Ryan Ke l lyDr. N icolas Shammas

Chr ist ine BeuthinJackie Car lson

Mart i CoxKathy LenaghanDr. Ram NiwasDr. Jon Lemke

06/18/15

ASSESSMENT OF TIME TO HOSPITAL READMISSIONS AND NUMBER OF

HOSPITAL PATIENT DAYS AFTER AN INITIAL HOSPITALIZATION FOR HEART

FAILURE

Genesis Research SummitWITH DISCUSSION ADDENDUM

The phases of this project include:

Identify patient population

Gather and consolidate patient information

Use Medicare models to determine relevant risk factors

Assess effects of risk factors on time to readmission and hospitalized days after index visit

PROJECT PHASES

Kelly, R. et al. June 18, 2015

Included Patients Medicare Patients

500 Admissions for Heart Failure 10/1/2011 – 9/30/2013 Index Diagnosis of Heart Failure in Study Time Frame GMC-Davenport or GMC-Silvis Reside in 17 County Service Area Results in 166 Patients in Final Population

Record all Patient Hospital Visits (Inpatient, Outpatient, Outpatient Observation, ED) from Index Hospitalization until 12/31/2013

Censor Patient Upon Death, Readmission Outside GHS, Elective Procedure, or AMA

STUDY DESIGN

Kelly, R. et al. June 18, 2015

Mean Age 79.3 Years

SexWomen: 76 (46%)Men: 90 (54%)

Type of Heart Failure

Diastolic: 55 (33%)Systolic: 55 (33%)Both: 11 (7%)Unspecified: 45 (27%)

Site of Index VisitDavenport: 102 (61%)Silvis: 64 (39%)

PATIENT DEMOGRAPHICS

Kelly, R. et al. June 18, 2015

5004003002001000

90+

80

70

60

50

Days Since Index Admission

Age

At Vis

it

EmergencyInpatientOutpatient

Admit Type

Follow Up Visits for 5 Sample Patients

Other Hospital

Expired

Elective

CutoffStudy

Left AMA

Kelly, R. et al. June 18, 2015

8007006005004003002001000

100

80

60

40

20

0

Mean 213.869Median 117IQR 299

Table of Statistics

Days After Initial Discharge

Perc

ent of

Pat

ient

s no

t Rea

dmitte

d

30

75

50

25

10

Days Until Inpatient Readmission after Index Hospitalization

Censored at Death, AMA, or Dec. 31, 2013Kaplan-Meier Method - 95% CI

Kelly, R. et al. June 18, 2015

8007006005004003002001000

100

80

60

40

20

0

Mean 126.708Median 27IQR 151

Table of Statistics

Days After Initial Discharge

Perc

ent of

Pat

ient

s no

t Rea

dmitte

d

30

25

50

75

10

Days Until Follow-Up Visit after Index HospitalizationKaplan-Meier Method

Censored at Death, AMA, or Dec. 31, 2013

Kelly, R. et al. June 18, 2015

Can we use PoA diagnoses at index visit to predict readmission and hospitalized days? CMS – Yale Models (30 day readmissions)

34 Groupers of Related ICD-9 Codes, Plus Age and Sex Does the type of Heart Failure and the medications given

significantly affect patient outcomes?

Univariate Analysis Split population based on inclusion in one of 34 groupers

For example, compare 46 anemic patients vs. 112 non-anemic patients

Multivariate Analysis Utilize all diagnoses, age, sex, and other predictors to

create comprehensive model

RISK FACTORS AND MODELING

Kelly, R. et al. June 18, 2015

Grouper Odds Ratio

Renal failure 1.20Severe hematological

disorders 1.18

Chronic obstructive pulmonary disease 1.17

Metastatic cancer or acute leukemia 1.16

Congestive heart failure 1.13Disorders of fluid,

electrolyte, acid-base 1.13

Acute coronary syndrome 1.12

End stage renal disease or dialysis 1.12

Cardio-respiratory failure or shock 1.11

MEDICARE 30 DAY READMISSION MODEL (YALE)

Grouper Odds Ratio

Pneumonia 1.11Diabetes or DM complications 1.10

Iron deficiency or other anemias and blood

disease1.10

Drug/alcohol abuse/dependence/psych

osis1.10

Protein-calorie malnutrition 1.09

Decubitus ulcer or chronic skin ulcer 1.09

Liver or biliary disease 1.08

Nephritis 1.08Vascular or circulatory

disease 1.07

Peptic ulcer, hemorrhage, other specified GI

disorders1.07

Kelly, R. et al. June 18, 2015

Grouper Odds Ratio

Other psychiatric disorders 1.07

Other urinary tract disorders 1.07

Coronary atherosclerosis or

angina1.06

Specified arrhythmias 1.06Major psychiatric

disorders 1.06

Other GI disorders 1.05Fibrosis of lung or other chronic lung disorders 1.05

Valvular or rheumatic heart disease 1.04

Other or unspecified heart disease 1.04

MEDICARE 30 DAY READMISSION MODEL (YALE)

Grouper Odds Ratio

Hemiplegia, paraplegia, paralysis, functional

disability1.04

Stroke 1.03Dementia or other

specified brain disorders 1.02

Depression 1.02

Asthma 1.01

Male 1.01

Cancer 1.00

Age > 65 1.00

Kelly, R. et al. June 18, 2015

8007006005004003002001000

100

80

60

40

20

0

144.541 37 16870.444 21 43

Mean Median I QRTable of Statistics

Days After Initial Discharge

Perc

ent

of Patients

not

Readm

itte

d

30

75

50

25

10

No COPDCOPD PoA

COPD

Days Until Follow-Up Visit after Index HospitalizationBy COPD Status (p = 0.09)

Censored at Death or Dec. 31, 2013

Kelly, R. et al. June 18, 2015

8007006005004003002001000

100

80

60

40

20

0

109.863 21 123177.342 42 256

Mean Median I QRTable of Statistics

Days After Initial Discharge

Perc

ent

of Patients

not

Readm

itte

d

30

75

50

25

10

No DiseaseValv./ Rheum. Disease PoA

Valvular or Rheumatic Disease

Days Until Follow-Up Visit after Index HospitalizationBy Valvular or Rheumatic Heart Disease Status (p = 0.09)

Censored at Death, or Dec. 31, 2013

Kelly, R. et al. June 18, 2015

Time to Readmission Model including risk factors

Model 30-Day Readmission Rate Probability of 30-Day readmission from the CMS model All encounters, and Inpatient Readmissions only

Poisson regression for number of hospitalized days after index Weighted by number of days at risk, and controlled

for age and sex

Ultimate goal to reduce cost per covered life through use of accurate models

MULTIVARIATE MODELING

Kelly, R. et al. June 18, 2015

RESULTS

0.40.30.20.10.0

0.4

0.3

0.2

0.1

0.0

CMS Probability 30-Day Readmission

Fitted

Pro

bab

ility

30

Day

Inpat

ient

Rea

dm

ission

Fitted Probability of 30-Day Inpatient Readmission

p = 0.024vs CMS Probability

Kelly, R. et al. June 18, 2015

RESULTS

0.70.60.50.40.30.20.10.0

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0.0

CMS Probability 30-Day Readmission

Fitted

Pro

bab

ility

30

Day

Rea

dm

ission

Fitted Probability 30-Day Encounter

p = 0.098vs CMS Probability

Kelly, R. et al. June 18, 2015

Model for Hospitalized Days with Age, Sex, Type of Heart Failure, Medication, and Each of 34 groupers Explained variation Lung Fibrosis and Eligibility for ACEI/ARB were both

significant

RESULTS

Indicated Eligible

ACEI/ARB?

With Lung Fibrosis?

Hospitalized Days

Days at Risk Rate

Yes Yes 33 106 31.1 per 100

Yes No 219.5 15902 1.4 per 100

No Yes 0.5 251 0.2 per 100

No No 311.5 8933 3.5 per 100Kelly, R. et al. June 18, 2015

Genesis announced they would begin CMS HF Bundled Payments on July 1st of this year These bundled payments include the index admission,

as well as all costs during 90 day episodes of care

A brief summary of our 166 patient population showed 287 follow up visits (1.7 per patient) 1158 Total Hospitalized Days

2.6 per visit 7.0 per patient 8.6 hospitalized days per 100 at risk

21 deaths (12.7%)

CMS HF BUNDLED PAYMENTS

Kelly, R. et al. June 18, 2015

0.350.300.250.200.150.100.050.00

2.5

2.0

1.5

1.0

0.5

0.0

CMS 30-Day Probability Readmission

Expe

cted

90

Day

Fol

low

-Up

Enco

unte

rs

90-Day Follow-Up Encounters

p = 0.015vs CMS Probability 30-Day Readmission

90-DAY EPISODES OF CARE

Kelly, R. et al. June 18, 2015

90-DAY EPISODES OF CARE

0.350.300.250.200.150.100.050.00

14

12

10

8

6

4

2

0

CMS Probability 30-Day Readmission

Fitt

ed H

ospi

taliz

ed D

ays

90-Day Episodes Hospitalized Days

p = 0.168 vs CMS Probability 30-Day Readmission

Kelly, R. et al. June 18, 2015

Further explore the potential diff erences in outcomes between patients with diastolic or systolic heart failure

Fine tune multivariate models used to predict readmissions

Develop graphical representations of the final models.

WHAT IS NEXT

Kelly, R. et al. June 18, 2015