724.2, 724.5 6

Strains/Sprains ICD-9-CM: 846.0, 847.2, 847.9 7

†Degenerative conditions of the lumbar spine were categorized and ordered based on how

well spinal surgery is believed to treat these conditions.

212

Table B-3. Diagnostic codes used to identify non-degenerative conditions of the lumbar

spine

Condition Codes

Spinal Fracture/Dislocation

ICD-9-CM: 733.10, 733.13, 733.8, 733.81, 733.82,

733.95, 805.4, 805.5, 805.8, 805.9, 806.4, 806.5, 806.8,

806.9, 839.2, 839.3, 839.4, 839.5, 839.6, 839.7, 839.8,

839.9, 905.1, V54.27

Spinal Cord Injury ICD-9-CM: 336.9, 952.2, 952.9, 953.2

Congenital or other spinal

anomalies

ICD-9-CM: 324.1, 344.60, 721.5, 721.6, 721.7, 722.30,

722.32, 724.6, 727.40, 733.20, 739.3, 739.4, 741.90,

756.10, 756.13, 756.14, 756.15, 756.16, 756.17, 756.19

Inflammatory Spondylopathy ICD-9-CM: 720.X

Osteoporosis ICD-9-CM: 733.0X, V17.81, V82.81

Table B-4. Diagnostic codes used to identify degenerative conditions of the spine

Condition Codes

Hierarchical

Category

Strains/Sprains ICD-9-CM: 846.0, 846.1, 846.2, 846.3, 846.8,

846.9, 847.9, 847.2, 847.0 1

Back Pain includes

unspecified disc disorders,

lumbago and back pain

NOS

ICD-9-CM: 723, 723.8 ,723.1, 721.0, 721.1, 721.2,

721.3, 721.4, 721.7, 721.8 , 721.9, 721.90,721.91,

722.90, 722.92 ,722.91, 722.93, 724.2, 724.5, 724.6,

724.70, 724.71, 724.79, 724.8, 724.9

2

Degenerative Disc Disease ICD-9-CM: 722.4, 722.5, 722.51, 722.52, 722.6 3

Herniated discs

ICD-9-CM: 722.0, 722.10, 722.11, 722.2, 353.9,

355.0, 355.9, 722.70, 722.71, 722.72, 722.73, 724.3,

721.4, 724.4

4

Stenosis ICD-9-CM: 721.42, 721.91, 724.00, 724.02, 724.09,

723.0,724.01 5

Listhesis ICD-9-CM: 738.4, 756.11, 756.12 6

Scoliosis

ICD-9-CM: 737, 737.1, 737.10, 737.19, 737.20,

737.3, 737.30, 737.32, 737.34, 737.39, 737.43,

737.8, 737.9,

7

†Degenerative conditions of the spine were categorized and ordered based on how well spinal

surgery is believed to treat these conditions.

213

Table B-5. Diagnostic codes used to identify non-degenerative conditions of the spine

Condition Codes

Spinal Fracture/Dislocation ICD-9-CM: 805.X, 806.X, 839.X, 733.1, 733.10, 733.13,

733.8, 733.81, 733.82, 733.95, 905.1, V54.17, V54.27

Spinal Cord Injury ICD-9-CM: 336.9, 952.0, 952.00, 952.03, 952.05, 952.09,

952.10, 952.9, 953.0, 952.04

Congenital or other spinal

anomalies

ICD-9-CM: 324.1, 344.60, 721.5, 721.6, 721.7, 722.30,

722.32, 724.6, 727.40, 733.20, 739.3, 739.4, 741.90,

756.10, 756.13, 756.14, 756.15, 756.16, 756.17, 756.19

Inflammatory Spondylopathy ICD-9-CM: 720.X

Osteoporosis ICD-9-CM: 733.0X, V17.81, V82.81

Table B-6. Diagnostic codes used to identify cancer-related health care encounters,

stratified by the organ system affected

Category Codes

Bones/soft tissue ICD-9-CM: 170.X, 171.X, 238.1, 238.2

Brain ICD-9-CM: 190–192.9, 237.5, 237.6, 239.6

Breast ICD-9-CM: 174.X, 175.X, 239.3

Colon ICD-9-CM: 153.X, 154.X, 235.2

Endocrine ICD-9-CM: 193, 194.X, 237.0, 237.4, 239.7

Gynecological ICD-9-CM: 180.X, 182.X, 183.X, 184.X, 236.1, 236.2

Head and neck ICD-9-CM: 140–149.9, 160.X, 161.X, 162.X, 195.0

Lung ICD-9-CM: 162.X, 235.9, 239.1

Non-colon Gastrointestinal ICD-9-CM: 150–152.9, 155–159.9, 235.X, 239.0

Pleura/mediastinum ICD-9-CM: 163.X, 164.X

Prostate ICD-9-CM: 185.X, 236.5

Skin ( Melanoma) ICD-9-CM: 173.X, 238.2

Skin (Melanoma) ICD-9-CM: 172.X

Testes/Male Genitourinary ICD-9-CM: 186.X, 187.3, 187.4, 187.9, 236.4, 236.6

Urinary Tract ICD-9-CM: 188.X, 189.X, 236.7, 236.91, 239.4, 239.5

Non-specific site ICD-9-CM: 195.X, 199.X, 238.8, 238.9, 239.8, 239.9

Lymph node spread ICD-9-CM: 196.X

Secondary cancer ICD-9-CM: 196.X, 197.X

214

Table B-7. Major oral pharmacologic treatments use for chronic back pain

Active Ingredient Equianalgesic Doses

Parenteral Oral

Morphine214

(Reference) 10 mg 30 mg

Buprenorphine

214,215 0.4mg 0.3 (SL)

