Association Between Early Follow-up with a Nephrologist

and Death in Survivors of Acute Kidney Injury

By

Ziv Harel

A thesis submitted in conformity with the requirements

for the degree of Master’s of Science

Graduate Department of Health Policy Management and Evaluation

University of Toronto

© Copyright by Ziv Harel 2012

ii

Association Between Early Follow-up with a Nephrologist and

Death in Survivors of Acute Kidney Injury

Ziv Harel

Master’s of Science

Department of Health Policy, Management and Evaluation

University of Toronto

2012

Abstract

Background: Survivors of severe acute kidney injury remain at high risk of death well-after

apparent recovery from the initial event.

Methods: We conducted a cohort study of hospitalized adults in Ontario from 1996 to 2008 with

acute kidney injury who received temporary dialysis and survived for 90 days following

discharge independent from dialysis. The exposure was nephrology follow-up. We used

propensity scores to match individuals with early nephrology follow-up to those without. The

primary outcome was time to mortality.

Results : We identified 3877 patients with acute kidney injury who met the eligibility criteria. A

total of 1583 patients had nephrology follow. The incidence of all-cause mortality was lower in

those with early nephrology follow-up as compared to those without early follow-up (8.4 vs. 10.6

per 100 person-years, HR 0.76 (95% CI 0.62-0.93)).

Conclusions: Nephrology follow-up after hospitalization with acute kidney injury and temporary

dialysis was associated with improved survival.

iii

Acknowledgments

I am extremely grateful to my primary supervisor Ed Etchells, and my co-supervisors Chaim Bell

and Ron Wald for their continued guidance, inspiration, and encouragement through this process.

Their mentorship, intelligence, enthusiasm and friendship made this a truly enjoyable experience.

I would also like to thank my committee members Muhammad Mamdani and Joanne Bargman,

as well as Amit Garg, Tara Gomes, Rob Quinn, Peter Austin and Donald Redelmeier for the

knowledge and expertise they shared with me during these 2 years.

This work was supported by a Canadian Institute of Health Research Master’s Award as

well as by the Clinician Scientist Program at the University of Toronto. The funding, and

educational components provided by these programs were invaluable in allowing me to complete

this work.

Finally, I would like to profoundly thank my family for their support and encouragement

not only throughout this Master’s but my entire academic career. I would not be where I am

without them.

iv

Table of Contents

Abstract ii

Acknowledgments iii

List of Tables vi

List of Figures vii

List of Appendices viii

Introduction 1.1 Study Rationale and Study Question 1

1.2 Hypothesis 1

Background

2.1. Epidemiology of Acute Kidney Injury (AKI) 2.1.1. Definition and Classification of Acute Kidney Injury 2

2.1.2. Prevalence of AKI in Hospitalized Patients 4

2.1.3. Short-term Outcomes of AKI 4

2.1.4. Methodological Issues in Studying the Long-term Outcomes of AKI 5

2.1.5. Long-term Outcomes of AKI 7

2.2. Pathophysiology of Acute Kidney Injury

2.2.1. Renal and Non-Renal Effects of AKI and their Relationship with Mortality 12

2.3 Potential Impact of Nephrologist Follow-up After Hospitalization with Severe AKI 14

Methods 3.1 Overview of Study Design 16

3.2 Description of Data Sources and Validity

3.2.1. Canadian Institute for Health Information Discharge Abstract Database 17

3.2.2. Ontario Health Insurance Premium Database 18

3.2.3. Registered Persons Databases 18

3.2.4. Canadian Organ Replacement Registry 18

3.2.5. Canadian Census Database 19

3.2.6. Ottawa Warehouse Database 19

3.2.7. CERNER Database 19

3.3 Cohort Definition

3.3.1. Inclusion criteria 20

3.3.2. Exclusion criteria 20

3.3.3. Justification of Cohort Selection (Inclusion and Exclusion Category) 22

3.4 Covariates and Outcome Variables

3.4.1. Primary Exposure: Nephrologist Consultation Within 90 Days of Discharge 23

3.4.2. Primary Outcome: All-Cause Mortality 25

3.4.3. Secondary Outcome: Receipt of Chronic Dialysis 25

3.5 Statistical Methods

3.5.1 Propensity Score Modelling 25

3.5.1.1. Specifying the Propensity Score 26

3.5.1.2. Matching on the Propensity Score 26

3.5.1.3. Assessing Balance Between Treated and Untreated Subjects 27

3.5.1.4. Estimation of the Treatment Effect 27

3.6 Subgroup Analysis 28

3.7 Sensitivity Analyses 28

3.7.1. High Dimensional Propensity Score 29

3.8 Additional Analyses

v

3.8.1. Healthcare Utilization 30

3.8.2. Unmatched Cohort 30

Results 4.1 Description of the Cohort 31

4.2 Outcomes 35

4.3 Subgroups 38

4.4 Sensitivity Analyses

4.4.1. Patients with a Discharge Serum Creatinine Measurement 41

4.4.2. Dialysis Censored Cohort 44

4.4.3. High Dimensional Propensity Score 44

4.4.4. Test of Specificity 45

4.5 Additional Analyses

4.5.1. Healthcare Utilization 46

4.5.2. Unmatched Cohort 47

Discussion 5.1 Major Findings 49

5.2 Interpretation 50

5.3 Strengths 54

5.4 Limitations 55

5.5 Implications and Future Directions 58

Conclusion 59

References 60

Appendices 68

vi

List of Tables

Table 1: The RIFLE criteria for acute kidney injury……………………………….... 3

Table 2: The AKIN criteria for acute kidney injury……………………………….…. 4

Table 3: Comparison of Two Algorithms for Defining Nephrologist………………... 24

Table 4: Baseline Characteristics of Cohort (Pre-Match)………………….……….... 33

Table 5: Baseline Characteristics of Cohort (Post-Match)………………..…………. 34

Table 6: Risk of All-Cause Mortality and Chronic Dialysis by Group ……………… 35

Table 7: Risk of All-Cause Mortality Stratified by Subgroup………………..……..... 39

Table 8: Baseline Characteristics of Patients with Discharge Creatinine

Values………………………………………………………………..…............. 42

Table 9: Characteristics of Patients with Discharge Creatinine Values

Post-Matching......................................................................................................43

Table 10: Additional Covariates Specified by the High Dimensional Propensity

Score……………………………………………………………………….…... 45

Table 11: Risk of All-Cause Mortality and Chronic Dialysis for Tracer Exposure.….46

Table 12: Characteristics of Post-Discharge Health Services Utilization and

Procedures in the Matched Cohort…………………………………………..47

Table 13: Comparison of Matched and Unmatched Cohorts…………………………. 48

vii

List of Figures

Figure 1: Creation of the Acute Kidney Injury Survivor Cohort……………………. 21

Figure 2: Comparison of High Dimensional Versus Traditional Propensity Score

Modeling……………………………………………………………………… 30

Figure 3: Risk of All-Cause Mortality in Association with Early Nephrology

Follow-up…………………………………………………………………....... 36

Figure 4: Risk of Chronic Dialysis in Association with Early Nephrology

Follow-up…………………………………………………………………….. 37

Figure 5: Association of Nephrology Follow-up with All-Cause Mortality

Within Subgroups…………………………………………………………… 40

viii

List of Appendices

Appendix 1: Diagnostic and Procedural Codes Used to Identify the Cohort, Exposure,

Comorbidities, and Outcomes………………………………………………68

Appendix2: Covariates Used in the Development of the Propensity Score……………72

1

Chapter 1

Introduction

The purpose of this chapter is to:

Describe the study rationale and study question.

1.1 Rationale and Study Question

Acute kidney injury (AKI) is an increasingly common and serious complication in hospitalized

patients.1;2

Even after discharge, survivors of hospitalizations that are complicated by AKI are

at higher risk of dying and are more likely to develop progressive chronic kidney disease

(CKD).3-6

These complications may occur even if renal recovery is ostensibly complete at the

time of the initial discharge.7 The first 3 to 24 months following an episode of AKI represent the

time of greatest risk for the long-term complications of AKI.8 As such, specialized nephrology

care during this crucial window of time may prevent poor outcomes. However, only 40% of

patients hospitalized with an episode of AKI see a nephrologist within the first year.9 This may

represent a missed opportunity for intervention.

Despite the theoretical benefits of nephrology follow-up in survivors of AKI, little is

known about the effect that this intervention may have on the long-term complications of AKI.

Accordingly, we undertook a population-based cohort study in Ontario, Canada, to determine

whether early follow-up with a nephrologist in survivors of severe AKI is associated with

reduced mortality and progression to chronic dialysis.

1.2 Hypothesis

It is hypothesized that follow-up with a nephrologist within 90 days of discharge is associated

with a decrease in mortality within 2 years of the index date, and a decrease in the need for

chronic dialysis.

2

Chapter 2

Background

The purpose of this chapter is to:

Review the epidemiology of acute kidney injury;

Describe the pathophysiological effects of acute kidney injury;

Discuss the potential impact of nephrologist follow-up in survivors of acute kidney injury

requiring temporary dialysis.

2.1. Epidemiology of Acute Kidney Injury

2.1.1 Definition and Classification of Acute Kidney Injury

Conceptually, acute kidney injury is defined by an abrupt increase in serum creatinine resulting

from injury or insult that causes a functional or structural change in the kidney. It involves a

spectrum of injury from less severe, which may require only watchful waiting, to more

advanced, often requiring supportive therapy with dialysis.

In the past, multiple definitions for AKI have been used in clinical studies, which has

limited our understanding of the epidemiology for AKI and thereby hindered progress in the

field. In order to address this and to establish a standardized definition of AKI, a

multidisciplinary collaborative network of investigators established the Acute Dialysis Quality

Initiative (ADQI).

This group formulated two definitions for AKI: the RIFLE criteria in 2004 and the Acute

Kidney Injury Network (AKIN) criteria in 2007. (Tables 1 and 2)

The RIFLE criteria represent an acronym for three graded levels of kidney injury: RISK,

INJURY and FAILURE, based upon either the magnitude of elevation in serum creatinine or

decrease in urine output; and two outcomes: LOSS and END STAGE RENAL DISEASE

3

(ESRD). A patient may fulfill the criteria through changes in serum creatinine or glomerular

filtration rate (GFR) or changes in urine output. The RIFLE stages are included in Table 1.

Table1: The RIFLE criteria for acute kidney injury.

RISK – 1.5 fold increase in serum creatinine or GFR decrease by 25% or urine output

<0.5ml/kg per hour for six hours.

INJURY - Twofold increase in serum creatinine or GFR decrease by 50% or urine output

<0.5ml/kg per hour for twelve hours.

FAILURE - Threefold increase in serum creatinine or GFR decrease by 75% or urine

output <0.5ml/kg per hour for twenty four hours or anuria for 12 hours.

LOSS – Complete loss of kidney function for more than four weeks.

ESRD – Complete loss of kidney function for more than three months.

The RIFLE criteria is dependent on a priori knowledge of the patient’s baseline

creatinine. In cases where the baseline creatinine is unknown, the developers of the RIFLE

criteria suggest calculating a theoretical baseline based on the Modification of Diet in Renal

Disease (MDRD) formula. 10

The AKIN criteria is more commonly used in clinical studies.11;12

These criteria stratify

patients into three stages of AKI based on their severity of renal dysfunction: Stage 1, 2, and 3.

These stages are synonymous with the RISK, INJURY AND FAILURE stages of the RIFLE

criteria with slight modifications. Specifically, these modifications include: i) broadening the

RISK category of RIFLE to include an increase in serum creatinine of at least 0.3mg/dL

(26umol/L) even if this doesn’t reach the 50% threshold; ii) setting a 48 hour window on the first

documentation of any criteria; and iii) categorizing patients as FAILURE if they are treated with

renal replacement therapy (RRT) regardless of what their serum creatinine or urine output is at

the point of initiation. Furthermore, the AKIN criteria should only be applied after volume status

has been optimized and urinary tract obstruction has been ruled out if oliguria was used as the

4

sole diagnostic criterion. As with the RIFLE criteria a patient may fulfill the AKIN criteria

through changes in serum creatinine or urine output.

Table 2: The AKIN criteria for acute kidney injury.

Stage Serum creatinine criteria Urine output criteria

I Increase in serum creatinine ≥0.3 mg/dL (26.4

µmol/L) or increase to more than or equal to 150-

200% from baseline

< 0.5ml/kg/hr for > 6 hours

II Increase in serum creatinine greater than 200-

300% from baseline

< 0.5ml/kg/hr for > 12

hours

III Increase in serum creatinine greater than 300%

from baseline or serum creatinine > 4.0 mg/dL

(354 µmol/L) with an acute increase of at least

0.5 mg/dL (44 µmol/L)

<0.3ml/kg/hr for 24 hours

or anuria for 12 hours

2.1.2 Prevalence of Acute Kidney Injury in Hospitalized Patients

Acute kidney injury is one of the most common conditions affecting hospitalized patients. Using

administrative data, Waikar and colleagues showed that it affects up to 15.3% of all hospitalized

patients.13

However, estimates of the incidence of AKI in hospitalized patients vary according

to the clinical setting as well as the definition of this condition.14-18

2.1.3 Short Term Outcomes of Acute Kidney Injury

Regardless of the definition used, AKI has been found to be associated with increased short-term

mortality, length of stay and hospital costs.1;19-22

This was first demonstrated by Chertow and

colleagues who retrospectively studied a cohort of 19 982 patients admitted for various

conditions to an urban academic hospital. They confirmed that an increase in serum creatinine as

5

low as 0.3 mg/dL (26 µmol/L) from baseline was associated with an odds ratio for mortality at

30 days of 4.1 (95% CI 3.1 to 5.5).1 In fact, the greater the deviation from the baseline

creatinine, the higher the odds of mortality. This association has been subsequently confirmed

by others across a spectrum of clinical settings including post-cardiothoracic surgery, post-

contrast injection, sepsis, congestive heart failure, and in the intensive care unit. 23-26

Aside

from an increase in mortality, AKI has also been found to be associated with an extended

hospital length of stay (LOS) and increased hospital costs.1;27;28

Similar to the association found

with 30 day mortality, the severity of renal dysfunction is positively correlated with increasing

LOS.27

The financial impact of AKI has been found to be largely explained by the increased

LOS associated with this condition.1

2.1.4 Methodological Issues in Studying the Long Term Outcomes of Acute Kidney Injury

While the short term hazards of AKI have been well defined, only recently has attention been

paid to the long-term complications of AKI. This trend may partly be explained by four reasons

including: i) the lack of a standardized definition for renal recovery; ii) the short duration, and

single-centre nature of most studies; iii) issues related to the definition of AKI and baseline

creatinine; and iv) potential confounding in studies of AKI.

The lack of a standardized definition of renal recovery has hampered research into the

long-term effects of AKI. Prior to 2005, studies describing the long-term implications of AKI

were heterogeneous in their definition of renal recovery, which limited not only generalizability,

but may have biased results.29-31

The apparent reversibility of AKI as manifest by a diminution of

serum creatinine from its peak was commonly used to define renal recovery in numerous historic

studies. This notion may have propagated the fallacy of complete renal recovery, when in fact

this was not the case as has been demonstrated by recent clinical and basic science studies.

4;8;32Collectively, these recent studies have refuted the belief that recovery of AKI, be it partial or

complete, portends no future adverse consequences. Responding to the issue surrounding the

heterogeneity in defining renal recovery, the Acute Dialysis Quality Initiative (ADQI) published

a consensus definition for renal recovery in 2004.33

Complete renal recovery was defined as the

return of kidney function to pre-ARF baseline levels. Recovery would be defined as partial if a

6

persistent change from baseline remained evident, but not including the continued need for

RRT. The requirement for RRT for more than 4 weeks would be classified as persistent ARF,

whereas those remaining dependent on RRT at 3 months would be defined as having progressed

to ESRD.33

The impact of the standardization of the definition of renal recovery has led to the

design of methodologically sound studies that are increasingly generalizable .

Another issue that has hampered studying the long-term effects of AKI has been the short

follow-up and single centre design of studies. Historically, most studies focusing on the long-

term impact of AKI were less than 6 months.5;34-37

This short follow-up had likely

underestimated the true effect of AKI on long-term outcomes such as progression of renal

disease and mortality as has been demonstrated by more recent studies. Similarly, study

generalizability was also impacted by the single centre design as protocols and procedures may

vary among different centres. Again, more recent works have addressed this issue using

population-based studies from Ontario, Canada and the Veteran’s Health Administration. 3;6

Consensus definitions of AKI (AKIN and/or RIFLE) fail to account for the underlying

cause of the acute renal injury, which may confound the true relationship between AKI and long-

term outcomes. Previous studies of AKI have considered this entity to be a homogeneous

phenomenon, such that disease entities such as prerenal azotemia, acute tubular necrosis (ATN),

and acute interstitial nephritis have been considered to be one and the same.1;38;39

It seems

implausible that an acute rise in serum creatinine due to prerenal azotemia would have the same

long-term renal effect as that due to acute tubular necrosis (ATN) as these entities may impart

different degrees of renal injury which may have dissimilar risks for the development of CKD,

the acceleration of CKD, or mortality.

Defining what constitutes baseline renal function has also hindered quality of care

research on long-term outcomes of AKI. In the renal literature, there is marked heterogeneity in

the definition of baseline creatinine. For example, it has been defined as the lowest serum

creatinine observed during hospitalization, the lowest of the first three inpatient serum

creatinines, or the prehospitalization serum creatinine.1;22;40;41

While the RIFLE criteria uses the

MDRD formula to estimate a baseline serum creatinine in cases where the baseline is unknown,

this too has its limitations as the patient may have had some renal dysfunction prior to the

episode of AKI, which would be unaccounted for by this approach. Researchers have attempted

7

to solve this issue by postulating that outpatient serum creatinines be used to define baseline

values of creatinine.42

While this approach has been used by some, it too may misclassify

individuals. The time lapse between the acute event and the last outpatient serum creatinine may

be excessive thereby being unable to account for interim changes in renal function. Conversely,

using outpatient values too close to the time of hospitalization may lead to the inclusion of an

already impaired serum creatinine due to the acute or subacute process that eventually leads to

hospitalization.

Associated with difficulties in determining baseline renal function is the issue of

confounding. Previous long-term studies of patients with AKI, have been unable to account for

important confounders. This may impact the determination of the relationship between AKI and

subsequent outcomes such as mortality, development of CKD, or progression of CKD as was

demonstrated in a systematic review and metaanalysis by Coca et al.5 Use of methodologically

sound techniques to account for residual confounding, such as propensity scores and matching,

have addressed some of these concerns.

2.1.5 The Epidemiology of Long Term Outcomes of Acute Kidney Injury

The long-term outcomes of AKI have recently been the focus of a number of studies.

Collectively, these have demonstrated that AKI is associated with increased mortality as well as

the progression of renal disease and ESRD.

In 2007, Coca and colleagues summarized the prevailing data on the long-term outcomes

of AKI in a systematic review and meta-analysis.5 The review focused on three outcomes: i) the

risk of mortality; ii) the risk of progression to chronic kidney disease (CKD) or dialysis; and iii)

the risk of cardiovascular events imparted by AKI. Their meta-analysis included 48 studies

which were heterogeneous in their definition of AKI, their duration of study, and their patient

populations. Nonetheless, Coca et al. were able to demonstrate an increased risk of mortality for

survivors of AKI compared to those without AKI [Risk Ratio 2.59; 95% CI 1.99-3.42]. They

also demonstrated a graded increase in risk of long-term mortality with increasing severity of

AKI. In fact, patients with severe AKI (dialysis requiring AKI) had twice the risk of mortality as

8

those with less severe AKI (not requiring dialysis) (RR 3.09; 95% CI 2.20-4.33 [severe]; RR

1.67; 95% CI 1.41-1.98 [mild]). Aside from demonstrating an increased risk for long term

mortality, Coca and colleagues also showed an increased rate of CKD at 7.8 per 100 patient

years in survivors of AKI. Due to methodological constraints imposed by the included studies,

risk ratios were unable to be calculated for the outcomes of progression to CKD as the majority

of studies did not include a comparator group without AKI.

Recent studies have overcome many of the methodological limitations of those included

in Coca’s review. They have demonstrated that AKI is associated with increased mortality and

the progression to CKD and chronic dialysis, even in patients with normal baseline renal

function. Wald and colleagues demonstrated that AKI requiring dialysis is associated with an

increased risk of progression to chronic dialysis.3 They used administrative data to study a

cohort of 3769 adult patients in Ontario, Canada with acute kidney injury who required in-

hospital dialysis and survived free of dialysis for at least 30 days after discharge. Using a

propensity score model, they demonstrated that AKI necessitating in-hospital dialysis was

associated with almost three times greater risk of chronic dialysis [adjusted hazard ratio 3.23;

95% CI 2.70 to 3.86) after a median follow up of 3 years. All-cause mortality was not increased

among patients with AKI [adjusted hazard ratio 0.95; 95% CI 0.89 to 1.02], which may have

been due to the exclusion of 7% of the exposed group with whom an appropriate match could not

be found. The unmatched individuals exhibited a higher mortality rate over time, such that the

inclusion of them in the final analysis may have altered the point estimate towards an increased

risk of all-cause mortality.

Similarly, work by Hsu and colleagues further established the association between

dialysis requiring AKI and poor long-term outcomes.43

They focused on a hospitalized cohort of

patients with underlying CKD (pre-hospitalization eGFR <45ml/min) and found that an episode

of AKI requiring dialysis increased the risk for progression to end stage renal disease (ESRD) or

death by 30% compared to those without AKI (adjusted HR 1.37; 95% CI 1.01-1.86). These

findings are clinically consistent as patients with underlying CKD already possess underlying

kidney damage. Therefore a subsequent renal ―hit‖ or ―insult‖ may unmask and/or accelerate

this injury. Subsequent work has corroborated the finding that CKD modifies the effect of AKI

on mortality and progression to CKD.44

9

While a prior history of CKD may accelerate the progression of CKD, subjects with

relatively normal renal function at baseline have also been found to be at risk of increased

mortality and the development of CKD after an episode of acute kidney injury. This has been

demonstrated by Lo et al. who used administrative data from the Kaiser Permanente Health Care

system to examine the impact of an episode of AKI requiring dialysis on the risk of progression

of renal disease and death among patients who had relatively preserved baseline renal function

(eGFR ≥45ml/min) and did not develop ESRD within 30 days of hospital discharge compared

with a matched control group.44

They demonstrated that patients experiencing AKI had a 28-

fold increased risk for the progression of CKD, which was defined as the development of stage

IV CKD or higher (95% CI 21.1 to 37.6), and an increased risk for death (adjusted HR 2.3; 95%

CI 1.8 to 3.0).

The studies by Wald, Hsu and Lo all focused on patients who developed AKI requiring

dialysis, which is the most severe form of renal injury according to validated criteria such as the

RIFLE and AKIN criteria. Hence, the need for dialysis may be the most important predictor of

not only mortality but the progression or development of subsequent CKD. This notion has been

corroborated by the work of Chawla and colleagues who used Veteran’s Administration (VA)

data to develop and validate a prediction tool for the progression to Stage IV CKD after an

episode of AKI.45

They studied a cohort of patients admitted to the VA who had normal baseline

kidney function (defined as an eGFR >60 ml/min) and developed AKI during their

hospitalization. Among the variables included in their predictive model, the requirement for

dialysis was the strongest predictor for the progression to CKD (adjusted odds ratio 53.18; 95%

CI 11.28-250.64).

Studies have also focused on the impact of less severe AKI on long-term outcomes and

have demonstrated an increased risk of mortality and progression of renal disease among

affected individuals. In their metaanalysis, Coca and colleagues examined the association

between AKI and death stratified by severity of AKI into mild, moderate and severe categories.5

Studies stratified into mild AKI enrolled patients which had a rise in serum creatinine ≥ 25%

from baseline or had a decrease in creatinine clearance ≥ 10% from baseline (3 studies).

Moderate AKI studies enrolled subjects which had a >50% rise in serum creatinine or a

creatinine concentration of > 1.7mg/dL from baseline (6 studies). Severe AKI studies enrolled

10

patients with AKI that required RRT (7 studies). Not surprisingly, Coca et al. demonstrated

that while severe AKI correlates with the greatest risk of mortality, both mild and moderate AKI

also increase the risk for long-term mortality. (mild AKI RR 1.67, 95% CI 1.41-1.98; moderate

AKI RR 2.70, 95% CI 1.77-4.12; severe AKI RR 3.09, 95% CI 2.20-4.33).

Aside from increased mortality, AKI not requiring RRT has been associated with

progression to ESRD, particularly among patients with underlying CKD. This was demonstrated

by Ishani et al. who used a random sample of Medicare beneficiaries.46

They showed that while

both patients with and without CKD had an increased risk of progression to ESRD, the risk was

substantially higher for patients with CKD (non-CKD: aHR 2.38; 95% CI 2.31-2.46; CKD: aHR

13.0; 95% CI 10.6-16.0). Despite demonstrating an important trend, Ishani et al.’s study had a

number of methodological issues particularly stemming from the use of administrative data.

Firstly, their use of administrative data may have misclassified individuals. It is well established

that administrative coding for AKI and CKD is highly specific but poorly sensitive. Therefore,

they may have misclassified some individuals with AKI and CKD as free from these conditions.

Secondly, the data set. did not include creatinine values. This did not allow for the classification

of AKI severity or the stage of CKD. Accounting for baseline CKD stage and severity of AKI

may have important prognostic significance as it is unlikely that an individual with Stage II CKD

and less severe AKI would have the same risk of subsequent poor outcomes as an individual with

Stage IV CKD and more severe AKI.

Building on the work of Ishani, LaFrance and Miller used VA administrative data to

show that patients who developed an episode of AKI while hospitalized and did not require RRT

were at increased risk for long-term mortality compared to control subjects (adjusted HR 1.41,

95% CI 1.39-1.43).6 In contrast to previous studies, they used the AKIN criteria to classify the

severity of AKI and accounted for post-discharge renal function as determined by eGFR.

Adjustment for post-discharge renal function is particularly important as CKD is an important

modifier of mortality.47;48

LaFrance also demonstrated that AKI severity, as classified by the

AKIN criteria, independently associates with a graded rise in the risk of long-term mortality

(AKIN I aHR 1.36, 95% CI 1.34-1.38; AKIN II aHR 1.46; 95% CI 1.42-1.50; AKIN III aHR

1.59; 95% CI 1.54-1.65).

11

It has also been proposed that the type of renal injury may impact long-term outcomes

of AKI. In this regard, patients with fulminant renal injury such as oligoanuric ATN may have

worse outcomes than those with less severe episodes of AKI. While this theory has biological

plausibility, there have been conflicting studies on this association. Amdur and colleagues

demonstrated that US veterans with no history of CKD who were admitted to hospital with AKI

have an increased risk of reaching the combined end-point of Stage 4 CKD, chronic dialysis or

death over a 75 month follow-up period. 49

In fact, patients with a history of ATN had a higher

hazard of this combined endpoint than their counterparts with non-ATN AKI (adjusted HR 6.64

for ATN, 4.03 non-ATN AKI). Interestingly, when studying the risk of mortality alone, there

was an increased risk of death in the non-ATN AKI group but not in the ATN group. This may

be reflective of the small sample size of the ATN group or the inability to adjust for potential

important confounders. However, studies by Chawla et al. and Liano et al. have also failed to

find an association between ATN and subsequent mortality compared to non-ATN AKI.49;50

Such a discrepancy may be a consequence of their use of administrative data to classify AKI, and

more specifically ATN, as previously discussed.

A majority of work focusing on AKI not requiring RRT has previously focused on

specialized populations such as the elderly and U.S. veterans. 6 Again, these studies have largely

corroborated the relationship between poor long-term outcomes and AKI. More recently, broad-

based population studies have expanded on literature of the long-term complications of AKI.

James and colleagues studied a cohort of patients undergoing coronary angiography who

developed AKI and were stratified according to AKIN criteria.51

They showed that over a

median of 19 months follow up, the risk of death and progression to ESRD was increased in

patients with AKI. The risk of such events was commensurate with increasing severity of AKI

[(Death: AKIN Stage I aHR 2.00; 95% CI 1.69-2.36; AKIN Stage II-III aHR3.72; 95% CI 2.92-

4.76); ESRD: AKIN Stage I aHR 4.15; 95% CI 2.32-7.42; AKIN Stage II-III 11.74; 95% CI

6.38-21.59)]. Similarly, a study by Lopes et al., focusing on patients admitted to the hospital

with infectious complications has also demonstrated that AKI is an independent predictor of

mortality at 1 and 2 years respectively. 52

Despite the convincing evidence that AKI leads to poor long-term outcomes, not all

studies have come to the same conclusion. For example, studies by Schiffl et al., Ponte et al. and

12

Liano et al. have all demonstrated opposing results.50;53;54

However, the conclusions of these

analyses are controversial. They are mostly small, single centre studies that failed to account for

residual confounding stemming from patient characteristics, the varying time periods under

study, as well as the aspect that not all patients required dialysis Therefore, the outcomes are

likely biased and reflect a selected cohort of patients who were fairly healthy prior to and during

their AKI event.

2.2 The Pathophysiology of Acute Kidney Injury

Recent epidemiological evidence demonstrating the association between AKI and poor long-term

outcomes has generated much interest in the pathophysiological mechanisms underscoring these

findings.

The majority of our current knowledge concerning the pathophysiology of AKI has been

gleaned from animal studies. Collectively, these have demonstrated that AKI primarily causes

structural changes in the kidney, which over time leads to functional changes such as proteinuria

and hypertension and ultimately a progressive decrease in GFR. However, AKI also affects

other organ systems, and perhaps the combination of the maladaptive processes affecting the

kidney and these other organs results in increased mortality demonstrated in observational

studies.

2.2.1 Renal and Non-Renal Effects of Acute Kidney Injury and their Relationship with Mortality

The kidney is the primary organ affected by AKI. Evidence from basic science studies has

shown that there exists strong biologic support for the association between AKI and subsequent

fixed renal damage.

Basile and colleagues were the first to confirm the structural-functional derangements

manifest by AKI in an elegant animal experiment.55

They took two groups of rats and subjected

one group to bilateral ischemic-reperfusion injury resulting in AKI. Each group of rats was

serially followed for changes in serum TGF-β levels, serum creatinine levels, urine protein

excretion, blood pressure and changes in renal morphology. As expected, in the AKI group,

13

there was a marked initial rise in serum creatinine which decreased to levels similar to the

control group by the end of the first week. The serum creatinine levels remained similar in both

groups for the duration of the study. However, from the outset levels of TGF-β were

significantly higher in the AKI group. Similarly, the AKI group manifested a significant rise in

urine protein excretion compared to the control group beginning at 20 week mark, along with an

increase in mean arterial pressure (MAP). Structural analysis of the renal parenchyma revealed a

decrease in microvascular density and the development of tubulointerstitial fibrosis at the 40

week mark in the AKI group.

Subsequent work by Spurgeon-Pechman and others has confirmed the findings of Basile

and elucidated the mechanism in which AKI leads to structural and functional changes in the

kidney.56-58

Initially, AKI leads to structural damage to the capillary network of the kidneys

leading to renal hypoxia. As a result, an inflammatory response ensues whereby many

proinflammatory and profibrotic cytokines such as TGF-β, interleukin-6 (IL-5) and tumour

necrosis factor α (TNFα) are elaborated. Some of these cytokines such as TGF-β mediate

tubulointerstitial fibrosis, while others promote arteriosclerosis. The arteriosclerosis that ensues

caused impairments in renal autoregulation leading to hypertension which promotes further renal

decline by way of ongoing ischemic renal injury. Moreover, the combination of interstitial

fibrosis and hypertension contribute to the development of proteinuria, which further damages an

already susceptible kidney. Accordingly, the acute renal injury results in a process leading to

further renal injury.

Aside from its effects on the kidney, it is increasingly recognized that AKI is a

multisystem disease affecting many other organs including the heart, lungs, and brain. Klein and

colleagues had demonstrated that an episode of ischemia-reperfusion renal injury in rats leads to

an ARDS-like lesion in the lungs manifest by increased vascular permeability.59

Similarly, in

rats subjected to ischemic reperfusion renal injury, cardiac myocyte function is impaired leading

to aberrant cardiac function and increased apoptosis.60

As with the renal manifestations of AKI,

the non-renal structural and functional changes are believed to be a consequence of the

elaboration of inflammatory cytokines resulting from the initial renal insult.

While the specific reason for the association between AKI and long-term mortality has

yet to be elucidated, it is highly probable that it may be mediated by the downstream effects of

14

the incident AKI. In other words, the functional changes that occur in both the kidney and other

organ systems leading to manifestations such as proteinuria, hypertension, and impaired lung

function may be the link between AKI and mortality. There is compelling evidence that

hypertension and proteinuria, both of which are cardiovascular risk factors, are associated with

increased mortality.47;61

Similarly, long-term survivors of ARDS also have also been found to

have persistent impairments in lung function and quality of life, both of which may lead to

increased morbidity and possibly mortality.62;63

2.3 The Potential Impact of Nephrologist Follow-up After Hospitalization with Severe

Acute Kidney Injury

The value of specialist follow-up after hospitalization for an array of acute conditions is well

established.64-67

Studies have shown that specialist follow-up is associated with decreased

mortality, rehospitalization, and emergency room visits.

The field of cardiology was the first specialty to demonstrate the importance of specialist

follow-up on mortality. In his seminal paper, Ayanian et al. demonstrated an almost 4%

decrease in two year mortality after admission with a myocardial infarction in patients who had

follow-up with a cardiologist after discharge compared to those who did not.64

Although

Ayanian and colleagues were unable to establish the underlying reason behind this finding, they

hypothesized that perhaps differences in quality of care among patients with and without follow-

up could account for this. Building upon Ayanian’s work, Ezekowitz et al. showed that new-

onset CHF patients who were recently discharged from Canadian hospitals had a survival

advantage at one year if they had follow-up with a cardiologist after discharge compared to those

who did not.68

More recently, two population-based studies of CHF and COPD patients had

indicated that early specialist follow-up after hospital discharge may have more immediate

benefits, specifically, fewer rehospitalizations and emergency room visits within 30 days.67;69

The benefit imparted by specialist follow-up after hospitalization may be related to

improvements in quality of care imparted by these physicians. Azaynian demonstrated that

patients who were followed-up by a cardiologist after an admission for myocardial infarction

15

were more likely to be prescribed evidence-based medications such as statins and beta-blockers

compared to those seen by a family physician or internist.70

Similarly, specialist care may also

provide for better health services utilization including access to life saving procedures such as

coronary bypass surgery and angioplasty.64

For survivors of severe acute kidney injury, nephrologist follow-up may have a number

of benefits, which may lead to improvement in morbidity and mortality. For example, follow-up

may lead to: i) testing for risk factors for future loss of renal function; ii) initiation of preventive

measures for progression of renal disease and generalized vascular disease; iii) monitoring and

management of patients with established CKD and its complications, and iv) improved access to

other health services and specialists.

16

CHAPTER 3

Methods

The purpose of this chapter is to:

Provide an overview of the study design;

Describe the data sources;

Clarify the patient selection criteria;

Define the propensity score model;

Specify the statistical approach for analysis;

Outline the subgroup and additional analyses.

3.1 Overview of Study Design

We performed a propensity-score matched cohort study of all adults in Ontario with acute kidney

injury and who received in-hospital dialysis, followed by a 90 day period of being free from

dialysis, death or rehospitalization after discharge, from 1994 to 2008. We compared those who

did versus those who did not receive follow-up care with a nephrologist within 90 days after the

index hospitalization discharge date, matched by a propensity score to receive such care. Cox

Proportional Hazards models were used to estimate the hazard ratio (HR) and 95% confidence

interval (CI).

17

3.2 Data Sources

3.2.1. Canadian Institute for Health Information Discharge Abstract Database (CIHI-DAD)

The Canadian Institute for Health Information Discharge Abstract Database

(CIHI-DAD) contains data on inpatient hospital discharges across Canada, supplied to the

Canadian Institute for Health Information (CIHI) from participating hospitals in all territories

and provinces of Canada except Quebec. The DAD contains demographic, administrative and

clinical data for hospital discharges (inpatient acute, chronic, rehabilitation) and day surgeries,

which is abstracted from patient charts by trained medical staff. In addition to clinical

information, the DAD contains some nonmedical data elements related to demographic and

administrative information for each separation.

Prior to 2002, the CIHI-DAD had used the International Classification of Diseases 9th

revision (ICD-9), and later the ICD-9 Clinical Modification (ICD-9CM) for diagnostic coding.

During that time, procedures were coded in the CIHI-DAD using the Canadian Classification of

Procedures (CCP). Beginning in 2002, the CIHI-DAD began using the International

Classification of Diseases 10th

revision (ICD-10) for diagnostic coding, and the Canadian

Classification of Health Interventions (CCI) for procedural coding for the province of Ontario.

In this study, the CIHI-DAD was used to: i) identify the index hospitalization; ii) define

the AKI-RRT cohort (along with the OHIP database); iii) measure important comorbidities

(through diagnostic codes); and iv) define outcome events.

In terms of data validity, a reabstraction study of the CIHI-DAD demonstrated excellent

agreement with regard to demographic variables (~99% ) and procedural codes (77.1%). 71

Coding of the most responsible diagnosis (MRDx), defined as the one diagnosis responsible for

the greatest contribution to the length of stay, demonstrated variability in agreement (67.6%). 71

Variability in agreement was more pronounced for coding of secondary codes, such as Type 1, 2

and 3 diagnoses.

18

3.2.2. Ontario Health Insurance Plan Database (OHIP)

The Ontario Health Insurance Plan (OHIP) database contains information on physician claims

for inpatient and outpatient services. These services include: i) physician consultations and

assessments in acute care settings, office settings and long-term care facilities; ii) technical and

professional components for diagnostic and therapeutic procedures; iii) surgical procedures; and

iv) laboratory services. The OHIP database also includes information on physician identifiers

(specialty codes) and patient identifiers. This database only captures information on Ontario

physicians who practice within a fee-for-service system. It is estimated that approximately 95%

of physicians practicing in Ontario are enrolled in a fee-for-service billing schedule with the

remaining 5% covered by alternate funding plans (AFPs).

In this study, the OHIP database was used to: i) define the cohort (along with the CIHI-

DAD); ii) define the exposure; iii) measure important comorbidities; and iv) define outcome

events.

Validity of the OHIP database has been less rigorous than for the CIHI-DAD. However,

the OHIP database has been utilized as a supplement to CIHI-DAD codes in administrative

diagnostic algorithms.72;73

It has also been used in administrative algorithms.74

3.2.3 Registered Persons Database (RPDB)

The Registered Persons Database (RPD) contains demographic and vital status data for

individuals with a valid OHIP number. Specifically, the RPDB contains the following

information: i) date of birth; ii) gender; iii) postal code; iv) date of death; and iv) individual

identifier.

In this study, RPDB was used to: i) obtain demographic information for each patient; and

ii) identify deaths (mortality outcome).

3.2.4 Canadian Organ Replacement Registry (CORR)

The Canadian Organ Replacement Register (CORR) is a Canadian information system

for renal and extra-renal organ failure and transplantation. It contains information on

19

outcomes of solid organ transplantation and renal dialysis, which is collected from hospital

dialysis programs, regional transplant programs, organ procurement organizations (OPOs) and

kidney dialysis services offered at independent health facilities. It contains the locations of all

dialysis facilities within Canada. This database has recently undergone validation and

demonstrates excellent agreement with the medical record for demographic data, but poor

sensitivity for comorbid conditions.75

In this study, the CORR database was used to define the distance from a subject’s

residence to the closest dialysis centre using geospatial mapping techniques. 76

The dialysis

centre was a marker for a nephrologists’ practice address.

3.2.5 Canadian Census Data

The Canadian Census data was used to determine income quintile and rural residence status

based on the 2001 Statistics Canada Census, which assigns income quintiles to each

neighborhood community.

3.2.6 Ottawa Data Warehouse

The Ottawa Data Warehouse contains inpatient laboratory data from the Ottawa Hospital and

affiliated institutions. It was used to determine the discharge serum creatinine in a subset of

hospitalized patients. The Ottawa Data Warehouse holdings have recently been used in a study,

but no formal validation process of its data holdings has been undertaken.77

3.2.7 CERNER Database

The CERNER database contains inpatient laboratory data from 11 hospitals in Southwestern

Ontario for patients aged 40 years and older. It was used in this study to determine the discharge

serum creatinine in a subset of hospitalized patients from 2002-2008. This database has been

used in a previous study but has not undergone formal validation.78

20

3.3 Cohort Definition

3.3.1 Inclusion Criteria

The cohort consisted of all adults (age ≥ 18 years) who were hospitalized in Ontario, Canada

between April 1, 1996 and March 31, 2008, and were discharged alive following an episode of

severe acute kidney injury requiring renal replacement therapy (AKI-RRT) during

hospitalization.

AKI-RRT was defined by the combination of a diagnostic code for AKI (all diagnostic

types) and a procedure code for dialysis. We defined AKI by ICD-9 codes: 584.5-584.9, 669.3,

958.5 634.3, 635.3, 636.3, 637.3, 638.3, 639.3; and ICD-10 codes: N17.0-N17.9, O08.4, T79.5,

O90.4. Dialysis was defined by OHIP codes: R849, R850, G323, G325, G326, G330, G331,

G332, G860, G333, G083, G091, G085, G295, G082, G090, G092, G093, G094, G861, G862,

G863, G864, G865, G866, G294, G095, G096; CCP codes: 51.95, 66.98; and CCI

codes:1PZ21HQBR, 1PZ21HPD4. This definition has been used in a previous study.3

(Appendix 1)

3.3.2 Exclusion Criteria

Patients were excluded from the study based on the following criteria: < 18 years at hospital

admission, AKI in the preceding 5 years, dialysis in the preceding 5 years, kidney transplant in

the preceding 5 years, patients who died, received dialysis or were re-hospitalized during the 90

days following hospitalization discharge date and patients with invalid/missing OHIP number or

data. Patients with > 1 hospitalization with AKI-RRT had the first hospitalization counted for

creation of the original cohort.

We had excluded patients with the aforementioned characteristics as we felt their

inclusion would impart a strong selection bias leading to confounding of our results.

21

Figure 1: Creation of the Acute Kidney Injury Survivor Cohort.

22

3.3.3 Justification of Cohort Selection (Inclusion and Exclusion Criteria)

Patients with AKI-RRT were chosen as the cohort of study for multiple reasons. Firstly, we felt

this group to be of sufficiently ―high-risk‖ for subsequent deleterious outcomes. Multiple studies

have demonstrated a continuum of renal damage conditional on the severity of the acute kidney

insult. 8;38;44;79

As dialysis requiring AKI represents the most severe form of AKI, it is plausible

that this cohort would develop clinically significant renal damage which would portend an

increased risk for long-term morbidity and mortality. This construct has been corroborated by

clinical studies where individuals with AKI-RRT demonstrate inferior outcomes and a faster

decline of renal function than their non-dialysis requiring counterparts with AKI.38;44;80

As such,

we felt that this cohort, compared to others with less severe forms of AKI, would garner the most

benefit from early nephrology follow-up.

Secondly, our cohort had been validated in a previous study and demonstrated excellent

sensitivity and specificity. 81

In that study, the combination of an ICD-9 diagnostic code for AKI

and an ICD-9 procedure code for dialysis had 90% sensitivity (95% CI 85% - 94%), 94%

specificity (95% CI 89% - 97%), and 94% positive predictive value (95% CI 89% - 97%) for

capturing the combination of acute kidney injury and dialysis. This is in contrast to ICD-9 and

ICD-10 diagnostic codes for AKI which demonstrated excellent specificity (median 99%) but

poor sensitivity (median 29%), according to a recent systematic review. 81

Furthermore, the same

definition of AKI-RRT had been employed in a recent study using linked Ontario administrative

databases to assess the risk of chronic dialysis and all-cause mortality, thereby adding to its face

validity. 38

We chose to exclude patients with a diagnosis of AKI, dialysis or transplantation in the 5

years preceding the index admission as these are all important confounders. We also excluded

patients who died, received dialysis or were rehospitalized during the first 90 days post-

discharge. Studies have demonstrated that a majority of patients discharged from hospital free of

dialysis either return to dialysis, are readmitted or die within the first 90 days and including such

patients would impart selection bias as sicker patients may experience the outcome earlier

regardless of the exposure.

23

3.4 Covariates and Outcome Variables

3.4.1. Primary exposure: Nephrology Consultation within 90 days of Discharge

The primary exposure for this study was at least one outpatient nephrology consultation within

90 days of discharge from the index hospitalization defined using the OHIP database. In the

OHIP database, the specialty code for nephrology (specialty code 16) was introduced in October

2009. Prior to this, nephrologists were coded in the same category as internal medicine

(specialty code 13). Therefore, we defined a nephrologist as per an algorithm used by Wald et

al. Accordingly, a nephrologist was any physician with 10 claims using any combination of the

following OHIP codes between start of ICES data until March 31, 2008: G326, G860, G333,

G862, G863, G865, G866, G332, G861, G864.

Aside from the aforementioned study by Wald et al., a study by Jain et al. looking at the

impact of eGFR reporting on nephrology referral rates in Ontario also used OHIP data to define a

nephrologist. 38;82

In that study, a nephrologist was a physician who had billed for chronic

dialysis (i.e. one of codes G860-866 or three of codes G326 or G333) at least 10 times in the past

year and who had billed at least one A135 claim in the same year. Jain et al.’s algorithm

identified nearly similar numbers of nephrologists in Ontario when compared to the ICES

Physicians Database (IPDB), as the gold standard, for the years 1999-2005.

Despite Jain et al.’s definition being validated against the IPDB, there were multiple

reasons for us not using it to define our exposure. Firstly, the IPDB has not been independently

validated, and therefore may not accurately code a physician’s specialty. Secondly, using kappa

statistics we assessed the degree of agreement between our definition and Jain et al.’s definition

with the IPDB. Both definitions had almost identical kappas (Jain κ=0.901, 95% CI 0.887-

0.915; Wald κ=0.891, 95% CI 0.876-0.905), demonstrating that both definitions had excellent

agreement with the IPDB. (Table 3) Finally, our definition yielded a greater number of exposed

individuals compared to Jain et al.’s definition (1588 vs. 1474). We felt that a greater number of

exposed individuals would protect our statistical power as it would offset any unexpected

decrements in the effect.

24

Table 3: Comparison of Two Algorithms for Defining a Nephrologist.

Jain et al.

Nephrologist*

Yes No Total

IPDB

Nephrologist

Yes 1357 63 1420

No 117 2350 2467

Total 1474 2413 3887

*one of codes G860-866 or three of codes G326 or G333 at least 10 times in the past year and who had billed at

least one A135 claim in the same year.

‡ any physician with 10 claims using any combination of the following OHIP codes: G326, G860, G333, G862,

G863, G865, G866, G332, G861, G864

Wald et al.

Nephrologist‡

Yes No Total

IPDB

Nephrologist

Yes 1403 17 1420

No 185 2282 2467

Total 1588 2299 3887

Κ =0.901

95% CI: 0.887-0.915 95% CI: 0.887-0.915

Standard Error: 0.0072

Κ =0.891

95% CI: 0.876-0.905

Standard Error: 0.0075

25

3.4.2 Primary Outcome: All-cause Mortality

The primary outcome of this study was all cause mortality within 2 years of the index date (date

of discharge + 90 days). The RPDB was used to define this outcome.

3.4.3 Secondary Outcome: Receipt of Chronic Dialysis

Using CCI/CCP and OHIP procedural codes, we defined ESRD as the receipt of dialysis

(hemodialysis or peritoneal dialysis) for a 90 day period. (Appendix 1) We censored this

outcome at the earliest of death or 2 years from the index date. The date of first dialysis claim

was considered to be the date of initiation of chronic dialysis. Our definition of ESRD is similar

to that used in previous studies. 9;38

Despite there being no direct validation study of our

definition of chronic dialysis, it has been used in previous studies.9;38

Moreover, procedure

codes in the OHIP and DAD are very accurate.83;84

3.5 Statistical Methods

3.5.1 Propensity Score Modeling

Propensity score modeling was used for our risk analysis. This is a statistical method that is used

to reduce the impact of confounding when estimating of causal treatment effects with

nonrandomized data.85-87

The propensity score is defined as a subject’s probability of receiving a

specific treatment conditional on the observed covariates. Individuals with the same propensity

score have the same distribution of measured covariates, independent of treatment status.

However, treated and untreated subjects with the same propensity score can have different

distributions of unmeasured variables.

We used an accepted iterative process to generate our propensity model.85;88

This process

comprised 4 steps: i) specifying the propensity score model; ii) matching on the propensity

score; iii) assessing balance between treated and untreated subjects, and iv) estimation of the

treatment effect.

26

3.5.1.1 Specifying the Propensity Score Model

Using a thorough review of the literature, we derived a list of measured baseline variables

(confounders) that are likely related to the exposure (nephrology consultation within 90 days of

discharge) and outcome (death) but are not in the causal pathway. These included: age

(continuous in years), sex, and income quintile at the hospital discharge date; era of admission

(1996-2001 vs. 2002-2008), distance from dialysis centre, hospital type (teaching vs. non-

teaching), rural location (in kilometres), number of hospitalizations or health care visits to any

physician (including nephrologist) or emergency department in the 5 years preceding the

hospital discharge date, CKD in the five years preceding the hospital admission date; receipt of

coronary, carotid, or peripheral arterial revascularization, cardiac valve surgery, coronary

angiography, cardiac stress testing, echocardiography, single 12-lead or ambulatory 24-hour

electrocardiography, pacemaker insertion, carotid Doppler ultrasonography or renal biopsy in the

5 years before the hospital discharge date; diagnosed liver dysfunction, heart failure, diabetes

mellitus, cancer, cerebrovascular disease, cardiac dysrhythmias, myocardial infarction or angina,

peripheral artery disease, major infection, hypertension, proteinuria, hematuria and score on the

Charlson Comorbidity Index in the 5 years before the hospital discharge date; and sepsis, receipt

of mechanical ventilation, nonruptured aortic aneurysm repair, or cardiac surgery, all within the

index hospitalization. The receipt of mechanical ventilation was used as a surrogate for

admission to a critical care unit.89

Geospatial mapping methods were used to calculate the

distance between each patient’s residence and the practice location of the closest dialysis unit.76

(Appendix 2)

We then derived a propensity score model using logistic regression. Here, treatment

assignment (at least one outpatient nephrology consultation within 90 days of discharge) was

regressed on the baseline variables. Using this model, the likelihood of nephrology consultation

within 90 days of discharge (propensity score) was calculated for each individual patients.

3.5.1.2 Matching on the Propensity Score

We next matched treated and untreated subjects on propensity score. We employed

nearest-neighbor matching within a 0.2 caliper width. In nearest-neighbour matching, the first

randomly selected treated subject is matched to the untreated subject with the closest propensity

27

score within a 0.2 caliper width. If multiple untreated subjects are equally close to this treated

subject, then one of these untreated subjects is randomly selected for matching to the treated

subject. This process is repeated until all possible matches have been formed. A 0.2 caliper

width was used as a recent study demonstrated that matching using this width tended to have

superior performance compared with other competing methods that are used in the medical

literature. 90

We also forced an exact match on the covariate hospital type as there was an

imbalance between the pairs as demonstrated by a standardized difference >10% for this variable

(see next section).

3.5.1.3. Assessing Balance between Treated and Untreated Subjects: Standardized Difference

Statistical hypothesis testing to detect differences in baseline variables between treated and

untreated subjects is inappropriate in matched propensity based samples. It is has been proposed

that assessment of balance in such situations be assessed using standardized differences. The

standardized difference is defined as:

where treatment and control are the mean of the variable among the treated and untreated subjects,

respectively, while s2

treatment and s2

control the sample standard deviation of covariate in the treated

and untreated subjects, respectively. A standardized difference greater than 10% for a pair of

covariates indicates imbalance. 91

We used standardized differences to assess for balance between

treated and untreated subjects in our cohort before and after the matching process.

3.5.1.4. Estimation of Treatment Effect

Time-to-event analyses were performed until March 31, 2010. For the mortality end-point, an

individual was censored at the earlier of death or 2 years from the initiation of the period of

follow-up (i.e., 90 days after the index hospitalization discharge date). For the chronic dialysis

outcome, an individual was censored at the earlier of dialysis, death, or 2 years from the index

date. Incidence rates of both all-cause mortality and chronic dialysis were determined for

28

participants with and without nephrology follow-up. Hazard ratios (HRs) and 95% confidence

intervals (CIs) were derived from Cox proportional hazards models, stratified on matched sets,

with matched individuals without a nephrology follow-up being the reference group. Survival

curves for all-cause mortality and chronic dialysis were generated from the Cox models. The

proportional hazards assumption was tested using graphical methods (log 1-log plots).

3.6 Subgroup Analysis

Stratified analyses were performed in the following subgroups: age, sex, rural location, hospital

type, prior nephrologist visit, Charlson score92

and a history of myocardial infarction, diabetes

mellitus, CKD, liver disease and peripheral vascular disease. For these subgroup analyses, we

repeated the same propensity-score matching process while simultaneously forcing an exact

match on the subgroup characteristics. A z-test was used to compare whether the hazard rates

differed within subgroups.

3.7 Sensitivity Analyses

We performed several sensitivity analyses. Firstly, to account for the effect of discharge serum

creatinine on our results we repeated the primary analysis after modifying the original propensity

score to include serum creatinine (continuous variable in µmol/L) for the individuals where this

data was available. Secondly, to demonstrate that the association between follow-up and

mortality was not due to better predialysis care, we repeated our primary analysis for the

mortality endpoint and censored all patients who went on to receive chronic dialysis. Thirdly,

we undertook high dimensional propensity score modeling in an attempt to further account for

potential confounders. Finally, we assessed the specificity of effect using dermatology or

psychiatry consultation within 90 days of hospital discharge as a tracer exposure, an exposure for

which we would not expect an association with all-cause mortality. Observing an association

would raise concerns about residual confounding.

29

3.7.1 High Dimensional Propensity Score

High dimensional propensity score (HDPS) modeling is a strategy, developed by the Brigham

and Women’s Division of Pharmacoepidemiology, that has been extensively used. 93;94

This

method of modeling uses an algorithm to ―mine‖ administrative databases resulting in the

definition of a large number of potential candidate covariates, which may act as ―proxies‖ for

potential confounders. These proxies are covariates that may not necessarily be included in the

traditional propensity score model by the investigator; hence they are considered to be candidate

covariates.

The creation of the HDPS involves several iterative steps which are simplistically

summarized and contrasted to the creation of the traditional PS in Figure 2. The first step

involves identifying the administrative databases or data sources in which the search for

candidate covariates will be identified. The next step involves the identification of candidate

covariates and ranking of these variables according to the degree of bias they impart. After this

step, the investigator decides which of the candidate variables to include in the propensity model

using clinical reasoning. Once the candidate variables have been selected, these are included

along with the investigator specified covariates to develop the propensity score model.

30

Comparison of HD versus conventional propensity

score models

High Dimensional Conventional

Estimate Propensity Score

CANDIDATE covariates + INVESTIGATOR SPECIFIED covariates

Algorithm identifies most prevalent codes in data sources

= CANDIDATE Covariates

Identify Administrative Data Sources

DAD

(Proc)

DAD

(Dx )

OHIP (Dx)

Estimate Propensity Score

Investigator Specified Covariates

Figure 2. Comparison of high dimensional versus traditional propensity score modeling.

3.8 Additional Analyses

3.8.1 Healthcare Utilization

We also performed a descriptive analysis of the matched primary cohort, in which we assessed

the use of health services and cardiac procedures within 2 years after the index date for patients

with and without follow-up with a nephrologist.

3.8.2 Unmatched Cohort

For unmatched patients, we also undertook descriptive analyses and calculated the

incidence of mortality and receipt of chronic dialysis within 2 years.

31

CHAPTER 4

Results

The purpose of this chapter is to:

Describe the acute kidney injury cohort and compare the baseline characteristics of

individuals with and without early post discharge nephrologist follow-up.

Determine the impact of early post-discharge nephrologist follow-up on the risk of

mortality and the receipt of chronic dialysis.

Describe the subgroup and sensitivity analyses

4.1 Description of the Cohort

During the 12 year study period 21,809 patients were hospitalized with acute kidney injury

requiring temporary renal replacement therapy. Of these patients, 4216 (19.4%) had an

exclusionary event [3982 had an exclusionary event in the 5 years preceding their admission to

hospital (2525 had a previous episode of AKI, 1454 previously received dialysis, and 3 had

kidney transplantation and 234 had a hospital stay greater than 180 days]. This left 17,593

(80.6%) patients with a first hospitalization for AKI-RRT. Of these subjects, 7176 died during

the index hospitalization and were subsequently excluded leaving 10,417 (47.7%) patients

surviving to discharge. Of the 10,417 patients, 6540 had an exclusionary event [6530 had an

exclusionary event within 90 days post-discharge (4409 received dialysis, 1851 were readmitted

to hospital, 270 died) and 10 had incomplete data]. Therefore, our final cohort included 3,877

(17.8%) who survived more than 90 days post-discharge without receiving dialysis or being re-

hospitalized. (Figure 1)

Of the 3,877 patients, 40.8% (n=1583) had follow-up with a nephrologist within 90 days

of hospital discharge. (Table 4) The mean duration between discharge and nephrologist follow-

up was 12 days (±19 days). The mean age of the participants was 61 years, approximately 40%

32

were women, and about one quarter lived in a rural setting. Most of the participants (>90%)

lived within 50 km of a dialysis unit. Access to healthcare was not substantially different

between the groups in the 5 years preceding hospital admission, except for prior nephrology

visits (median 0 (IQR 0-0) without follow-up vs. median 0 (IQR 0-2) with follow-up). There was

marked comorbid disease in both groups. Almost 45% of the cohort had a history of cardiac

disease, 40% had a history of cancer, and 42% had a history of diabetes. The mean Charlson

Comorbidity Index was 2.9 (± 2.3). A number of subjects in both groups had previous

procedures, with the majority being cardiac in nature.

Individuals who had post-discharge nephrologist follow-up differed from those who did

not undergo follow up on a number of baseline characteristics. (Table 4) Subjects with early

follow-up had a higher likelihood of having pre-existing CKD; a history of hematuria, and

hypertension; previous visits to a nephrologist, and a prior renal biopsy. They also had lower

rates of a previous history of cerebrovascular disease, liver disease, and a major respiratory tract

infection; sepsis, receipt of mechanical ventilation, cardiac surgery and admission to a teaching

hospital during their index hospitalization. The standardized differences for each covariate pair

were all less than 10% with the exception of teaching hospital.

Overall, 74.8% of individuals who underwent early follow-up with a nephrologist

(n=1184) were successfully matched to similar patients who did not. The covariate balance in

the matched cohort was considerably improved as demonstrated by smaller standardized

differences. (Table 5)

33

Table 4: Baseline characteristics of cohort (pre-match).

No. (%)a

Characteristic

No Nephrology

Follow-up

(n=2294)

Nephrology

Follow-up

(n=1583)

Standardized

Difference

Demographics Age, mean (SD), y

Female gender

60.7 (16.4)

929 (40.5)

60.8 (16.5)

633 (40.0)

0.01

0.01

Lowest income quintile

Rural residence

564 (24.6)

327 (14.3)

382 (24.1)

214 (13.5)

0.01

0.02

Distance from nearest dialysis unit (km) 0-50

51-150

151-300 >300

207 (90.1)

212 (9.2)

9 (0.4) 6 (0.3)

1454(91.9)

114 (7.2)

8 (0.5) 7 (0.4)

0.06

0.07

0.02 0.03

Hospital type Teaching hospital

Healthcare accessb Number of hospitalizations

Number of emergency visits

Number of GP visits Number of internist visits

Number of cardiology visits

Number of nephrology visits

1291 (56.3)

1 (0-2)

2 (1-5)

39 (18-68) 5 (1-16)

0 (0-2)

0 (0-0)

656 (41.4)

1(0-2)

2 (1-5)

40 (19-69) 7 (2-20)

0 (0-3)

0 (0-2)

0.3

0.05

0.05

0.03 0.1

0.05

0.39

Comorbid diseasec

Myocardial Infarction Coronary artery disease

Cardiac dysrythmias

Heart failure Cerebrovascular disease

Diabetes

Cancer Liver disease

Peripheral vascular disease

Chronic kidney diseasec

Hypertension

Hematuria Proteinuria

Major respiratory infection

Major genitourinary/gastrointestinal infection Major skin/soft tissue infection

Charlson comorbidity index, mean (SD), score

1053 (45.9) 890 (38.8)

338 (14.7)

881 (38.4) 475 (20.7)

959 (41.8)

916 (39.9) 388 (16.9)

75 (3.3)

282 (12.3)

1545 (67.3)

578 (25.2) 9 (0.4)

1561 (68.0)

614 (26.8) 686 (29.9)

2.9 (2.3)

664 (41.9) 595 (37.6)

206 (13.0)

573(36.2) 223 (14.1)

707 (44.7)

584 (36.9) 204 (12.9)

56 (3.5)

387 (24.4)

1131 (71.4)

479 (30.3) 11 (0.7)

985 (62.2)

354 (22.4) 443 (28.0)

2.9 (2.3)

0.08 0.02

0.05

0.05 0.17

0.06

0.06 0.11

0.01

0.33

0.09

0.11 0.04

0.12

0.1 0.04

0

Proceduresd Angiography

Coronary artery bypass grafting

Percutaneous coronary intervention Valvular surgery

Pacemaker

Echocardiography Electrocardiography

Holter monitor Stress test

Peripheral vascular procedures

Carotid doppler ultrasound Carotid endarderectomy

Renal biopsy

519 (22.6)

244 (10.6)

485 (21.1) 162 (7.1)

176 (7.7)

957 (41.7) 1919 (83.7)

242 (10.5) 707 (30.8)

46 (2.0)

302 (13.2) 17 (0.7)

47 (2.0)

293 (18.5)

141 (8.9)

271 (17.1) 66 (4.2)

94 (5.9)

686 (43.3) 1362 (86.0)

188 (11.9) 546 (34.5)

26 (1.6)

262 (16.6) 10 (0.6)

168 (10.6)

0.1

0.06

0.1 0.12

0.07

0.03 0.07

0.04 0.08

0.03

0.1 0.01

0.38

Procedure or condition during index hospitalization Sepsis

Cardiac surgery

Abdominal aortic aneurysm repair Mechanical ventilation

509 (22.2)

333 (14.5)

118 (5.1) 1474 (64.3)

196 (12.4)

143 (9.0)

37 (2.3) 602 (38.0)

0.26

0.17

0.14 0.54

a All data presented as No. (%) unless otherwise indicated. b In the 5 years preceding hospital admission expressed as median (IQR). c In the 5 years preceding the hospital discharge date except chronic kidney disease which had a lookback period of 5 years preceding the hospital admission date. d In the 5 years preceding hospital discharge date.

34

Table 5: Baseline characteristics of cohort (post-match).

`

No. (%)a

Characteristic

No Nephrology

Follow-up

(n=1184)

Nephrology

Follow-up

(n=1184)

Standardized

Difference

Demographics

Age, mean (SD), y

Female gender

61.4 (16.5)

474 (40.0)

61.0 (16.5)

473 (39.9)

0.03

0

Lowest income quintile Rural residence

279 (23.6) 156 (13.2)

280 (23.6) 170 (14.4)

0 0.03

Distance from nearest dialysis unit (km)

0-50 51-150

151-300

>300

1077 (91.0) 94 (7.9)

7 (0.6)

6 (0.5)

1072 (90.5) 99 (8.4)

7 (0.6)

6 (0.5)

0.01

Hospital type

Teaching hospital

Healthcare access b

Number of hospitalizations Number of emergency visits

Number of GP visits

Number of internist visits Number of cardiology visits

Number of nephrology visits

535 (45.2)

1 (0-2) 2 (1-5)

40 (19-69)

5 (1-16) 0 (0-3)

0 (0-1)

535 (45.2)

1(0-2) 2 (1-5)

39 (18-68)

6 (1-17) 0 (0-3)

0 (0-1)

0

0.05 0.02

0

0.02 0

0.04

Comorbid diseasec

Myocardial Infarction

Coronary artery disease Cardiac dysrythmias

Heart failure

Cerebrovascular disease Diabetes

Cancer

Liver disease Peripheral vascular disease

Chronic kidney disease

Hypertension Hematuria

Proteinuria Major respiratory infection

Major genitourinary/gastrointestinal infection

Major skin/soft tissue infection

Charlson comorbidity index, mean (SD), score

522 (44.1)

443 (37.4) 164 (13.9)

430 (36.3)

198 (16.7) 510 (43.1)

448 (37.8)

157 (13.3) 39 (3.3)

204 (17.2)

839 (69.5) 175 (14.8)

<5(0.4) 753 (63.6)

272 (23.0)

328 (27.7)

2.8 (2.3)

514 (43.4)

456 (38.5) 155 (13.1)

429 (36.2)

189 (16.0) 517 (43.7)

440 (37.2)

164 (13.9) 41 (3.5)

205 (17.3)

816 (68.9) 345 (29.1)

8 (0.7) 742 (62.7)

279 (23.6)

344 (29.1)

2.9 (2.2)

0.01

0.02 0.02

0

0.02 0.01

0.01

0.02 0.01

0

0.01 0.03

0.03 0.02

0.01

0.03

0.02

Procedured

Angiography Coronary artery bypass grafting

Percutaneous coronary intervention

Valvular surgery Pacemaker

Echocardiography

Electrocardiography Holter monitor

Stress test

Peripheral vascular procedures

Carotid doppler ultrasound

Carotid endarderectomy Renal biopsy

232 (19.6) 115 (9.7)

214 (18.1)

68 (5.7) 68 (5.7)

495 (41.8)

996 (84.1) 139 (11.7)

377 (31.8)

21 (1.8)

178 (15.0)

8 (0.7) 46 (3.9)

226 (19.1) 114 (9.6)

209 (17.7)

56 (4.7) 70 (5.9)

483 (40.8)

1000 (84.5) 123 (10.4)

378 (31.9)

17 (1.4)

177( 14.9)

7 (0.6) 49 (4.1)

0.01 0

0.01

0.05 0.01

0.02

0.01 0.04

0

0.03

0

0.01 0.01

Procedure or condition during index hospitalization

Sepsis Cardiac surgery

Abdominal aortic aneurysm repair

Mechanical ventilation

175 (14.8) 131 (11.1)

36 (3.0)

563 (47.6)

183 (15.5) 121 (10.4)

33 (2.8)

550 (46.5)

0.02 0.02

0.02

0.02

a All data presented as No. (%) unless otherwise indicated. b In the 5 years preceding hospital admission expressed as median (IQR). c In the 5 years preceding the hospital discharge date except chronic kidney disease which had a lookback period of 5 years preceding the hospital admission date. d In the 5 years preceding hospital discharge date.

35

4.2 Outcomes

Within the matched cohort, the frequency of all-cause mortality was 15.5% (n=184 patients)

among those with follow-up with a nephrologist after hospitalization. The mortality rate was

18.9% (n=224 patients) among those without follow-up with a nephrologist after hospitalization.

The incidence rate of all-cause mortality was 8.4 per 100 person-years and 10.6 per 100 person-

years among those with and without follow-up with a nephrologist, corresponding to a hazard

ratio of 0.76 (95% confidence interval [CI] 0.62-0.93). In contrast, the frequency of chronic

dialysis was 7.0% (83 patients) in patients with nephrologist follow-up, which was higher than

those without follow-up [2.7% (32 patients)]. The incidence rate was 7.0 per 100 person-years,

and 2.7 per 100 person-years among those with and without follow-up with a nephrologist,

corresponding to a hazard ratio of 2.71 (95% CI 1.76-4.19). (Figures 3 and 4; Table 6)

Table 6: Risk of all-cause mortality and chronic dialysis by group

Nephrology Follow-up

(n=1184)

No Nephrology Follow-up

(n=1184)

Outcome

No. of Events

(%)

Incidence

Rate Per 100

Person-

Years

No. of Events

(%)

Incidence

Rate Per 100

Person-

Years

Hazard Ratio

(95% Confidence

Interval)

All-cause

mortality

184 (15.5) 8.4 224 (18.9) 10.6 0.76 (0.62-0.93)

Chronic

Dialysis

83 (7.0) 7.0 32 (2.7) 2.7 2.71 (1.76-4.19)

36

0%

5%

10%

15%

20%

0 100 200 300 400 500 600 700 800

Cu

mu

lati

ve

Ris

k o

f A

ll-C

au

se

Mo

rta

lity

Follow-up, days

No Follow-up

Follow-up

Figure 3. Risk of All-Cause Mortality in Association with Early Nephrology Follow-up

37

Figure 4. Risk of Chronic Dialysis in Association with Early Nephrology Follow-up

38

4.3 Subgroup Analysis

Stratified analysis for all-cause mortality among subgroups demonstrated findings consistent

with our primary analysis: decreased all-cause mortality for patients with early nephrology

follow-up within 2 years. (Table 7) (Figure 5) Although the point estimate demonstrating a

decrease in mortality was consistent among all pre-specified subgroups, except those with no

history of peripheral vascular disease (aHR 1.00), it was only statistically significant in certain

subgroups. These subgroups included males (aHR 0.72, 95% CI 0.55-0.94), those less than 65

years of age (aHR 0.56, 95% CI 0.42-0.85), those with a history of diabetes (aHR 0.66, 95% CI

0.48-0.90), those with no previous nephrology visits (aHR 0.75, 95% CI 0.85-0.97) and those

with no history of liver disease (aHR 0.79, 95% CI 0.64-0.98). Within subgroups, there was only

a significant difference in the HR among those less than 65 and greater than and equal to 65

years old (p=0.00).

39

Table 7: Risk of all-cause mortality stratified by subgroup.

Subgroup Lower

Limit

Upper

Limit

Adjusted

Hazard

ratio

Log hazard

ratio

Z statistic p-value 2sided p

Age

3.451438 0 0

≥65 0.71 1.18 0.911 -9.32E-02

<65 0.42 0.85 0.595 -0.519193873

Hospital Type

Teaching Hospital 0.56 1.06 0.773 -0.25747623

Non-Teaching

Hospital

0.57 0.98 0.744 -0.295714244

0.37615 0.352 0.704

Admission Era

1996-2001 0.42 0.79 0.577 -0.549913012

2001-2008 0.69 1.17 0.9 -0.105360516 -2.60923 0.005 0.01

Gender

Male 0.55 0.94 0.72 -0.328504067

Female 0.61 1.17 0.846 -0.167235919 -1.03178 0.152 0.304

Residence

Rural 0.39 1.18 0.677 -0.390084006

Urban 0.66 1.02 0.819 -0.199671195 -0.56131 0.288 0.576

Previous Nephrology

Visit

Yes 0.64 1.25 0.889 -0.117658043

No 0.58 0.97 0.75 -0.287682072 1.119185 0.131 0.262

Charlson Score

≤2

>2

0.57

0.63

1.17

1.04

0.815

0.809

-0.204567166

-0.211956362

0.165727

0.433 0.866

History of Myocardial

Infarction

Yes

No

0.62 1.1 0.825 -0.192371893

0.60 1.05 0.791 -0.234457311 0.430481 0.334 0.668

History of Diabetes

Yes 0.48 0.90 0.656 -0.42159449

No 0.60 1.05 0.791 -0.234457311 -1.01166 0.156 0.312

History of CKD

Yes 0.64 1.44 0.958 -4.29E-02

No 0.63 1.02 0.801 -0.221894332 0.990191 0.184 0.368

History of Liver

Disease

Yes 0.32 1.14 0.6 -0.510825624

No 0.64 0.98 0.789 -0.236988958 -0.72707 0.233 0.466

History of PVD

Yes 0.66 1.00 0.813 -0.207024169

No 0.40 2.52 1 -1.477 0.074 0.148

40

Hazard Ratio and 95% CI

Hospital Type

Non-teaching Teaching Peripheral Vascular Disease Absent Present Liver Disease Absent Present Chronic Kidney Disease Absent Present Diabetes Absent Present Myocardial Infarction Absent Present Charlson Score >2 ≤2 Previous Nephrology Visit No Yes Rural Residence No Yes Sex Female Male **Age <65 ≥65

Mortality

Decreased Increased

Figure 5: Association of nephrology follow-up with all-cause mortality within subgroups

0.25 0.5 1 2 4

41

4.4 Sensitivity Analyses

4.4.1 Patients with a Discharge Serum Creatinine Measurement

Among the matched cohort, 530 patients (33%) had serum creatinine values at time of discharge

from hospital, of which 47% were in the early nephrology care visit group and 53% were in the

other group. Similar to the overall cohort there were a number of differences in baseline

characteristics between individuals that had early follow-up and those which did not. (Table 8)

Particularly, patients who had follow-up with a nephrologist after discharge were older, lived

farther away from a dialysis unit; and had a history of hypertension and chronic kidney disease.

The mean serum creatinine at time of discharge was 270 µmol/L (±147 µmol/L) for patients with

early follow-up compared to 139 µmol/L (±94 µmol/L) in those without early follow-up. Almost

75% of those with early follow-up had Stage IV/V CKD; whereas the majority of patients

without follow-up had Stage II/III CKD. Patients without early follow-up had a higher rate of

cardiovascular procedures in the preceding 5 years and had higher rates of sepsis, cardiac surgery

and ICU admission during their index hospitalization.

Of the 530 patients, 102 patients with early follow-up were matched to 102 patients

without early follow-up using the modified propensity scores (which included serum creatinine).

There was marked improvement in covariate balance in the matched cohort. (Table 9)

In keeping with the primary analysis, there was a decrease in the point estimate for all-

cause mortality (HR 0.84; 95% CI 0.43-1.64) and increased receipt chronic dialysis (HR 1.50;

95% CI 0.25-8.90) in patients who visited a nephrologist post-discharge.

42

Table 8: Baseline characteristics of patients with discharge creatinine values.

No. (%)a

Characteristic

No Nephrology

Follow-up

(n=280)

Nephrology

Follow-up

(n=250)

Standardized

Difference

Demographics

Age, mean (SD), y Female gender

60.0 (15.7) 108 (38.6)

62.4 (14.6) 109 (43.6)

0.16 0.1

Lowest income quintile

Rural residence

66 (23.6)

49 (17.5)

66 (26.4)

57 (22.8)

0.07

0.13 Distance from nearest dialysis unit (km)

0-50

51-150 151-300

249 (88.9)

29 (10.4) 2 (0.7)

207 (82.8)

43 (17.2) 0 (0)

0.18

0.2 0.12

Hospital type Teaching hospital

Healthcare accessb Number of hospitalizations

Number of emergency visits

Number of GP visits Number of internist visits

Number of cardiology visits

Number of nephrology visits

252 (90.0)

1 (0-2)

3 (1-6)

39 (22-67) 7 (2-21)

1 (0-3)

0 (0-1)

205 (82.0)

1(0-2)

3 (1-5)

40 (19-69) 9 (2-21)

0 (0-2)

0 (0-2)

0.23

0.03

0.05

0.06 0.05

0.14

0.3

Comorbid diseasec

Myocardial Infarction Coronary artery disease

Cardiac dysrythmias Heart failure

Cerebrovascular disease

Diabetes Cancer

Liver disease

Peripheral vascular disease Chronic kidney disease

Hypertension

Hematuria Proteinuria

Major respiratory infection

Major genitourinary/gastrointestinal infection Major skin/soft tissue infection

Charlson comorbidity index, mean (SD), score

115 (41.1) 90 (32.1)

37 (13.2) 95 (33.9)

53 (18.9)

127 (45.4) 124 (44.3)

61 (21.8)

7 (2.5) 38 (13.6)

186 (66.4)

83 (29.6) 1 (0.4)

183 (65.4)

67 (23.9) 96 (34.3)

3.1 (2.5)

103 (41.2) 89 (35.6)

33 (13.2) 82 (32.8)

36 (14.4)

120 (48.0) 100 (40.0)

30 (12.0)

9 (3.6) 71 (28.4)

184 (73.6)

67 (26.8) 2 (0.4)

153 (61.2)

50 (20.0) 78 (31.2)

2.9 (2.1)

0 0.07

0 0.02

0.12

0.05 0.09

0.26

0.06 0.37

0.16

0.06 0.06

0.09

0.09 0.07

0.08

Proceduresd

Angiography

Coronary artery bypass grafting

Percutaneous coronary intervention

Valvular surgery

Pacemaker Echocardiography

Electrocardiography

Holter monitor Stress test

Carotid doppler ultrasound

Renal biopsy

54 (19.3)

33 (11.8)

64 (22.9)

16 (5.7)

40 (14.3) 95 (33.9)

244 (87.1)

242 (10.5) 81 (28.9)

32 (11.4)

1 (0.4)

39 (15.6)

21 (8.4)

37 (14.8)

8 (3.2)

19 (7.6) 93 (37.2)

225 (90.0)

188 (11.9) 93 (37.2)

41 (16.4)

34 (13.6)

0.1

0.11

0.21

0.12

0.21 0.07

0.09

0.04 0.18

0.14

0.55

Procedure or condition during index hospitalization

Sepsis Cardiac surgery

Abdominal aortic aneurysm repair

Mechanical ventilation

63 (22.5) 51 (18.2)

14 (5.0)

197 (70.4)

30 (12.0) 17 (6.8)

10 (4.0)

96 (38.4)

0.28 0.35

0.05

0.68

Laboratory results

Discharge serum creatinine, mean (SD), µmol/L Chronic kidney disease stagef

Stage I

Stage II Stage III

Stage IV

Stage V

139.2 (94.3)

48 (17.1)

72 (25.7) 108 (38.6)

45 (16.1)

7 (2.5)

270.4 (147.5)

4 (1.6)

12 (4.8) 55 (22.0)

117 (46.8)

62 (24.8)

1.07

0.54

0.6 0.36

0.71

0.7 a All data presented as No. (%) unless otherwise indicated. b In the 5 years preceding hospital admission expressed as median (IQR). c In the 5 years preceding the hospital discharge date except chronic kidney disease which had a lookback period of 5 years preceding the hospital admission date. d In the 5 years preceding hospital discharge date.

eTable 1. Characteristics of hospitalized individuals by group

43

Table 9: Characteristics of patients with discharge creatinine values post-matching.

No. (%)a

Characteristic

No Nephrology

Follow-up

(n=102)

Nephrology

Follow-up

(n=102)

Standardized

Difference

Demographics

Age, mean (SD), y Female gender

65.2 (13.9) 42 (41.2)

62.0 (15.8) 39 (38.2)

0.21 0.06

Lowest income quintile

Rural residence

21 (20.6)

21 (20.6)

26 (25.5)

17 (16.7)

0.12

0.10 Distance from nearest dialysis unit (km)

0-50

51-150

87 (85.3)

15 (14.7)

90 (88.2)

12 (11.8)

0.09

0.09

Hospital type Teaching hospital

Healthcare accessb Number of hospitalizations

Number of emergency visits

Number of GP visits Number of internist visits

Number of cardiology visits

Number of nephrology visits

93 (91.2)

1 (0-2)

3 (1-6)

42 (24-71) 8 (2-17)

1 (0-3)

0 (0-1)

93 (91.2)

1 (0-2)

2 (1-5)

43 (17-75) 7 (2-20)

1 (0-3)

0 (0-1)

0

0.02

0

0.01 0.05

0.01

0.09

Comorbid diseasec

Myocardial Infarction Coronary artery disease

Cardiac dysrythmias Heart failure

Cerebrovascular disease

Diabetes Cancer

Liver disease

Peripheral vascular disease Chronic kidney diseaseb

Hypertension

Hematuria Proteinuria

Major respiratory infection

Major genitourinary/gastrointestinal infection Major skin/soft tissue infection

Charlson comorbidity index, mean (SD), score

42 (41.2) 29 (28.4)

12 (11.8) 31 (30.4)

14 (13.7)

47 (46.1) 43 (42.2)

14 (13.7)

2 (2.0) 22 (21.6)

77 (75.5)

28 (27.5) 0 (0)

67 (65.7)

24 (23.5) 38 (37.3)

2.9 (2.4)

45 (44.1) 30 (29.4)

15 (14.7) 32 (31.4)

18 (17.6)

49 (48.0) 46 (45.1)

17 (16.7)

4 (3.9) 23 (22.5)

75 (73.5)

28 (27.5) 1 (1.0)

69 (67.6)

23 (22.5) 36 (35.3)

3.1 (2.1)

0.06 0.02

0.09 0.02

0.11

0.04 0.06

0.08

0.12 0.02

0.04

0 0.14

0.04

0.02 0.04

0.11

Proceduresd

Angiography

Coronary artery bypass grafting Percutaneous coronary intervention

Valvular surgery

Pacemaker Echocardiography

Electrocardiography

Holter monitor Stress test

Carotid doppler ultrasound

Renal biopsy

14 (13.7)

6 (5.9) 13 (12.7)

5 (4.9)

8 (7.8) 39 (38.2)

94 (92.2)

7 (6.9) 33 (32.4)

15 (14.7)

1 (1.0)

15 (14.7)

10 (9.8) 15 (14.7)

4 (3.9)

10 (9.8) 38 (37.3)

92 (90.2)

7 (6.9) 35 (34.3)

16 (15.7)

4 (3.9)

0.03

0.15 0.06

0.05

0.07 0.02

0.07

0 0.04

0.03

0.19

Procedure or condition during index hospitalization

Sepsis Cardiac surgery

Abdominal aortic aneurysm repair

Mechanical ventilation

18 (17.6) 7 (6.9)

7 (6.9)

52 (51.0)

16 (15.7) 11 (10.8)

6 (5.9)

57 (55.9)

0.05 0.14

0.04

0.1

Laboratory results

Discharge serum creatinine, mean (SD), µmol/L Chronic kidney disease stagee

Stage I

Stage II Stage III

Stage IV

Stage V

188.5 (128.5)

5 (4.9)

18 (17.6) 38 (37.3)

34 (33.3)

7 (6.9)

202.4 (93.9)

3 (2.9)

10 (9.8) 35 (34.3)

47 (46.1)

7 (6.9)

0.12

0.1

0.23 0.06

0.26

0 a All data presented as No. (%) unless otherwise indicated. b In the 5 years preceding hospital admission expressed as median (IQR). c In the 5 years preceding the hospital discharge date except chronic kidney disease which had a lookback period of 5 years preceding the hospital admission date. d In the 5 years preceding hospital discharge date.

eTable 2. Characteristics of hospitalized individuals by group post-matcha

44

4.4.2 Dialysis censored cohort

Repeating the primary analysis while censoring for dialysis yielded an incidence of all-cause

mortality of 7.3 per 100-patient years in patients with follow-up compared to 10.9 per 100

patient-years in those without follow-up. The frequency of all-cause mortality within 2 years

from cohort entry was 14.1% (n=167) and 18.3% (n=217) in those with and without follow-up,

corresponding to a hazard ratio of 0.71 (95%CI 0.58-0.89). This trend was similar to that seen in

the primary analysis.

4.4.3 High Dimensional Propensity Score

The algorithm for the HDPS yielded 8 additional candidate covariates.(Table 10) These included

4 OHIP codes of which three were diagnostic codes and one was a fee/procedural code; and 3

DAD diagnostic codes of which two were fee/procedural and one was diagnostic. Of these

codes, we eliminated all but OHIP codes 531 and 519 as we felt the other candidate covariates

were not likely to be confounders. Including the OHIP diagnostic codes in our PS yielded a

total sample size of 1584 with 782 individuals in the exposed group and 782 individuals in the

unexposed group. Although the standardized differences after matching for each covariate pair

included in our PS were slightly smaller than in our traditional PS, this came at a cost of losing

782 patients (392 in each group). Despite the increased precision afforded by the HDPS, we

abandoned further analysis using this model as we felt it did not substantially add extra

information to our analysis and may have limited our statistical power to detect a difference

between the two groups.

45

Table 10: Additional covariates specified by the high dimensional propensity score.

Dimension Code

Diagnostic code – OHIP

Diagnostic code – OHIP

Diagnostic code – OHIP

Fee code – OHIP

Diagnostic Code – DAD

Fee code – DAD

Fee code – DAD

519 – Other diseases of the respiratory system

531 – Gastric ulcer with or without hemorrhage or perforation

769 – Respiratory distress syndrome

G082 – Continuous venovenous hemodiafiltration

T81 – Complications of procedure, not elsewhere classified

2GM71- Biopsy, bronchus using endoscopic per orifice approach

1OT52 – Draining, abdominal cavity

4.4.4 Test of Specificity

Our test of specificity demonstrated no association between dermatology or psychiatry

follow-up within 90 days of hospital discharge and all-cause mortality (HR 0.81; 95% CI 0.56-

1.19). Similarly there was a non-significant association for the receipt of chronic dialysis (HR

1.5; 95% CI 0.84-1.19). (Table 11)

46

Table 11: Risk of all cause mortality and chronic dialysis for tracer exposure.

Dermatology or Psychiatry Follow-

up

(n=452)

No Dermatology or Psychiatry

Follow-up

(n=452)

Outcome No. of Events (%)

Incidence

Rate Per 100

Person-Years

No. of Events (%)

Incidence

Rate Per 100

Person-Years

Hazard Ratio

(95% Confidence Interval)

All-cause

mortality

49 (10.8) 5.8 64 (14.2) 7.7 0.81 (0.56-

1.19)

Chronic

Dialysis

14 (3.1) 1.7 10 (2.2) 1.2 1.5 (0.84-

4.27)

4.5 Additional Analyses

4.5.1 Healthcare Utilization

Descriptive analysis of health services utilization and cardiac procedures among the matched

cohort failed to demonstrate marked differences in post-hospitalization care between the groups.

Individuals with early nephrologist follow-up after hospitalization did not have increased rates of

hospitalizations, emergency room visits, cardiology visits, neurologist visits, or cardiac

procedures. Notably, individuals with nephrology follow-up did have an increased number of

subsequent nephrology visits (median 2 visits versus 0 visits) and internist visits (7 visits versus

4 visits). (Table 12) The time from first nephrology consultation to initiation of dialysis was 372

days (±202) in those with early nephrology consultation versus 94 days (±166) in those without

early consultation. Every patient initiating chronic dialysis had a nephrology consultation prior

to this.

47

Table 12: Characteristics of post- discharge health services utilization and procedures in

matched cohort.

Characteristic

No Nephrology

Follow-up

(n=1184)

Nephrology

Follow-up

(n=1184)

Absolute

Standardized

Difference

Measures of healthcare access, median (IQR)

Number of hospitalizations

Number of emergency visits

Number of GP visits

Number of internist visits

Number of cardiology visits

Number of nephrology visits

0 (0-2)

1 (0-3)

20 (9-38)

4 (0-12)

0 (0-2)

0 (0-1)

0 (0-1)

1 (0-4)

18 (9-32)

7 (2-17)

0 (0-2)

2 (0-6)

0.04

0.05

0.13

0.23

0

0.3

Procedure, n (%)

Angiography

Coronary artery bypass grafting

Percutaneous coronary intervention

Valvular surgery

Pacemaker

Echocardiography

Electrocardiography

Holter monitor

Stress test

Carotid doppler ultrasound

Renal biopsy

67 (5.7)

6 (0.5)

38 (3.2)

5 (0.4)

43 (3.6)

319 (26.9)

715 (60.4)

70 (5.9)

205 (17.3)

63 (5.3)

6 (0.5)

68 (5.7)

8 (0.7)

35 (3.0)

6 (0.5)

32 (2.7)

352 (29.7)

774 (65.4)

75 (6.3)

224 (18.9)

78 (6.6)

5 (0.4)

0

0.02

0.01

0.01

0.05

0.06

0.1

0.02

0.04

0.05

0.01

4.5.2 Unmatched Cohort

There were 399 (25.2%) subjects who had early follow-up with a nephrologist and could

not be matched to a suitable control.(Table 13) The unmatched group had a much higher

proportion of patients with a history of chronic kidney disease (45.6% vs. 17.3%) and previous

nephrology visits (66.9% versus 27.2%). Their frequency of all-cause mortality and receipt of

chronic dialysis were 17.5% (70 patients) and 12.8% (51 patients) respectively, which were

higher than for the 1184 matched patients.

48

Table 13: Characteristics of matched versus unmatched cohort.

Characteristic

Nephrology Follow-up

Unmatched

(n=399)

Nephrology Follow-up

Matched

(n=1184)

Demographics

Age, mean (SD), y

Female gender

60.4 (16.5)

160 (40.1)

61.0 (16.5)

473 (39.9)

Low income quintile

Rural residence

102 (25.6)

44 (11.0)

280 (23.6)

170 (14.4)

Distance from nearest dialysis unit (km)

0-50

51-150

151-300

>300

382 (95.7)

15 (3.8)

1 (0.3)

1 (0.3)

1072 (90.5)

99 (8.4)

7 (0.6)

6 (0.5)

Hospital type

Teaching hospital

Healthcare access in the 5 y preceding hospital admission date

Number of hospitalizations

Number of emergency visits

Number of GP visits

Number of internist visits

Number of cardiology visits

Number of patients with nephrology visits, mean

121 (30.3)

1 (0-2)

2 (1-5)

42 (23-73)

11 (3-27)

1 (0-3)

267 (66.9)

535 (45.2)

1(0-2)

2 (1-5)

39 (18-68)

6 (1-17)

0 (0-3)

322 (27.2)

Comorbid disease in the 5 y preceding hospital discharge dateb

Myocardial Infarction

Coronary artery disease

Cardiac dysrythmias

Heart failure

Cerebrovascular disease

Diabetes

Cancer

Liver disease

Peripheral vascular disease

Chronic kidney diseaseb

Hypertension

Hematuria

Proteinuria

Major respiratory infection

Major genitourinary/gastrointestinal infection

Major skin/soft tissue infection

Charlson comorbidity index, mean (SD), score

150 (37.6)

139 (38.4)

51 (12.8)

144 (36.1)

34 (8.5)

190 (47.6)

144 (36.1)

40 (10.0)

15 (3.8)

182 (45.6)

315 (78.9)

134 (33.6)

3 (0.8)

243 (60.9)

75 (18.8)

99 (24.8)

3.0 (2.3)

514 (43.4)

456 (38.5)

155 (13.1)

429 (36.2)

189 (16.0)

517 (43.7)

440 (37.2)

164 (13.9)

41 (3.5)

205 (17.3)

816 (68.9)

345 (29.1)

8 (0.7)

742 (62.7)

279 (23.6)

344 (29.1)

2.9 (2.2)

Procedures in the 5 y preceding hospital discharge date

Angiography

Coronary artery bypass grafting

Percutaneous coronary intervention

Valvular surgery

Pacemaker

Echocardiography

Electrocardiography

Holter monitor

Stress test

Peripheral vascular procedures

Carotid doppler ultrasound

Carotid endarderectomy

Renal biopsy

67 (16.8)

27 (6.8)

62 (15.5)

10 (2.5)

24 (6.0)

203 (50.9)

362 (90.7)

65 (16.3)

168 (42.1)

9 (2.3)

85 (23.1)

9 (0.8)

119 (29.8)

226 (19.1)

114 (9.6)

209 (17.7)

56 (4.7)

70 (5.9)

483 (40.8)

1000 (84.5)

123 (10.4)

378 (31.9)

17 (1.4)

177( 14.9)

7 (0.6)

49 (4.1)

Procedure or condition during index hospitalization

Sepsis

Cardiac surgery

Abdominal aortic aneurysm repair

Mechanical ventilation

13 (3.3)

20 (5.0)

4 (1.0)

52 (13.0)

183 (15.5)

121 (10.4)

33 (2.8)

550 (46.5)

a All data presented as No. (%) unless otherwise indicated.

b Chronic kidney disease lookback period of 5 years preceding the hospital admission date.

49

Chapter 5

Discussion

The purpose of this chapter is to:

Discuss the major findings of the study.

Discuss the strengths of the study.

Discuss the limitations of the study.

Discuss the implications of the study.

5.1 Major Findings

In this large population-based cohort study of survivors of acute kidney injury requiring in-

hospital dialysis, we have demonstrated that a visit with a nephrologist within 90 days of

discharge was associated with a 24% lower mortality rate at 2 years (HR 0.73; 95% CI 0.63-

0.93). This finding was consistent among all important subgroups. Follow-up was also

associated with a statistically significant increase in the rate of chronic dialysis (HR 2.71; 95%

CI 1.76-4.19) within 2 years.

Sensitivity analysis comprising 530 patients with a serum creatinine measurement at time

of discharge confirmed our primary findings of decreased mortality and increased need for

chronic dialysis among those with follow-up compared to those without follow-up, although

these results were not statistically significant [(mortality: HR 0.84; 95% CI 0.43-1.64) and

(chronic dialysis: HR 1.50; 95% CI 0.25-8.90)]. Our sensitivity analysis also demonstrated that

the mortality benefit imparted by follow-up extends beyond those patients who received chronic

dialysis.

50

5.2 Interpretation

The benefit of specialist follow-up on important clinical outcomes has been well

established for an array of acute conditions across many disciplines. However, aside from a

single case-series by Khan et al.95

, the impact of nephrologist follow-up in survivors of AKI has

not been studied. Khan described the effect of post-discharge follow up with a nephrologist in

patients who were hospitalized with AKI of varying severity at a single centre in the United

Kingdom during the period 1989-1990. Among the 310 patients with AKI studied, those

classified as ―high-risk‖ based on clinical and demographic characteristics demonstrated a

survival benefit at two years. Despite this result, Khan’s study had many limitations, including

its single centre and small sample size, arbitrary definition for AKI, and its failure to account for

potential confounders given its case series design. Furthermore, not all patients in the ―high

risk‖ subgroup had dialysis requiring AKI. As such, it was difficult to draw any meaningful

conclusions from Khan’s study.

In contrast, our study was designed to evaluate the effect of early follow-up with a

nephrologist on the long-term outcomes of AKI among survivors of AKI receiving temporary

dialysis in a large population with universal access to healthcare. Similar to previous studies of

AKI survivors,3;6;46

patients in our matched cohort possessed a large burden of comorbid illness.

Some historical reports have demonstrated that referral of such patients to a nephrologist may be

considered futile by some practitioners thereby leading to selection bias whereby healthier

patients are selectively referred.96;97

To mitigate the impact of the inevitable bias associated with

referral practices, we matched patients who received nephrology follow-up with those who did

not using a propensity score. To further reduce the possibility of survivor-treatment bias, we also

required a 90-day period following hospital discharge in which an individual survived and was

free of dialysis. We expected that this time period would appropriately exclude individuals with

a high burden of disease in whom early nephrology visit would be of little impact.

We demonstrated that follow-up with a nephrologist after an episode of severe AKI was

associated with decreased mortality within 2 years. While the mechanism underlying this

association is uncertain, a possible explanation is that follow-up may have enhanced the quality

of care of survivors by targeting the maladaptive physiological changes that ensue as a

consequence of AKI.

51

Basic science data has shown that in the kidney, ischemia-reperfusion injury causes

permanent histological changes, including interstitial fibrosis and loss of the renal vasculature,

which occur even if there is recovery of renal function.58;98;99

In time, these changes lead to

functional impairments such as proteinuria and hypertension which promote deterioration of

kidney function and cardiovascular mortality.47;56;100

The degree of structural and functional

changes in the kidney correlate with the severity of the episode of AKI, such that survivors of

AKI requiring dialysis possess the highest risk of progression to CKD, dialysis and mortality.101

Even in patients without a history of CKD, defined as an eGFR > 60 ml/min, dialysis requiring

AKI is associated with a 500 fold increased risk of progressing to Stage 4 CKD.45

Nephrologists may be uniquely positioned to attenuate these risks and manage the

complications that ensue from progressive renal decline. While internists are well versed in the

management of hypertension and hyperlipidemia in patients with renal dysfunction, the potential

value added by nephrology care is the management of the anemia, acidemia, electrolyte

disorders, malnutrition and bone disease that accompany renal decline; as well as the preparation

for ESRD. Indeed, interventions targeting these conditions have been found to reduce mortality

and preserve kidney function.102-107

Unfortunately, our study was not designed to measure these

processes of care, and therefore, the impact of these interventions is merely speculative.

Additionally, early follow-up with a nephrologist may allow for the timely initiation of

dialysis, possibly with permanent vascular access in place among select patients. In the

predialysis population, delayed nephrology care is associated with several unfavorable outcomes.

It may lead to reduced access to peritoneal dialysis (PD), which may impact on the quality of life

of patients.108

Moreover, it may also restrict access to kidney transplantation, which may

decrease patient survival and quality of life.109;110

Among patients who start hemodialysis,

delayed nephrological care is associated with increased rates of dialysis initiation through a

central venous catheters (CVCs) instead of arteriovenous fistulas (AVFs) or grafts.111;112

Initiation of dialysis with CVCs is associated with an increased risk of mortality, compared to

initiation with an AVF or graft.113;114

Patients with CKD who consult a nephrologist relatively

later or infrequently also experience a considerable excess mortality rate after starting

maintenance dialysis, especially during the first few months of dialysis initiation.115

52

In our study, individuals who had early nephrology follow-up had higher rates of

initiation of chronic dialysis, which may be attributed primarily to the timely initiation of

dialysis. As demonstrated in our sensitivity analysis patients with follow-up tended to have

higher serum creatinine levels at the time of discharge. In fact, a majority of these patients had

Stage IV and V CKD and therefore were at higher risk of having uremic symptoms,

hyperkalemia and volume overload and therefore were more likely to start dialysis in a timely

manner.

However, there are other reasons which may explain the higher rate of chronic dialysis

among survivors with nephrology follow-up. First, our inability to adjust for discharge serum

creatinine for our entire matched cohort may have led to confounding by indication whereby

patients with higher discharge creatinine values and thereby higher risk for progression to

chronic dialysis, were preferentially referred to a nephrologist. Another possibility is that

interventions undertaken by nephrologists, such as initiation of blockers of the renin-angiotensin

system or diuretics, may have hastened the decline in renal function in survivors. Patients with

CKD are at higher risk of developing AKI.116

In our study, patients with nephrology follow-up

had an increased burden of CKD, as demonstrated by our sensitivity analysis; therefore, they

were more likely to develop subsequent episodes of AKI which may have been severe enough to

require initiation of chronic dialysis. Furthermore, this group may have had greater access to

dialysis services, which may have hastened the initiation of dialysis. It has been recently shown

that between 1996 and 2006 patients aged greater than 65 had initiated dialysis with increasingly

preserved kidney function, from an estimated eGFR of 8 ml/min in 1996 to 12 ml/min in 2006.117

Although the timely initiation of dialysis may have contributed to the decreased mortality

among patients with follow-up with a nephrologist seen in our study, our sensitivity analysis

demonstrated that the survival benefit seen in those with early follow-up extended beyond those

patients initiated on chronic dialysis. This implies that the benefits conferred by nephrological

follow-up are not just related to dialysis initiation, but to other processes of care.

Moreover, patients with nephrologist follow-up may also have benefited from improved

access to specialist care for comorbid conditions such as diabetes, coronary artery disease and

COPD. Our descriptive analysis of post hospitalization healthcare utilization showed that

patients with nephrologist follow-up had more visits with internists in the ensuing two years.

53

Combined care among nephrologists, internists, and primary care practitioners may lead to

better management of common coexisting conditions such as coronary artery disease and

diabetes, as well as timely referral for lifesaving medical procedures such as coronary artery

bypass and revascularization.68;107

Indeed, the merits of combined care on survival have been

demonstrated in multiple disciplines including nephrology.64;68;118

Although we found no

difference in the receipt of cardiac medical procedures between the two groups after discharge,

the number of events was low. Moreover, there may have been other lifesaving interventions that

we did not account for in our descriptive analysis that may have impacted on the survival benefit.

On a similar note, the timing of follow-up may also be an important determinant of

patient survival. Ishani et al. recently demonstrated that the risk of adverse events related to an

episode of AKI is greatest within the first 3 to 24 months after the episode.8 This may be related

to the initiation of new medications, alterations in usual medications, or comorbid conditions,

such as cardiac disease.119

Studies in other medical specialties have also corroborated the

importance of the timing of follow-up.69;120;121

For example, in patients with congestive heart

failure, follow-up by a cardiologist within 2 weeks of hospital discharge for a heart failure

admission was associated with a significant decrease in 30-day readmission and mortality.69

As

such, our 90 day follow-up period may have allowed for the early identification of high-risk

individuals and therefore timely introduction of interventions, which may have led to our

observed survival benefit.

Finally, improvements in continuity of care in patients with follow-up may have

contributed to the survival benefit demonstrated. Continuity of care involves three inclusive

dimensions—continuity in information, continuity in management, and continuity in the patient-

physician relationship.122

It is especially important for patients with a large burden of chronic

disease as they are most likely to benefit from informed management and shared decision

making among their healthcare providers.123

There are numerous benefits to continuity of care

including: improved patient satisfaction, increased use of appropriate preventive health services,

greater medication adherence, lower hospitalization rates, and lower cost. In the field of

nephrology, continuity of care can also impart a graded survival benefit commensurate with

increasing nephrology visits, especially in patients with CKD not on dialysis. 118

Our

descriptive analysis found that patients with follow-up with a nephrologist had more nephrology

54

visits over the ensuing two years, which may have improved their quality of care and

contributed to their survival benefit.

5.3 Strengths

Our study has several strengths. First, it is a novel study that uses a population-based

design to determine the benefit of early follow-up with a nephrologist in a cohort that has the

highest risk for progression to CKD and death among AKI survivors. The use of a population-

based design allowed us to capture a large sample size of survivors of severe AKI and adequately

power our study for our primary outcome.

Second, we used rigorous propensity score methods to minimize selection bias and

undertook multiple sensitivity analyses to demonstrate our findings are robust.

We also excluded patients who were readmitted, required dialysis or died within 90 days of

discharge. Exclusion of patients who had received dialysis within 90 days of discharge was

important all these patients had seen a nephrologist for this procedure to be initiated. Including

them in the cohort would had led to reverse causality whereby the need for dialysis prompted

nephrology follow-up.

Finally, our study design protected against immortal time bias.124

Immortal time refers

to a span of time in the observation or follow-up period of a cohort during which the outcome

under study could not have occurred. It usually occurs with the passing of time before a subject

initiates a given exposure. While a subject is not truly immortal during this time span, the subject

necessarily had to remain event free until start of exposure to be classified as exposed. In our

study, all patients had to survive to 90 days post discharge and had to have follow-up with a

nephrologist prior to this time, which ensured that no immortal time bias exists.

5.4 Limitations

Our study has a number of limitations. First, as an observational study, our results demonstrate

an association but do not prove causation. While randomized control trials are best suited to

demonstrate causality, our population is of sufficiently high-risk that a randomized study design

55

would be both unfeasible and unethical. However, given that we studied a selected population

with the most severe form of AKI, our results may not be generalizable to patients with less

severe episodes of AKI. As such, similar cohort studies using different samples of patients with

varying degrees of AKI are needed to confirm our findings.

Second, despite our rigorous matching and adjustment on important variables,

confounding by indication could still occur due to unmeasured confounders. We employed

propensity score modeling as a means to adjust for a large amount of confounders. While

propensity scores are a powerful method to minimize bias in observational trials, they are subject

to bias due to unmeasured confounders. 85

Indeed, in our primary analysis we were unable to

account for potential unmeasured confounders including laboratory data of which residual

kidney function at the time of discharge was most important. As demonstrated in our sensitivity

analysis, patients with higher discharge serum creatinine, were more likely to be referred for

nephrology follow-up. This is in contrast to some studies which have demonstrated that an

inverse referral bias may exist whereby physicians would be less willing to refer some patients

with high creatinine levels to nephrologists. According to physicians following this philosophy

of care, some patients with high creatinines may not benefit from nephrological care including

dialysis as they possess a large burden of comorbid illness and referral for nephrological care

would bring undue hardship without any demonstrable benefit. While this may be plausible, our

sensitivity analysis does not confirm this practice is occurring to a large extent in our cohort.

Furthermore, our propensity score methods were able to account for a myriad of comorbid

conditions in the two groups that may have affected referral practices. As CKD is a potent risk

factor for mortality, the inability to adjust for this confounder using discharge serum creatinine

values in our primary analysis should have tended to bias the hazard ratio for mortality towards

the null hypothesis.47

However, we demonstrated a robust relationship between nephrology

follow-up and mortality, implying that the magnitude of the protective effect of early follow-up

may be even greater than demonstrated.

Furthermore, our test of specificity using a tracer exposure was able to mitigate further

concerns regarding residual confounding as demonstrated by a lack of a statistically significant

association between psychiatry or dermatology consultation and mortality. This group of

patients was fundamentally different than our matched cohort on a number of baseline

56

characteristics – they were younger, had substantially less comorbid illness (including CKD)

and procedures; and had less severe hospital admissions. Given this, one would have expected a

statistically significant decrease in mortality in this group, which was not the case.

Third, we completed our study using administrative data, and had to rely on diagnosis

and procedure codes to identify exposure, outcome and covariates. As such, there may be issues

related to the validity of our exposure, cohort and the variables included in our propensity score.

As previously mentioned, the primary exposure for this study was at least one outpatient

nephrology consultation within 90 days of discharge from the index hospitalization defined using

the OHIP database. In the OHIP database, the specialty code for nephrology (specialty code 16)

was introduced in October 2009. Prior to this, nephrologists were coded in the same category as

internal medicine (specialty code 13). This required us to use an algorithm developed by Wald et

al., which has face validity, but has not been formally validated, to define a nephrologist. 38

We

compared our results to those obtained using a different algorithm for defining a nephrologist

using OHIP data by Jain et al., which has been used in a previous study. 82

Jain et al.’s algorithm

identified nearly similar numbers of nephrologists in Ontario when compared to the ICES

Physicians Database (IPDB), as the gold standard, for the years 1999-2005. Using kappa

statistics we demonstrated a high degree of agreement between our definition of nephrologist and

Jain et al.’s definition with the IPDB. However, our definition yielded a slightly greater number

of exposed individuals compared to Jain et al.’s definition.

The use of administrative data may also misclassify individuals with AKI, as this code in

isolation has fairly low sensitivity but high specificity. However, the combination of a

diagnostic code for AKI and a procedure code for dialysis demonstrated greater than 90%

sensitivity and specificity in a recent validation study13

, which mitigates any misclassification of

our cohort.

Our secondary outcomes of chronic dialysis and AKI may have also been susceptible to

misclassification. Although we used widely accepted definition for chronic dialysis that has

been employed in many studies, we may have misclassified individuals who truly developed end

stage renal disease but died in the first 90 days after commencing chronic dialysis.3;125

Similarly,

as previously mentioned AKI has poor sensitivity (19-28%) but excellent specificity. As such,

57

patients with true AKI may be misclassified as not having this condition and thus

underrepresented in administrative databases.

Furthermore, administrative databases show poor accuracy in defining certain comorbid

conditions which we included in our propensity score. For example, a recent systematic review

of validating administrative data codes for AKI, CKD and dialysis demonstrated that chronic

kidney disease is poorly sensitive but highly specific (19-80% Sensitivity; 97-100%

Specificity).126

As with AKI, administrative databases may be underreporting the true number of

patients with this condition.

Fourth, we were unable to explain the process(es) of care leading to the mortality benefit

demonstrated in our study, despite examining the patterns of healthcare utilization between

exposed and unexposed patients. Future work is needed to elucidate the mechanism by which

nephrologist’s impact on mortality in survivors of severe AKI.

Finally, the estimated association between early nephrology follow-up and outcomes are

valid for the matched cohort but not necessarily for the unmatched cohort. Unmatched patients

who underwent follow-up had higher rates of chronic kidney disease, previous nephrology visits,

and had a higher risk of mortality and chronic dialysis than the matched cohort. These

characteristics made them difficult to match. As such, the advantage of early nephrology follow-

up may not be generalizable to the entire spectrum of patients with dialysis-requiring AKI.

5.5 Implications and Future Directions

We have demonstrated that in patients affected by the most severe form of AKI, that which

requires dialysis, there is an increased risk of mortality within 2 years that may be mitigated with

appropriate nephrology follow-up. Currently, approximately 25% of Medicare patients

hospitalized for an episode of AKI are seen by a nephrologist within 30 days of discharge. This

increases to almost 40% within 180 days after hospitalization. In the non-Medicare population,

only 25% of patients are referred to a nephrologist post hospitalization complicated by AKI. 23

This trend may be leading to missed opportunities for intervention especially among patients at

high risk of progressive renal decline and death.

58

Despite the noted benefits of nephrology follow-up after an episode of AKI, one must

keep in mind the public health ramifications of such an intervention. Given the limited pool of

nephrologists in Canada, there is a strong potential to overwhelm this already scarce healthcare

resource by a significant increase in the number of post-AKI nephrology referrals. In fact, such a

trend was seen after automated laboratory reporting of estimated glomerular filtration rate

(eGFR) was introduced in Ontario and Alberta.82;127

As such, the development of a risk prediction rule for mortality and CKD progression in

AKI survivors is of paramount importance. Such a rule would accurately predict the risk of

mortality and CKD progression and thereby facilitate individualized decision making, enabling

timely and appropriate care. This rule would also help direct scare resources, costly testing and

interventions. Although predictive rules of CKD progression are in widespread use, only one has

been developed for a cohort of AKI survivors. 45;128-130

That rule, by Chawla and colleagues, was

developed using a fairly limited set of covariates in a fairly homogeneous population of United

States veterans.45

Therefore, it might not perform accurately in other settings. Furthermore,

there are currently no predictive rules in use that estimate the risk of mortality in AKI survivors.

Given these points, physicians must often make ad hoc decisions about which survivors of AKI

to refer to nephrologists for follow-up based on their personal determination of risk. This is

undoubtedly a dangerous task especially with a heterogeneous and sick patient population.

Accordingly, further studies are necessary to elucidate the factors that predict progression

to CKD, chronic dialysis and mortality among survivors of AKI and develop an integrative

prediction rule that will appropriately stratify these patients according to their level of risk. This

will not only protect scarce health resources, but should also improve patient outcomes. Once

the prediction rule is developed and validated, subsequent work can begin on identifying factors

that may ameliorate the long-term complications of AKI.

Conclusion

Survivors of acute kidney injury requiring dialysis who receive post-discharge follow-up with a

nephrologist appear to have decreased mortality but increased progression to chronic dialysis

59

within 2 years. Our findings expand on those found in other disciplines, that is, improved

continuity of care among specialists translates into improved survival after hospital discharge.

60

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Appendix

APPENDIX 1: DIAGNOSTIC CODES FOR DISEASES/CONDITIONS PERTINENT TO THE STUDY

Condition/Disease ICD-9 ICD-10 Diagnostic type

Procedure Codes

OHIP, CCI, CCP

AK

I R

RT

su

rviv

ors

co

ho

rt

Age ≥ 18 years

No codes for AKI or any form of dialysis in preceding five yrs

Hospital admission with AKI

Dialysis during hospitalization

alive at 90 days post-discharge

no dialysis during the 90-day period after discharge

no rehospitalization for 90 days following discharge

584.5-584.9

669.3, 958.5 634.3, 635.3,

636.3, 637.3

638.3, 639.3

N17.0-N17.9, O08.4, T79.5, O90.4

all R849, R850, G323, G325, G326, G330, G331, G332, G860, G333, G083, G091, G085, G295, G082, G090, G092, G093, G094, G861, G862, G863, G864, G865, G866, G294, G095, G096

CCP: 51.95, 66.98

CCI:

1PZ21HQBR, 1PZ21HPD4

Kid

ney

Tra

nsp

lan

t

exclusion from cohort if received a kidney transplant during the 5 years prior to the index admission

CCP: 6759

CCI: 1PC85LAXXJ

1PC85LAXXK

71

A

ny d

ialy

sis

exclusion from cohort if received any form of dialysis during the 5 years prior to the index admission

R849, R850, G323, G325, G326, G330, G331, G332, G860, G333, G083, G091, G085, G295, G082, G090, G092, G093, G094, G861, G862, G863, G864, G865, G866, G294, G095, G096

CCP: 51.95, 66.98

CCI:

1PZ21HQBR, 1PZ21HPD4

Mech

an

ical

Ven

tila

tio

n

describing a patient’s receipt of mechanical ventilation on an admission of interest (used for matching of cohorts to non-exposed individuals)

G557, G558, G559, G405, G406, G407

Ch

ron

ic K

idn

ey D

isea

se

the presence of this condition for any inpatient or outpatient encounter in 5 years preceding date of admission for index hospitalization

016.0x, 095.4, 250.4x,

274.1x, 403.xx,

404.xx, 405.01,

405.11, 405.91, 440.1,

446.21, 581.xx,

582.xx, 583.xx, 585.x,

586, 587.x, 588.0,

588.8x, 588.9, 589.x,

590.0x, 593.7x, 791.0,

794.4

A18.1, N29.0, E10.20,

E10.21, E11.20, E11.21,

M10.39, I12.0, I12.9,

I13.0, I13.1, I13.2, I13.9,

I15.0, I70.1, M31.0,

N03.x, N04.x, N05.x,

N06.x, N07.x, N08.x,

N11.x, N12, N13.7,

N13.8, N13.9, N14.x,

N15.x, N16.x, N18.0,

N18.8, N18.9x, N18.90,

N18.91, N19, N25.0,

N25.8, N25.9, N26,

N27.x, R80, R94.4

all 403, 581, 582, 585, 586

72

C

ard

iac s

urg

ery

includes all cardiac surgery except percutaneous insertions, pacemakers, ICDs

for subcategorization of patients with AKI-RRT on index admission

E646-647 E650-652, E656, E658, E660-661, E670-671 E682, M134,M137, R700, R709-714, R715-718, R720-730, R722-738, R741-743, R746-749, R755, R758-759, R762, R768-774, R870, R874, R920-930, R863, R876

Ao

rtic

an

eu

rysm

(no

n-

rup

ture

d)

surgical procedures for unruptured aortic (thoracic and abdominal) aneurysms

for subcategorization of patients with AKI-RRT on index admission

R799-803, R816-817, R875 (EVAR)

Sep

sis

for subcategorization of patients with AKI-RRT on index admission (based on criteria of Martin et. al. NEJM)

003·1, 036·2 and 038·00–038·90

A082.1, A39.4, A40.3, A40.9, A41.2, A41.3, A41.4, A41.51, A41.52, A41.58, A41.8, A41.9

all

Pro

pen

sit

y

sco

re

to be used for matching

variable list and associated codes attached below

Nep

hro

log

ist

Any physician with 10 claims using any

of the OHIP codes on the right anytime

between start of ICES data until March

31, 2007 (a person may have any

combination of these codes but as long

as 10 are claimed, they are considered

a nephrologist) – yellow highlighted

codes

G326, G860, G333, G862, G863, G865, G866, G332, G861, G864

Condition/Disease ICD-9 ICD-10 Diagnostic type

OHIP

Death

(in RPDB)

------- ------- ----- ----- -----

Acute Kidney Injury

-------- 584.5-584.9

669.3, 958.5 634.3, 635.3,

636.3, 637.3638.3, 639.3

N17.0-N17.9, O08.4, T79.5, O90.4

all -----

End-stage renal disease-Definition B

Receipt of dialysis for a 90-day period after 90 days post-discharge

Comment: If any of the listed codes is detected, count forward 90 days. One of the listed codes has to appear after the 90-day mark but within 60 days after the 90-day mark. The date of the initial code is considered the “date of ESRD”.

R849, R850, G323, G325, G326, G330, G331, G332, G860, G333, G083, G091, G085, G295, G082, G090, G092, G093, G094, G861, G862, G863, G864, G865, G866, G294, G095, G096

CCP: 51.95, 66.98

CCI:

1PZ21HQBR, 1PZ21HPD4

eAppendix2: Covariates used in the development of the propensity score.

Characteristic

Demographics

Age, mean (SD), y

Female gender

Lowest income quintile

Rural residence

Distance from nearest dialysis unit (km)a

0-50

51-150

151-300

>300

Admission Era

1995-2001

2002-2008

Proceduresc

Angiography

Coronary artery bypass grafting

Percutaneous coronary intervention

Valvular surgery

Pacemaker

Echocardiography

Electrocardiography

Holter monitor

Stress test

Peripheral vascular procedures

Carotid doppler ultrasound

Carotid endarderectomy

Renal biopsy

Hospital type

Teaching hospital

Healthcare accessb

Number of hospitalizations

Number of emergency visits

Number of GP visits

Number of internist visits

Number of cardiology visits

Number of nephrology visits

Procedure or condition during index hospitalization

Sepsis

Cardiac surgery

Abdominal aortic aneurysm repair

Mechanical ventilation d

Comorbid disease c

Myocardial Infarction

Coronary artery disease

Cardiac dysrythmias

Heart failure

Cerebrovascular disease

Diabetes

Cancer

Liver disease

Peripheral vascular disease

Chronic kidney diseasec

Hypertension

Hematuria

Proteinuria

Major respiratory infection

Major genitourinary/gastrointestinal infection

Major skin/soft tissue infection

Charlson comorbidity index, mean (SD), score

aGeospatial mapping methods were used to calculate the distance between each patient’s residence and the practice

location of the closest dialysis unit.1

b In the 5 years preceding the hospital discharge date except chronic kidney disease which had a lookback period of

5 years preceding the hospital admission date. c In the 5 years preceding hospital discharge date.

d The receipt of mechanical ventilation was used as a surrogate for admission to a critical care unit.2