Butorphanol214,215

2 mg -

Codeine214,215

100mg 165mg

Fentanyl214

0.1mg 0.2 mg (TM), 0.8 (BC)

Hydrocodone214

- 30 mg

Hydromorphone214

1.5 mg 7.5 mg

Levorphanol215

- 4 mg

Meperidine214

100 mg 300 mg

Nalbuphine215

10 mg -

Oxycodone215

- 20 mg

Oxymorphone214

1 mg 10 mg

Pentazocine215

60 mg 150 mg

Propoxyphene† - 130mg-200mg

Tapentadol215

- 100 mg

Tramadol214

100 mg 120 mg

Methadone198,215

Ayorinde Algorithim

*Use Active Ingredient- NDC crosswalk software to obtain National Drug

Codes.

TM: Transmucosal, TD: Transdermal, SC: Subcutaneous, IV: Intravenous,

BC: Buccal, SL: Sublingual

†130mg (Hydrochloride); 200mg (Napsylate)

215

APPENDIX C

EXPLORATION OF MODEL ASSUMPTIONS

Part IIA: Association between rhBMPs use and subsequent refusion procedures

Part IIA of this dissertation examined the association between rhBMP use and the

incidence of refusion procedures during follow-up. The models used to examine this question,

hazard regression analyses, were built on one central assumption: proportionality of hazards. The

proportional hazard assumption states that ratio of hazards does not change over time. For this

and all subsequent time to event analyses undertaken in this dissertation, we used three methods

to test this assumption, namely, covariate-time interaction analysis, the score test and the log (-

log (survival) versus log (time) plot visual inspection approach.

Examination of the proportionality assumption using the three methods listed produced

divergent results. In particular, results from the log (-log (survival) versus log (time) approach,

which suggested that the proportionality assumption was violated, contradicted the conclusions

derived from both the score test and the covariate-time interaction method. A summary of these

results is presented in Table C-1.

In order to quantify the effect of violating the proportional hazard assumption on our

study conclusions, we plotted the calculated association between rhBMP use and the incidence of

refusion risk as a function of time using the Equation C-1.

HR (t) = exp([β1 + β2t])

(C-1)

Where:

)(ti : Subject i’s hazard of having a revision procedure at time t


β1: Parameter estimate for the rhBMP use status

β2: Parameter estimate for the interaction between rhBMP status and time

216

As shown in Figures C-5, C-7 and C-8, the estimated hazard ratio summarizing the

association between rhBMP exposure and the risk for refusion procedures within the any

degenerative condition, Stenosis and Listhesis was not sensitive to changes in follow-up time.

The apparent stability of these point estimates suggests that the proportionality of hazards

assumption holds in these cohorts. On the hand, the hazard ratio estimate appears to shift

downwards within the LDDD cohort (Figure C-6) with an increase in the follow up time which

indicates that the hazard ratio is time-dependent. However, since the hazard ratio of 1 is

contained within the confidence interval band throughout our observation period then the

conclusions of the study hold true. In other words, while the relationship between rhBMP use

during LDDD-indicated fusion procedures and risk for refusion procedures may vary based on

the duration the follow up, this analysis of 1120 patients followed over a median follow-up of 20

months found no statistically significant evidence of an association during the 3.5 years of

observation.

217

Table C-1. Summary of proportionality assumption test (refusion risk analysis)

Study

Population Test Test Results Interpretation

Any

Degenerative

Condition

Score Test p value = 0.958 Assumption Confirmed

Time Dependent Covariate Test HR (95% CI): 0.95 (0.69, 1.30), p = 0.73 Assumption Confirmed

log(-log(survival) versus log(time) Figure C-1 Assumption Violated

Lumbar

Degenerative

Disc Disease



log(-log(survival) versus log(time) Figure C-2 Assumption Violated

Stenosis



log(-log(survival) versus log(time) Figure C-3 Inconclusive

Listhesis


Time Dependent Covariate Test HR (95% CI): 0.98 (0.64, 1.51), p =0.93 Assumption Confirmed

log(-log(survival) versus log(time) Figure C-4 Inconclusive

218

Figure C-1. Proportionality assumption test for rhBMP-refusion risk assessment model (any

degenerative condition cohort)

219

Figure C-2. Proportionality assumption test for rhBMP-refusion risk assessment model (LDDD

cohort)

220

Figure C-3. Proportionality assumption test for rhBMP-refusion risk assessment model (Stenosis

cohort)

221

Figure C-4. Proportionality assumption test for rhBMP-refusion risk assessment model (Listhesis

cohort)

222

Figure C-5. Effect of the rhBMP use on refusion risk as a function of time (any degenerative

condition cohort)

223

Figure C-6. Effect of the rhBMP use on refusion risk as a function of time (LDDD cohort)

224

Figure C-7. Effect of the rhBMP use on refusion risk as a function of time (Stenosis cohort)

225

Figure C-8. Effect of the rhBMP use on refusion risk as a function of time (Listhesis cohort)

226

Part IIB: Effect of rhBMP use on Post-Discharge Hospitalization Patterns

Part of our investigation into the effect of rhBMP use on readmission risks examined the

effect of the osteobiologic on the time to the first LDDD-related readmission. The analysis used

the proportional hazard approach, which as the name suggests assumes that the estimated hazard

ratio is constant over time. We used three methods to test the proportionality of hazards

assumption namely time interaction analysis, the score test and the log (-log (survival) versus log

(time) plot visual inspection approach whose results are summarized in Table C-2.

As was the case in Part IA of this dissertation, the results of the three, proportionality of

hazards assumption tests provided different conclusions. We used Equation C-1 to plot the effect

of rhBMP use on LDDD-related readmission risks as a function of time for each of the three

cohorts analyzed. As shown in Figures C-12, C-13 and C-14, the duration of follow up appears

to affect the point estimate of the association but not the conclusions of the analysis. However,

the plots indicate that the association between rhBMP use and LDDD-related readmission risks

was not statistically significant throughout the duration of follow-up in this study.

227

Table C-2. Summary of proportionality assumption test (readmission risk analysis)


Identification Method Test Test Results

Assumption

Interpretation


Score Test p value = 0.261 Holds

Time Dependent Covariate Test HR (95% CI): 1.00 (0.99, 1.00) , p = 0.28 Holds

log(-log(survival) versus log(time) Plot Figure C-9 Violated

LDDD Diagnostic Code

listed as Primary

Diagnosis


Time Dependent Covariate Test HR (95% CI): 1.00 (0.99, 1.00) , p = 0.89 Holds


LDDD Diagnosis listed at

any position on the

Procedure Claim


Time Dependent Covariate Test HR (95% CI): 0.99 (0.99, 1.00) , p =0.37 Holds


228

Figure C-9. Proportionality assumption test for rhBMP-readmission risk assessment model

(hierarchical definition cohort)

229


(primary diagnosis definition cohort)

230


(comprehensive definition cohort)

231

Figure C-12. Effect of the rhBMP use on LDDD- related readmission risk as a function of time

(hierarchical algorithm cohort)

232


(primary diagnosis definition cohort)

233


(comprehensive definition cohort)

234

Part IID: Effect of rhBMP use on Changes in Opioid Analgesic Use

The association between intraoperative rhBMP use and opioid analgesic dose changes before

and after the index fusion procedure was assessed using the Analysis of Covariance Model

(ANCOVA). The interpretation of the ANCOVA model relies on the assumption that the effect

of the continuous covariate is statistically equivalent across all levels of the categorical predictor

variable. In our case, we used the T-Test to confirm that the effect of the propensity score was

independent of the patient’s rhBMP exposure status (Figure C-15, p value =0.12). The

homogeneity of regression slopes assumption was tested by including an interaction term

between the categorical (rhBMP exposure status) and the continuous predictors (propensity

score) in the regression. A non-significant interaction term was used as evidence that the

regression slopes were statistically comparable (Figure C-16, Baseline- First Outcome

Assessment Window: p value = 0.42; Figure C-17, Baseline- Second Outcome Assessment

Window: p value = 0.58).

235

Figure C-15. ANCOVA model independence of predictors’ assumption test

236

Figure C-16. ANCOVA model homogeneity assumption test (first post-procedure evaluation window)

237

Figure C-17. ANCOVA model homogeneity assumption test (second post-procedure evaluation window)

238

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BIOGRAPHICAL SKETCH

Irene Berita Murimi was born and raised in Mombasa, Kenya. She is a graduate of

Alliance Girls High School, the Massachusetts Institute of Technology and Yale University.

Ms. Murimi has participated in the teaching of Pharmacy students since her arrival at the

University of Florida. In 2013, she was selected as one of the lead Teaching Assistants in the

University. She has also been honored with Outstanding International Student Award conferred

by the International Students Office at the University of Florida. During her doctoral studies,

Ms. Murimi was a recipient of the Alumni Fellowship and the Oak Ridge Science and Education

Fellowship.

Ms. Murimi is actively involved with the International Society for

Pharmacoepidemiology and Risk Management, serving Chair of the Medical Device Special

Interest Group (SIG). Through the professional society, she has helped coordinate symposium

and workshops on Medical Device and Combination Products Epidemiology.

Documents

COMPARATIVE SAFETY AND EFFECTIVENESS OF ...ufdcimages.uflib.ufl.edu/UF/E0/04/92/80/00001/MURIMI_I.pdfCOMPARATIVE SAFETY AND EFFECTIVENESS OF TREATMENTS FOR DEGENERATIVE DISC DISEASE: