Long-term Outcomes Associated With Implantable Cardioverter Defibrillator in Adults With Chronic Kidney Disease | Cardiology | JAMA Internal Medicine | JAMA Network
[Skip to Navigation]
Figure 1.  Cohort Assembly
Cohort Assembly

Exclusions are not mutually exclusive. CKD indicates chronic kidney disease; HDPS, high-density propensity score; and ICD, implantable cardioverter defibrillator.

aBased on as many as 3 matches per ICD recipient.

bIndex date is the date when ICD placement occurred.

cIndex date is the date when ICD placement occurred for their matched counterparts.

Figure 2.  Probability of Survival Among Adults With Reduced Left Ventricular Ejection Fraction Heart Failure and Chronic Kidney Disease
Probability of Survival Among Adults With Reduced Left Ventricular Ejection Fraction Heart Failure and Chronic Kidney Disease

Data are stratified by implantable cardioverter defibrillator (ICD) placement status. Index date indicates date when ICD placement occurred (ICD group) or date when ICD placement occurred for the matched counterparts (non-ICD group).

Table 1.  Baseline Characteristics of Adults With Heart Failure and Reduced Ejection Fraction Who Did or Did Not Receive an ICDa
Baseline Characteristics of Adults With Heart Failure and Reduced Ejection Fraction Who Did or Did Not Receive an ICDa
Table 2.  Association of ICD Placement With All-Cause Mortality Among Adults With CKD and Reduced LVEF Heart Failure
Association of ICD Placement With All-Cause Mortality Among Adults With CKD and Reduced LVEF Heart Failure
Table 3.  Association of ICD Placement With All-Cause and Heart Failure–Related Hospitalization Among Adults With CKD and Reduced LVEF Heart Failure
Association of ICD Placement With All-Cause and Heart Failure–Related Hospitalization Among Adults With CKD and Reduced LVEF Heart Failure
1.
Levey  AS, Stevens  LA, Schmid  CH,  et al; CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration).  A new equation to estimate glomerular filtration rate.  Ann Intern Med. 2009;150(9):604-612.PubMedGoogle ScholarCrossref
2.
United States Renal Data System Coordinating Center. United States Renal Data System 2017 Annual Data Report. https://www.usrds.org/adr.aspx. Accessed December 1, 2017.
3.
Go  AS, Chertow  GM, Fan  D, McCulloch  CE, Hsu  CY.  Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization.  N Engl J Med. 2004;351(13):1296-1305.PubMedGoogle ScholarCrossref
4.
Kottgen  A, Russell  SD, Loehr  LR,  et al.  Reduced kidney function as a risk factor for incident heart failure: the Atherosclerosis Risk in Communities (ARIC) study.  J Am Soc Nephrol. 2007;18(4):1307-1315.PubMedGoogle ScholarCrossref
5.
Gottdiener  JS, Arnold  AM, Aurigemma  GP,  et al.  Predictors of congestive heart failure in the elderly: the Cardiovascular Health Study.  J Am Coll Cardiol. 2000;35(6):1628-1637.PubMedGoogle ScholarCrossref
6.
Bansal  N, Katz  R, Robinson-Cohen  C,  et al.  Absolute rates of heart failure, coronary heart disease, and stroke in chronic kidney disease: an analysis of 3 community-based cohort studies.  JAMA Cardiol. 2017;2(3):314-318.PubMedGoogle Scholar
7.
Ezekowitz  J, McAlister  FA, Humphries  KH,  et al; APPROACH Investigators.  The association among renal insufficiency, pharmacotherapy, and outcomes in 6,427 patients with heart failure and coronary artery disease.  J Am Coll Cardiol. 2004;44(8):1587-1592.PubMedGoogle ScholarCrossref
8.
McAlister  FA, Ezekowitz  J, Tonelli  M, Armstrong  PW.  Renal insufficiency and heart failure: prognostic and therapeutic implications from a prospective cohort study.  Circulation. 2004;109(8):1004-1009.PubMedGoogle ScholarCrossref
9.
Goldenberg  I, Moss  AJ, McNitt  S,  et al; Multicenter Automatic Defibrillator Implantation Trial-II Investigators.  Relations among renal function, risk of sudden cardiac death, and benefit of the implanted cardiac defibrillator in patients with ischemic left ventricular dysfunction.  Am J Cardiol. 2006;98(4):485-490.PubMedGoogle ScholarCrossref
10.
Moss  AJ, Hall  WJ, Cannom  DS,  et al; Multicenter Automatic Defibrillator Implantation Trial Investigators.  Improved survival with an implanted defibrillator in patients with coronary disease at high risk for ventricular arrhythmia.  N Engl J Med. 1996;335(26):1933-1940.PubMedGoogle ScholarCrossref
11.
Moss  AJ, Zareba  W, Hall  WJ,  et al; Multicenter Automatic Defibrillator Implantation Trial II Investigators.  Prophylactic implantation of a defibrillator in patients with myocardial infarction and reduced ejection fraction.  N Engl J Med. 2002;346(12):877-883.PubMedGoogle ScholarCrossref
12.
Buxton  AE, Lee  KL, Fisher  JD, Josephson  ME, Prystowsky  EN, Hafley  G; Multicenter Unsustained Tachycardia Trial Investigators.  A randomized study of the prevention of sudden death in patients with coronary artery disease.  N Engl J Med. 1999;341(25):1882-1890.PubMedGoogle ScholarCrossref
13.
Hager  CS, Jain  S, Blackwell  J, Culp  B, Song  J, Chiles  CD.  Effect of renal function on survival after implantable cardioverter defibrillator placement.  Am J Cardiol. 2010;106(9):1297-1300.PubMedGoogle ScholarCrossref
14.
Williams  ES, Shah  SH, Piccini  JP,  et al.  Predictors of mortality in patients with chronic kidney disease and an implantable defibrillator: an EPGEN substudy.  Europace. 2011;13(12):1717-1722.PubMedGoogle ScholarCrossref
15.
Singh  SM, Wang  X, Austin  PC, Parekh  RS, Lee  DS; Ontario ICD Database Investigators.  Prophylactic defibrillators in patients with severe chronic kidney disease.  JAMA Intern Med. 2014;174(6):995-996.PubMedGoogle ScholarCrossref
16.
Pun  PH, Al-Khatib  SM, Han  JY,  et al.  Implantable cardioverter-defibrillators for primary prevention of sudden cardiac death in CKD: a meta-analysis of patient-level data from 3 randomized trials.  Am J Kidney Dis. 2014;64(1):32-39.PubMedGoogle ScholarCrossref
17.
Masoudi  FA, Havranek  EP, Wolfe  P,  et al.  Most hospitalized older persons do not meet the enrollment criteria for clinical trials in heart failure.  Am Heart J. 2003;146(2):250-257.PubMedGoogle ScholarCrossref
18.
Bilchick  KC, Stukenborg  GJ, Kamath  S, Cheng  A.  Prediction of mortality in clinical practice for Medicare patients undergoing defibrillator implantation for primary prevention of sudden cardiac death.  J Am Coll Cardiol. 2012;60(17):1647-1655.PubMedGoogle ScholarCrossref
19.
Go  AS, Magid  DJ, Wells  B,  et al.  The Cardiovascular Research Network: a new paradigm for cardiovascular quality and outcomes research.  Circ Cardiovasc Qual Outcomes. 2008;1(2):138-147.PubMedGoogle ScholarCrossref
20.
Magid  DJ, Gurwitz  JH, Rumsfeld  JS, Go  AS.  Creating a research data network for cardiovascular disease: the CVRN.  Expert Rev Cardiovasc Ther. 2008;6(8):1043-1045.PubMedGoogle ScholarCrossref
21.
Smith  DH, Thorp  ML, Gurwitz  JH,  et al.  Chronic kidney disease and outcomes in heart failure with preserved versus reduced ejection fraction: the Cardiovascular Research Network PRESERVE Study.  Circ Cardiovasc Qual Outcomes. 2013;6(3):333-342.PubMedGoogle ScholarCrossref
22.
Go  AS, Yang  J, Ackerson  LM,  et al.  Hemoglobin level, chronic kidney disease, and the risks of death and hospitalization in adults with chronic heart failure: the Anemia in Chronic Heart Failure: Outcomes and Resource Utilization (ANCHOR) Study.  Circulation. 2006;113(23):2713-2723.PubMedGoogle ScholarCrossref
23.
McKee  PA, Castelli  WP, McNamara  PM, Kannel  WB.  The natural history of congestive heart failure: the Framingham study.  N Engl J Med. 1971;285(26):1441-1446.PubMedGoogle ScholarCrossref
24.
Kusumoto  FM, Calkins  H, Boehmer  J,  et al; Heart Rhythm Society; American College of Cardiology; American Heart Association.  HRS/ACC/AHA expert consensus statement on the use of implantable cardioverter-defibrillator therapy in patients who are not included or not well represented in clinical trials.  J Am Coll Cardiol. 2014;64(11):1143-1177.PubMedGoogle ScholarCrossref
25.
Levey  AS, de Jong  PE, Coresh  J,  et al.  The definition, classification, and prognosis of chronic kidney disease: a KDIGO Controversies Conference report.  Kidney Int. 2011;80(1):17-28.PubMedGoogle ScholarCrossref
26.
Arellano  MG, Petersen  GR, Petitti  DB, Smith  RE.  The California Automated Mortality Linkage System (CAMLIS).  Am J Public Health. 1984;74(12):1324-1330.PubMedGoogle ScholarCrossref
27.
Wentworth  DN, Neaton  JD, Rasmussen  WL.  An evaluation of the Social Security Administration master beneficiary record file and the National Death Index in the ascertainment of vital status.  Am J Public Health. 1983;73(11):1270-1274.PubMedGoogle ScholarCrossref
28.
Schneeweiss  S, Rassen  J.  Re: Confounding adjustment via a semi-automated high-dimensional propensity score algorithm: an application to electronic medical records.  Pharmacoepidemiol Drug Saf. 2011;20(10):1110-1111.PubMedGoogle ScholarCrossref
29.
Garbe  E, Kloss  S, Suling  M, Pigeot  I, Schneeweiss  S.  High-dimensional versus conventional propensity scores in a comparative effectiveness study of coxibs and reduced upper gastrointestinal complications.  Eur J Clin Pharmacol. 2013;69(3):549-557.PubMedGoogle ScholarCrossref
30.
Schneeweiss  S, Rassen  JA, Glynn  RJ, Avorn  J, Mogun  H, Brookhart  MA.  High-dimensional propensity score adjustment in studies of treatment effects using health care claims data.  Epidemiology. 2009;20(4):512-522.PubMedGoogle ScholarCrossref
31.
Lévesque  LE, Hanley  JA, Kezouh  A, Suissa  S.  Problem of immortal time bias in cohort studies: example using statins for preventing progression of diabetes.  BMJ. 2010;340:b5087.PubMedGoogle ScholarCrossref
32.
Suissa  S.  Immortal time bias in observational studies of drug effects.  Pharmacoepidemiol Drug Saf. 2007;16(3):241-249.PubMedGoogle ScholarCrossref
33.
Suissa  S.  Immortal time bias in pharmaco-epidemiology.  Am J Epidemiol. 2008;167(4):492-499.PubMedGoogle ScholarCrossref
34.
Chan  PS, Nallamothu  BK, Spertus  JA,  et al.  Impact of age and medical comorbidity on the effectiveness of implantable cardioverter-defibrillators for primary prevention.  Circ Cardiovasc Qual Outcomes. 2009;2(1):16-24.PubMedGoogle ScholarCrossref
35.
Harel  Z, Wald  R, McArthur  E,  et al.  Rehospitalizations and emergency department visits after hospital discharge in patients receiving maintenance hemodialysis.  J Am Soc Nephrol. 2015;26(12):3141-3150.PubMedGoogle ScholarCrossref
36.
Ronksley  PE, Hemmelgarn  BR, Manns  BJ,  et al.  Potentially preventable hospitalization among patients with CKD and high inpatient use.  Clin J Am Soc Nephrol. 2016;11(11):2022-2031.PubMedGoogle ScholarCrossref
37.
Korte  T, Jung  W, Ostermann  G,  et al.  Hospital readmission after transvenous cardioverter/defibrillator implantation: a single centre study.  Eur Heart J. 2000;21(14):1186-1191.PubMedGoogle ScholarCrossref
38.
Goldenberg  I, Moss  AJ, Hall  WJ,  et al; Multicenter Automatic Defibrillator Implantation Trial (MADIT) II Investigators.  Causes and consequences of heart failure after prophylactic implantation of a defibrillator in the Multicenter Automatic Defibrillator Implantation Trial II.  Circulation. 2006;113(24):2810-2817.PubMedGoogle ScholarCrossref
39.
Charytan  DM, Patrick  AR, Liu  J,  et al.  Trends in the use and outcomes of implantable cardioverter-defibrillators in patients undergoing dialysis in the United States.  Am J Kidney Dis. 2011;58(3):409-417.PubMedGoogle ScholarCrossref
40.
Sakhuja  R, Keebler  M, Lai  TS, McLaughlin Gavin  C, Thakur  R, Bhatt  DL.  Meta-analysis of mortality in dialysis patients with an implantable cardioverter defibrillator.  Am J Cardiol. 2009;103(5):735-741.PubMedGoogle ScholarCrossref
41.
Aggarwal  A, Wang  Y, Rumsfeld  JS, Curtis  JP, Heidenreich  PA; National Cardiovascular Data Registry.  Clinical characteristics and in-hospital outcome of patients with end-stage renal disease on dialysis referred for implantable cardioverter-defibrillator implantation.  Heart Rhythm. 2009;6(11):1565-1571.PubMedGoogle ScholarCrossref
42.
Tompkins  C, McLean  R, Cheng  A,  et al.  End-stage renal disease predicts complications in pacemaker and ICD implants.  J Cardiovasc Electrophysiol. 2011;22(10):1099-1104.PubMedGoogle ScholarCrossref
43.
Clerkin  KJ, Topkara  VK, Demmer  RT,  et al.  Implantable cardioverter-defibrillators in patients with a continuous-flow left ventricular assist device: an analysis of the INTERMACS Registry.  JACC Heart Fail. 2017;5(12):916-926.PubMedGoogle ScholarCrossref
44.
Sidney  S, Sorel  M, Quesenberry  CP  Jr, DeLuise  C, Lanes  S, Eisner  MD.  COPD and incident cardiovascular disease hospitalizations and mortality: Kaiser Permanente Medical Care Program.  Chest. 2005;128(4):2068-2075.PubMedGoogle ScholarCrossref
45.
Go  AS, Lee  WY, Yang  J, Lo  JC, Gurwitz  JH.  Statin therapy and risks for death and hospitalization in chronic heart failure.  JAMA. 2006;296(17):2105-2111.PubMedGoogle ScholarCrossref
46.
Selby  JV, Fireman  BH, Lundstrom  RJ,  et al.  Variation among hospitals in coronary-angiography practices and outcomes after myocardial infarction in a large health maintenance organization.  N Engl J Med. 1996;335(25):1888-1896.PubMedGoogle ScholarCrossref
47.
Setoguchi  S, Warner Stevenson  L, Stewart  GC,  et al.  Influence of healthy candidate bias in assessing clinical effectiveness for implantable cardioverter-defibrillators: cohort study of older patients with heart failure.  BMJ. 2014;348:g2866.PubMedGoogle ScholarCrossref
Original Investigation
March 2018

Long-term Outcomes Associated With Implantable Cardioverter Defibrillator in Adults With Chronic Kidney Disease

Author Affiliations
  • 1Kidney Research Institute, Division of Nephrology, University of, Seattle
  • 2Department of Biostatistics, University of Washington, Seattle
  • 3Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena
  • 4Kaiser Permanente Northwest Center for Health Research, Portland, Oregon
  • 5Department of Medicine, Kaiser Permanente Colorado, Denver
  • 6Meyers Primary Care Institute, Worcester, Massachusetts
  • 7University of Colorado Hospital, Aurora
  • 8Marshfield Clinic Research Foundation, Marshfield, Wisconsin
  • 9Kaiser Permanente Northern California Division of Research, Oakland
  • 10Department of Epidemiology, University of California, San Francisco
  • 11Department of Biostatistics, University of California, San Francisco
  • 12Department of Medicine, University of California, San Francisco
JAMA Intern Med. 2018;178(3):390-398. doi:10.1001/jamainternmed.2017.8462
Key Points

Question  Does placement of implantable cardioverter defibrillators improve clinical outcomes in patients with chronic kidney disease?

Findings  In this cohort study of 5877 community-based patients with heart failure and chronic kidney disease, use of implantable cardioverter defibrillators was not significantly associated with improved survival but was associated with increased risk for subsequent heart failure and all-cause hospitalization.

Meaning  The potential risks and benefits of implantable cardioverter defibrillators should be carefully considered in patients with heart failure and chronic kidney disease.

Abstract

Importance  Chronic kidney disease (CKD) is common in adults with heart failure and is associated with an increased risk of sudden cardiac death. Randomized trials of participants without CKD have demonstrated that implantable cardioverter defibrillators (ICDs) decrease the risk of arrhythmic death in selected patients with reduced left ventricular ejection fraction (LVEF) heart failure. However, whether ICDs improve clinical outcomes in patients with CKD is not well elucidated.

Objective  To examine the association of primary prevention ICDs with risk of death and hospitalization in a community-based population of potentially ICD-eligible patients who had heart failure with reduced LVEF and CKD.

Design, Settings, and Participants  This noninterventional cohort study included adults with heart failure and an LVEF of 40% or less and measures of serum creatinine levels available from January 1, 2005, through December 31, 2012, who were enrolled in 4 Kaiser Permanente health care delivery systems. Chronic kidney disease was defined as an estimated glomerular filtration rate of less than 60 mL/min/1.73 m2. Patients who received and did not receive an ICD were matched (1:3) on CKD status, age, and high-dimensional propensity score to receive an ICD. Follow-up was completed on December 31, 2013. Data were analyzed from 2015 to 2017.

Exposures  Placement of an ICD.

Main Outcomes and Measures  All-cause death, hospitalizations due to heart failure, and any-cause hospitalizations.

Results  A total of 5877 matched eligible adults with CKD (1556 with an ICD and 4321 without an ICD) were identified (4049 men [68.9%] and 1828 women [31.1%]; mean [SD] age, 72.9 [8.2] years). In models adjusted for demographics, comorbidity, and cardiovascular medication use, no difference was found in all-cause mortality between patients with CKD in the ICD vs non-ICD groups (adjusted hazard ratio, 0.96; 95% CI, 0.87-1.06). However, ICD placement was associated with increased risk of subsequent hospitalization due to heart failure (adjusted relative risk, 1.49; 95% CI, 1.33-1.60) and any-cause hospitalization (adjusted relative risk, 1.25; 95% CI, 1.20-1.30) among patients with CKD.

Conclusions and Relevance  In a large, contemporary, noninterventional study of community-based patients with heart failure and CKD, ICD placement was not significantly associated with improved survival but was associated with increased risk for subsequent hospitalization due to heart failure and all-cause hospitalization. The potential risks and benefits of ICDs should be carefully considered in patients with heart failure and CKD.

Introduction

Chronic kidney disease (CKD)1 is a major public health condition that is estimated to affect 14% of US adults.2 Cardiovascular disease is the leading cause of morbidity and death in patients with CKD,3 with heart failure being one of the most common cardiovascular manifestations.4-6 Concurrently, more than 5.7 million adults in the United States are estimated to have heart failure, of whom 30% have CKD.7,8 A complication of heart failure is sudden cardiac death, with CKD being one of the strongest risk factors for this outcome.9

Placement of an implantable cardioverter defibrillator (ICD) as a primary prevention strategy for sudden cardiac death reduced the risk of death due to arrhythmia in adults with heart failure and reduced left ventricular ejection fraction (LVEF) compared with optimal medical therapy alone in selected participants enrolled in randomized trials.10-12 However, patients with CKD are notably underrepresented in existing trials. Because ICD placement carries risks and is expensive, a better understanding of how best to use this therapy in high-risk subgroups such as patients with CKD is critical.

Existing studies of primary prevention ICDs in patients with CKD have been limited by modest sample size,9,13-15 highly selected populations of trial participants9,16 (who are typically healthier and less representative compared with target patients with heart failure17), and lack of a comparison group of similar patients with CKD who did not undergo ICD placement.13,18 Therefore, in this noninterventional study, we examined the association of primary prevention ICD placement with the risk of death and hospitalization in a community-based population of potentially ICD-eligible patients with reduced LVEF heart failure and CKD.

Methods
Source Population

The study population included members from 4 participating health care delivery systems within the Cardiovascular Research Network from January 1, 2005, through December 31, 2013.19 Sites included Kaiser Permanente Northern California, Kaiser Permanente Southern California, Kaiser Permanente Northwest, and Kaiser Permanente Colorado. Each site has a virtual data warehouse that served as the primary data source for participant identification and characterization.20 The virtual data warehouse is a distributed, standardized data resource that consists of electronic data sets at each site that are populated with linked demographic, administrative, outpatient pharmacy, laboratory testing, and health care resource (ambulatory visits and network and nonnetwork hospitalizations with diagnoses and procedures) data. Institutional review boards at Kaiser Permanente Northern California, Kaiser Permanente Southern California, Kaiser Permanente Northwest, and Kaiser Permanente Colorado approved the study and waived the need for informed consent owing to the retrospective nature of the study.

Study Sample

We identified adults 21 years or older who had at least 12 months of continuous health plan enrollment and pharmacy benefit before the index date to ensure adequate data on covariates. Patients had a diagnosis of heart failure based on having been hospitalized with a primary discharge diagnosis of heart failure and/or having at least 3 ambulatory visits coded for heart failure, with at least 1 visit being with a cardiologist, from January 1, 2005, through December 31, 2012.21 We used the following codes from the International Classification of Diseases, Ninth Revision (ICD-9): 398.91, 402.01, 402.11, 402.91, 404.01, 404.03, 404.11, 404.13, 404.91, 404.93, 428.0, 428.1, 428.20, 428.21, 428.22, 428.23, 428.30, 428.31, 428.32, 428.33, 428.40, 428.41, 428.42, 428.43, and 428.9. Previous studies have shown a positive predictive value of greater than 95% for admissions with a primary discharge diagnosis of heart failure based on these codes compared against manual medical record review using Framingham clinical criteria.22,23

Because we were interested in patients who were eligible for ICD placement, we further ascertained information on quantitative and/or qualitative assessments of left ventricular systolic function from the results of echocardiograms, radionuclide scintigraphy, other nuclear imaging modalities, and left ventriculography test results available from site-specific databases complemented by manual medical record review.21 Only patients with an LVEF of 40% or less or moderate to severely reduced systolic function on qualitative assessment with no history of ICD placement were included (Figure 1).12,24 We only included patients with CKD, which was defined25 as an estimated glomerular filtration rate (eGFR) of less than 60 mL/min/1.73 m2 by the Chronic Kidney Disease Epidemiology Collaboration equation1 using the closest outpatient serum creatinine level to 365 days before or on the index date. We excluded patients who had missing measures of serum creatinine levels at study entry, had preexisting end-stage renal disease (defined as receipt of long-term dialysis or a kidney transplant), did not have follow-up data, or had a previous solid organ transplant (Figure 1).

Estimation of ICD Implantation

The main exposure of interest was ICD placement during follow-up. Placement of an ICD was identified based on any 1 of the following procedure codes: 00.51, 00.52, 00.54, 37.94, 37.95, 37.96, 37.97, 37.98, 37.99, V45.02, 37.75, 37.79, 37.99, or 89.49 from ICD-9; 00534, 33215, 33216, 33217, 33218, 33220, 33224, 33225, 00534, 33240, 33241, 33243, 33244, 33249, 33223, 33230, 33231, 33248, 33262, 33263, 33264, 93283, 93284, 93287, 93737, 93738, or 93745 from Current Procedural Terminology; and C1721, C1722, C1777, C1882, C1895, C1896, or C1899 from the Health Care Financing Administration Common Procedural Coding System. Patients were also required to have an outpatient, non–emergency department serum creatinine value in the 1 year before ICD placement, no history of end-stage renal disease, no history of organ transplantation, and available follow-up after the ICD placement date. With these criteria, 3312 patients who received ICDs were eligible for matching (Figure 1).

Outcomes

Patients were censored during follow-up at the time of any solid organ transplant or the end of the follow-up period on December 31, 2013. We studied the following 3 primary outcomes: all-cause mortality, heart failure–related hospitalizations, and any-cause hospitalizations. All-cause mortality was identified through health system administrative databases, member proxy reporting, hospitalizations, regional cancer registries, Social Security Administration vital status files, and state death certificate files.26,27 Heart failure–related hospitalizations were identified using a principal discharge diagnosis of heart failure based on validated ICD-9 diagnosis codes 398.91, 402.x1, 404.01, 404.03, 404.11, 404.13, 404.91, 404.93, and 428.x.21 Hospitalizations for any cause were identified through Kaiser Permanente’s comprehensive hospitalization and billing claims databases for network and nonnetwork admissions, respectively.

Covariates

We ascertained information on demographic characteristics and comorbidity based on previously validated diagnoses and procedures using ICD-9 codes, laboratory results, prescribed medications, and registries.3 Potential confounders included demographic characteristics (age, sex, and race/ethnicity), comorbid conditions based on relevant diagnosis and procedure codes (prior acute coronary syndrome, coronary revascularization, ischemic stroke or transient ischemic attack, atrial fibrillation, ventricular fibrillation or tachycardia, mitral or aortic valvular heart disease, obstructive sleep apnea, peripheral arterial disease, tobacco use, dyslipidemia, hypertension, diabetes, dementia, depression, lung disease, liver disease, and cancer), ambulatory measures of blood pressure, body mass index, low- and high-density lipoprotein cholesterol levels, hemoglobin level, LVEF, and selected medication use (angiotensin-converting enzyme inhibitors, angiotensin II receptor blockers, β-blockers, and diuretics) within 120 days before the index date from pharmacy dispensing databases.

Statistical Analysis

Data were analyzed from 2015 to 2017. All analyses were conducted using SAS statistical software (version 9.3; SAS Institute). We conducted a matched parallel cohort study to examine the association between ICD placement and subsequent outcomes of death, hospitalization for heart failure, and any-cause hospitalization. The cohort entry date was the date of ICD placement in the ICD group. The same calendar date was also the cohort entry date for the matched non-ICD group. We used a 2-step process for matching. We first estimated a high-dimensional propensity score28 for receipt of ICD for each patient.28 We developed the high-dimensional propensity score using techniques by Schneeweiss et al.28-30 In brief, the high-dimensional propensity score algorithm (1) required the identification of approximately 500 variables from different data dimensions (eg, demographic, hospitalization, procedure, outpatient care, laboratory, and medication data) in the database, (2) identified the most prevalent variables in each data dimension as candidate covariates, (3) ranked candidate covariates based on their occurrence (the frequency that the codes were recorded for each individual during the baseline period), (4) ranked covariates across all data dimensions by their potential for control of confounding based on the bivariate associations of each covariate with the treatment and with the outcome, (5) selected covariates from step 4 (eg, 200) for propensity score modeling, and (6) estimated the propensity score with multivariable logistic regression using the selected covariates. We then individually matched patients who did or did not receive an ICD based on age (±5 years), being alive on the ICD placement date (or the same calendar date for the non-ICD group), CKD status (eGFR <60 or ≥60 mL/min/1.73 m2) as of the matching date, and having a high-dimensional propensity score difference of 0.005 or less. In addition, matched patients who did not receive an ICD also were required to have at least 365 days of continuous health plan enrollment and pharmacy benefits before the corresponding match date, no history of end-stage renal disease or organ transplant, and an outpatient serum creatinine level measured within 365 days before the match date. We chose this approach for the non-ICD group to avoid immortal time bias.31-33 On the basis of these criteria, we successfully matched 1556 unique patients who underwent ICD with CKD and 4321 patients with CKD who did not receive an ICD (Figure 1).

Characteristics of matched patients were compared using analysis of variance or a relevant nonparametric test for continuous variables and χ2 tests for categorical variables. Given the large sample size, standard differences in each variable were compared between matched groups by computing a difference in means of the 2 groups divided by the pooled SD, with D values of greater than 0.10 considered to be statistically significant. Rates of each outcome (per 100 person-years) with associated 95% CIs were calculated for matched patients. After confirming no violation in the proportional hazards assumption, Cox proportional hazards regression models were used to examine the association between receipt of a primary prevention ICD and risk of all-cause death, and a generalized estimating equation Poisson regression with robust SEs was used to examine the association of ICD placement with heart failure–related hospitalizations and any-cause hospitalizations to allow multiple events per patient. We performed a series of sequential, nested models using the following approach. Model 1 adjusted for age, sex, and race. Model 2 adjusted for the covariates in model 1 and added baseline smoking, prevalent heart failure, acute myocardial infarction, unstable angina, coronary bypass surgery, percutaneous coronary intervention, ischemic stroke or transient ischemic attack, other arterial thromboembolic event, atrial fibrillation or flutter, ventricular tachycardia or fibrillation, mitral and/or aortic valvular disease, peripheral artery disease, rheumatic heart disease, pacemaker, dyslipidemia, hypertension, diabetes, diagnosed dementia, diagnosed depression, chronic lung disease, chronic liver disease, systemic cancer, LVEF, hemoglobin level, systolic blood pressure, high- and low-density lipoprotein cholesterol levels, eGFR, and calendar year of study entry. Finally, model 3 adjusted for covariates in model 2 and added baseline use of targeted cardiopreventive medications (angiotensin-converting enzyme inhibitors, angiotensin II receptor blockers, β-blockers, and diuretics).

Results
Characteristics of Study Population

Among the 5877 matched patients with reduced LVEF heart failure and CKD (4049 men [68.9%] and 1828 women [31.1%]; mean [SD] age, 72.9 [8.2] years; 1556 with ICD and 4321 without ICD), those with ICD placement had lower mean (SD) LVEF (26.8% [7.7%] vs 29.4% [8.2%]), were more likely to have known coronary heart disease (577 [37.1%] vs 1301 [30.1%]), and were more likely to be taking a loop diuretic (1288 [82.8%] vs 3174 [73.5%]). However, the ICD group was less likely to have diagnosed dementia (43 [2.8%] vs 166 [3.8%]) (Table 1). Overall, the patients were well-matched on all other covariates.

ICD Placement and Mortality

Overall, 2541 patients (43.2%) died during a mean (SD) follow-up of 3.1 (2.3) years. The crude rate of death was 14.9 per 100 person-years (95% CI, 13.9-16.1 per 100 person-years) in the ICD group vs 13.6 per 100 person-years (95% CI, 13.0-14.2 per 100 person-years) in the non-ICD group, with no significant difference in the unadjusted probability of survival during follow-up (Table 2 and Figure 2A). Furthermore, the association between receipt of an ICD and all-cause death was not statistically significant in adjusted models (Table 2).

ICD Placement and Subsequent Heart Failure–Related Hospitalization

A total of 1922 patients (32.7%) were hospitalized for heart failure during follow-up. Crude rates of heart failure–related hospitalization were significantly higher in the ICD group (16.90 per 100 person-years; 95% CI, 15.64-18.27 person-years) than in the non-ICD group (11.12 per 100 person-years; 95% CI, 10.53-11.74 per 100 person-years) (Table 3 and Figure 2B). In Poisson models, ICD implantation was associated with a higher risk of heart failure–related hospitalization, even after adjustment for demographics, comorbidities, and medication use (adjusted relative risk, 1.49; 95% CI, 1.39-1.60) among patients with CKD (Table 3).

ICD Placement and Subsequent Any-Cause Hospitalization

In the study population, a total of 4093 patients (69.6%) were hospitalized for any cause during follow-up. The crude rate of hospitalization was significantly higher in the ICD group (51.65 per 100 person-years; 95% CI, 48.79-54.68 per 100 person-years) compared with the non-ICD group (38.27 per 100 person-years; 95% CI, 36.91-39.69 per 100 person-years) (Table 3 and Figure 2C). In models adjusted for demographics, comorbidities, and medication use, ICD placement was associated with a 25% higher relative risk of hospitalization for any cause (adjusted relative risk, 1.25; 95% CI, 1.20-1.30) compared with receiving no ICD implant (Table 3) among patients with rEF heart failure and CKD.

Discussion

In this study of nearly 6000 contemporary matched patients with CKD and reduced LVEF heart failure who were potentially eligible for a primary prevention ICD, we found that after accounting for a broad range of potential confounders, ICD placement was not significantly associated with a lower risk of all-cause death but was associated with higher adjusted rates of heart failure–related and any-cause hospitalization. These noninterventional study findings suggest that ICDs do not provide a survival benefit in a contemporary population with CKD with reduced LVEF heart failure and may be associated with greater morbidity (eg, hospitalizations). In the absence of randomized trials of ICD therapy that include a large number of patients with moderate to advanced CKD, these data may offer insight in weighing the net potential risks and benefits to make clinical decisions regarding ICD placement in current practice among patients with CKD, who constitute a notable proportion of patients with reduced LVEF heart failure in the United States.

In our study, ICD placement was not significantly associated with a lower risk of death in patients with reduced LVEF heart failure and CKD after careful matching and further adjustment for other potential confounding factors. Few studies have examined the effect of CKD on outcomes among patients with heart failure who are eligible for an ICD. The limited studies that have included patients with kidney disease have largely relied on administrative diagnostic codes to define CKD (which is known to lead to misclassification) or have been limited to patients undergoing long-term dialysis,34 who represent a substantially smaller, higher-risk subset of the larger population with CKD. Several studies have reported that among patients who receive an ICD, those with CKD have a higher risk of death. Among Medicare beneficiaries, CKD was associated with a 2.3-fold higher rate of death after ICD implantation.18 In 958 patients with heart failure who received a primary prevention ICD, a stepwise increase in mortality for every worsening stage of CKD was found.13 In a single-center study of 199 patients with CKD who received an ICD,14 more advanced CKD was also associated with higher mortality. These prior studies have been limited only to patients who received an ICD (without a comparison group of patients with CKD who did not receive an ICD).

Only a few studies15,34 have compared outcomes in patients with heart failure and CKD who did vs did not receive an ICD. For example, in a prospective study of 900 patients eligible for ICD,34 investigators found no difference in the association of ICD with all-cause death in the 23 patients with kidney failure who received dialysis vs all other patients. In a study of 108 patients with eGFR less than 30 mL/min/1.73 m2 who received a primary prevention ICD matched to patients without an ICD,15 prophylactic ICD placement also did not confer a survival advantage. Although informative, these prior studies are limited by small sample size and did not account for a broad range of confounders, including laboratory data and receipt of cardiovascular medications. Another strength of our study is the systematic classification of CKD, which was defined by ambulatory, non–emergency department eGFR measures proximal to ICD placement rather than administrative diagnostic codes.

Hospitalizations are an important outcome to patients as a quality of life measure and pose substantial economic burdens to the health care system. Patients with CKD are known to have a disproportionate burden of hospitalizations and recurrent hospitalizations even without placement of cardiac devices.35,36 In our analyses, we found that ICD placement was independently associated with greater risks of heart failure–related hospitalization and any-cause hospitalization in patients with CKD and reduced LVEF heart failure. Our findings extend data from other studies in non-CKD populations.37,38 In a single-center study of 180 patients with an ICD,37 more than half of patients were rehospitalized within the first 1 to 2 years. In a retrospective analysis of the Multicenter Automatic Defibrillator Implantation Trial (MADIT) II trial,38 investigators found that participants who received an ICD had a 90% greater risk of heart failure–related hospitalization compared with those randomly assigned to conventional therapy, with a similar effect size as observed in our study. Our study expands on these prior studies by examining a large, contemporary, and representative population with well-characterized reduced LVEF heart failure and CKD. Although ICD placement may reduce risk of arrhythmic events in certain groups, patients with heart failure remain at high risk for recurrent hospitalizations. Furthermore, ICDs have known complications (eg, ICD device infections) that may contribute to higher rates of hospitalization overall.

The findings of this noninterventional study may have important therapeutic implications, particularly given the paucity of clinical trial data related to ICD placement in patients with CKD. These data call for a more comprehensive view of the net risks and benefits of ICD placement in eligible patients with reduced LVEF heart failure and CKD and for future trials to help directly address these questions. The findings are consistent with the literature on patients with end-stage renal disease undergoing dialysis that has suggested that patients may be too sick to benefit from primary prevention ICD placement owing to high competing risks of death39,40 and post-ICD complications.39,41,42 Similarly, a recent analysis of patients with continuous-flow left ventricular assist devices also noted that the presence of an ICD was not associated with improved survival.43

Strengths and Limitations

Our study had several strengths. We studied a large, contemporary, multicenter, well-characterized population of patients in typical clinical practice. We used advanced statistical methods to account for confounding, including high-dimensional propensity score matching and additional adjustment for a broad range of possible confounders, including detailed laboratory data and receipt of cardiovascular medications. Chronic kidney disease was defined using ambulatory measures of eGFR proximal to the index date. We also recognize certain limitations. The outcome of heart failure–related hospitalization was ascertained using relevant ICD-9 diagnosis codes. However, previous work22,23,44-46 has validated the high positive predictive value of this approach for this outcome. Specific outcome data on arrhythmic events or death and cause of hospitalization were not systematically available. Health care intensity and surveillance (which is often subjective and clinician and patient dependent) are possible confounders in this analysis; however, these are not easily obtained comprehensively through electronic health data. Our study population largely included patients with moderate stages of CKD with reduced LVEF heart failure; further work is needed to determine whether these findings are generalizable to other populations with CKD. In our study population, 1878 (32.0%) had known coronary heart disease; thus, our findings may not be generalizable to all patients with reduced LVEF heart failure, particularly those with ischemic heart failure. Despite individual matching on key confounders and additional statistical adjustment for a wide range of other potential explanatory factors, we cannot completely exclude residual or unmeasured confounding or selection bias,47 which may have affected our findings. The findings from our study should be confirmed with future randomized clinical trials. Patients in the study were enrolled in health care delivery systems and thus may not be completely generalizable to all uninsured patient populations. Finally, we were not able to determine causality in this noninterventional study.

Conclusions

Placement of ICDs was not independently associated with lower all-cause mortality but was associated with higher adjusted risks of heart failure–related and any-cause hospitalization in a large, community-based population of adults with moderate CKD and reduced LVEF heart failure. The risks and benefits should be carefully balanced in the decision to place an ICD in patients with CKD and heart failure.

Back to top
Article Information

Accepted for Publication: December 11, 2017.

Corresponding Author: Nisha Bansal, MD, MAS, Kidney Research Institute, Division of Nephrology, University of Washington, 908 Jefferson St, 3rd Floor, Seattle, WA 98104 (nbansal@nephrology.washington.edu).

Published Online: February 5, 2018. doi:10.1001/jamainternmed.2017.8462

Author Contributions: Ms Tabada and Dr Go had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: Bansal, Smith, Gurwitz, Masoudi, Greenlee, Dighe, Go.

Acquisition, analysis, or interpretation of data: Bansal, Szpiro, Reynolds, Gurwitz, Masoudi, Greenlee, Tabada, Sung, Go.

Drafting of the manuscript: Bansal, Tabada, Dighe.

Critical revision of the manuscript for important intellectual content: Bansal, Szpiro, Reynolds, Smith, Gurwitz, Masoudi, Greenlee, Tabada, Sung, Go.

Statistical analysis: Bansal, Szpiro, Tabada.

Obtained funding: Bansal, Masoudi, Go.

Administrative, technical, or material support: Masoudi, Greenlee, Sung, Dighe.

Study supervision: Reynolds, Smith, Go.

Conflict of Interest Disclosures: None reported.

Funding/Support: This study was supported grants R56HL121069 and U19 HL91179-01 from the National Heart, Lung, and Blood Institute, grant K23 DK088865 from the National Institute of Diabetes and Digestive and Kidney Diseases, and grant HHSA290-2005-0033-I-TO8-WA from the US Department of Health and Human Services.

Role of the Funder/Sponsor: The funding source had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Additional Contributions: We thank all the project managers, data programmers, and analysts for their critical technical contributions and support that made this study possible.

References
1.
Levey  AS, Stevens  LA, Schmid  CH,  et al; CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration).  A new equation to estimate glomerular filtration rate.  Ann Intern Med. 2009;150(9):604-612.PubMedGoogle ScholarCrossref
2.
United States Renal Data System Coordinating Center. United States Renal Data System 2017 Annual Data Report. https://www.usrds.org/adr.aspx. Accessed December 1, 2017.
3.
Go  AS, Chertow  GM, Fan  D, McCulloch  CE, Hsu  CY.  Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization.  N Engl J Med. 2004;351(13):1296-1305.PubMedGoogle ScholarCrossref
4.
Kottgen  A, Russell  SD, Loehr  LR,  et al.  Reduced kidney function as a risk factor for incident heart failure: the Atherosclerosis Risk in Communities (ARIC) study.  J Am Soc Nephrol. 2007;18(4):1307-1315.PubMedGoogle ScholarCrossref
5.
Gottdiener  JS, Arnold  AM, Aurigemma  GP,  et al.  Predictors of congestive heart failure in the elderly: the Cardiovascular Health Study.  J Am Coll Cardiol. 2000;35(6):1628-1637.PubMedGoogle ScholarCrossref
6.
Bansal  N, Katz  R, Robinson-Cohen  C,  et al.  Absolute rates of heart failure, coronary heart disease, and stroke in chronic kidney disease: an analysis of 3 community-based cohort studies.  JAMA Cardiol. 2017;2(3):314-318.PubMedGoogle Scholar
7.
Ezekowitz  J, McAlister  FA, Humphries  KH,  et al; APPROACH Investigators.  The association among renal insufficiency, pharmacotherapy, and outcomes in 6,427 patients with heart failure and coronary artery disease.  J Am Coll Cardiol. 2004;44(8):1587-1592.PubMedGoogle ScholarCrossref
8.
McAlister  FA, Ezekowitz  J, Tonelli  M, Armstrong  PW.  Renal insufficiency and heart failure: prognostic and therapeutic implications from a prospective cohort study.  Circulation. 2004;109(8):1004-1009.PubMedGoogle ScholarCrossref
9.
Goldenberg  I, Moss  AJ, McNitt  S,  et al; Multicenter Automatic Defibrillator Implantation Trial-II Investigators.  Relations among renal function, risk of sudden cardiac death, and benefit of the implanted cardiac defibrillator in patients with ischemic left ventricular dysfunction.  Am J Cardiol. 2006;98(4):485-490.PubMedGoogle ScholarCrossref
10.
Moss  AJ, Hall  WJ, Cannom  DS,  et al; Multicenter Automatic Defibrillator Implantation Trial Investigators.  Improved survival with an implanted defibrillator in patients with coronary disease at high risk for ventricular arrhythmia.  N Engl J Med. 1996;335(26):1933-1940.PubMedGoogle ScholarCrossref
11.
Moss  AJ, Zareba  W, Hall  WJ,  et al; Multicenter Automatic Defibrillator Implantation Trial II Investigators.  Prophylactic implantation of a defibrillator in patients with myocardial infarction and reduced ejection fraction.  N Engl J Med. 2002;346(12):877-883.PubMedGoogle ScholarCrossref
12.
Buxton  AE, Lee  KL, Fisher  JD, Josephson  ME, Prystowsky  EN, Hafley  G; Multicenter Unsustained Tachycardia Trial Investigators.  A randomized study of the prevention of sudden death in patients with coronary artery disease.  N Engl J Med. 1999;341(25):1882-1890.PubMedGoogle ScholarCrossref
13.
Hager  CS, Jain  S, Blackwell  J, Culp  B, Song  J, Chiles  CD.  Effect of renal function on survival after implantable cardioverter defibrillator placement.  Am J Cardiol. 2010;106(9):1297-1300.PubMedGoogle ScholarCrossref
14.
Williams  ES, Shah  SH, Piccini  JP,  et al.  Predictors of mortality in patients with chronic kidney disease and an implantable defibrillator: an EPGEN substudy.  Europace. 2011;13(12):1717-1722.PubMedGoogle ScholarCrossref
15.
Singh  SM, Wang  X, Austin  PC, Parekh  RS, Lee  DS; Ontario ICD Database Investigators.  Prophylactic defibrillators in patients with severe chronic kidney disease.  JAMA Intern Med. 2014;174(6):995-996.PubMedGoogle ScholarCrossref
16.
Pun  PH, Al-Khatib  SM, Han  JY,  et al.  Implantable cardioverter-defibrillators for primary prevention of sudden cardiac death in CKD: a meta-analysis of patient-level data from 3 randomized trials.  Am J Kidney Dis. 2014;64(1):32-39.PubMedGoogle ScholarCrossref
17.
Masoudi  FA, Havranek  EP, Wolfe  P,  et al.  Most hospitalized older persons do not meet the enrollment criteria for clinical trials in heart failure.  Am Heart J. 2003;146(2):250-257.PubMedGoogle ScholarCrossref
18.
Bilchick  KC, Stukenborg  GJ, Kamath  S, Cheng  A.  Prediction of mortality in clinical practice for Medicare patients undergoing defibrillator implantation for primary prevention of sudden cardiac death.  J Am Coll Cardiol. 2012;60(17):1647-1655.PubMedGoogle ScholarCrossref
19.
Go  AS, Magid  DJ, Wells  B,  et al.  The Cardiovascular Research Network: a new paradigm for cardiovascular quality and outcomes research.  Circ Cardiovasc Qual Outcomes. 2008;1(2):138-147.PubMedGoogle ScholarCrossref
20.
Magid  DJ, Gurwitz  JH, Rumsfeld  JS, Go  AS.  Creating a research data network for cardiovascular disease: the CVRN.  Expert Rev Cardiovasc Ther. 2008;6(8):1043-1045.PubMedGoogle ScholarCrossref
21.
Smith  DH, Thorp  ML, Gurwitz  JH,  et al.  Chronic kidney disease and outcomes in heart failure with preserved versus reduced ejection fraction: the Cardiovascular Research Network PRESERVE Study.  Circ Cardiovasc Qual Outcomes. 2013;6(3):333-342.PubMedGoogle ScholarCrossref
22.
Go  AS, Yang  J, Ackerson  LM,  et al.  Hemoglobin level, chronic kidney disease, and the risks of death and hospitalization in adults with chronic heart failure: the Anemia in Chronic Heart Failure: Outcomes and Resource Utilization (ANCHOR) Study.  Circulation. 2006;113(23):2713-2723.PubMedGoogle ScholarCrossref
23.
McKee  PA, Castelli  WP, McNamara  PM, Kannel  WB.  The natural history of congestive heart failure: the Framingham study.  N Engl J Med. 1971;285(26):1441-1446.PubMedGoogle ScholarCrossref
24.
Kusumoto  FM, Calkins  H, Boehmer  J,  et al; Heart Rhythm Society; American College of Cardiology; American Heart Association.  HRS/ACC/AHA expert consensus statement on the use of implantable cardioverter-defibrillator therapy in patients who are not included or not well represented in clinical trials.  J Am Coll Cardiol. 2014;64(11):1143-1177.PubMedGoogle ScholarCrossref
25.
Levey  AS, de Jong  PE, Coresh  J,  et al.  The definition, classification, and prognosis of chronic kidney disease: a KDIGO Controversies Conference report.  Kidney Int. 2011;80(1):17-28.PubMedGoogle ScholarCrossref
26.
Arellano  MG, Petersen  GR, Petitti  DB, Smith  RE.  The California Automated Mortality Linkage System (CAMLIS).  Am J Public Health. 1984;74(12):1324-1330.PubMedGoogle ScholarCrossref
27.
Wentworth  DN, Neaton  JD, Rasmussen  WL.  An evaluation of the Social Security Administration master beneficiary record file and the National Death Index in the ascertainment of vital status.  Am J Public Health. 1983;73(11):1270-1274.PubMedGoogle ScholarCrossref
28.
Schneeweiss  S, Rassen  J.  Re: Confounding adjustment via a semi-automated high-dimensional propensity score algorithm: an application to electronic medical records.  Pharmacoepidemiol Drug Saf. 2011;20(10):1110-1111.PubMedGoogle ScholarCrossref
29.
Garbe  E, Kloss  S, Suling  M, Pigeot  I, Schneeweiss  S.  High-dimensional versus conventional propensity scores in a comparative effectiveness study of coxibs and reduced upper gastrointestinal complications.  Eur J Clin Pharmacol. 2013;69(3):549-557.PubMedGoogle ScholarCrossref
30.
Schneeweiss  S, Rassen  JA, Glynn  RJ, Avorn  J, Mogun  H, Brookhart  MA.  High-dimensional propensity score adjustment in studies of treatment effects using health care claims data.  Epidemiology. 2009;20(4):512-522.PubMedGoogle ScholarCrossref
31.
Lévesque  LE, Hanley  JA, Kezouh  A, Suissa  S.  Problem of immortal time bias in cohort studies: example using statins for preventing progression of diabetes.  BMJ. 2010;340:b5087.PubMedGoogle ScholarCrossref
32.
Suissa  S.  Immortal time bias in observational studies of drug effects.  Pharmacoepidemiol Drug Saf. 2007;16(3):241-249.PubMedGoogle ScholarCrossref
33.
Suissa  S.  Immortal time bias in pharmaco-epidemiology.  Am J Epidemiol. 2008;167(4):492-499.PubMedGoogle ScholarCrossref
34.
Chan  PS, Nallamothu  BK, Spertus  JA,  et al.  Impact of age and medical comorbidity on the effectiveness of implantable cardioverter-defibrillators for primary prevention.  Circ Cardiovasc Qual Outcomes. 2009;2(1):16-24.PubMedGoogle ScholarCrossref
35.
Harel  Z, Wald  R, McArthur  E,  et al.  Rehospitalizations and emergency department visits after hospital discharge in patients receiving maintenance hemodialysis.  J Am Soc Nephrol. 2015;26(12):3141-3150.PubMedGoogle ScholarCrossref
36.
Ronksley  PE, Hemmelgarn  BR, Manns  BJ,  et al.  Potentially preventable hospitalization among patients with CKD and high inpatient use.  Clin J Am Soc Nephrol. 2016;11(11):2022-2031.PubMedGoogle ScholarCrossref
37.
Korte  T, Jung  W, Ostermann  G,  et al.  Hospital readmission after transvenous cardioverter/defibrillator implantation: a single centre study.  Eur Heart J. 2000;21(14):1186-1191.PubMedGoogle ScholarCrossref
38.
Goldenberg  I, Moss  AJ, Hall  WJ,  et al; Multicenter Automatic Defibrillator Implantation Trial (MADIT) II Investigators.  Causes and consequences of heart failure after prophylactic implantation of a defibrillator in the Multicenter Automatic Defibrillator Implantation Trial II.  Circulation. 2006;113(24):2810-2817.PubMedGoogle ScholarCrossref
39.
Charytan  DM, Patrick  AR, Liu  J,  et al.  Trends in the use and outcomes of implantable cardioverter-defibrillators in patients undergoing dialysis in the United States.  Am J Kidney Dis. 2011;58(3):409-417.PubMedGoogle ScholarCrossref
40.
Sakhuja  R, Keebler  M, Lai  TS, McLaughlin Gavin  C, Thakur  R, Bhatt  DL.  Meta-analysis of mortality in dialysis patients with an implantable cardioverter defibrillator.  Am J Cardiol. 2009;103(5):735-741.PubMedGoogle ScholarCrossref
41.
Aggarwal  A, Wang  Y, Rumsfeld  JS, Curtis  JP, Heidenreich  PA; National Cardiovascular Data Registry.  Clinical characteristics and in-hospital outcome of patients with end-stage renal disease on dialysis referred for implantable cardioverter-defibrillator implantation.  Heart Rhythm. 2009;6(11):1565-1571.PubMedGoogle ScholarCrossref
42.
Tompkins  C, McLean  R, Cheng  A,  et al.  End-stage renal disease predicts complications in pacemaker and ICD implants.  J Cardiovasc Electrophysiol. 2011;22(10):1099-1104.PubMedGoogle ScholarCrossref
43.
Clerkin  KJ, Topkara  VK, Demmer  RT,  et al.  Implantable cardioverter-defibrillators in patients with a continuous-flow left ventricular assist device: an analysis of the INTERMACS Registry.  JACC Heart Fail. 2017;5(12):916-926.PubMedGoogle ScholarCrossref
44.
Sidney  S, Sorel  M, Quesenberry  CP  Jr, DeLuise  C, Lanes  S, Eisner  MD.  COPD and incident cardiovascular disease hospitalizations and mortality: Kaiser Permanente Medical Care Program.  Chest. 2005;128(4):2068-2075.PubMedGoogle ScholarCrossref
45.
Go  AS, Lee  WY, Yang  J, Lo  JC, Gurwitz  JH.  Statin therapy and risks for death and hospitalization in chronic heart failure.  JAMA. 2006;296(17):2105-2111.PubMedGoogle ScholarCrossref
46.
Selby  JV, Fireman  BH, Lundstrom  RJ,  et al.  Variation among hospitals in coronary-angiography practices and outcomes after myocardial infarction in a large health maintenance organization.  N Engl J Med. 1996;335(25):1888-1896.PubMedGoogle ScholarCrossref
47.
Setoguchi  S, Warner Stevenson  L, Stewart  GC,  et al.  Influence of healthy candidate bias in assessing clinical effectiveness for implantable cardioverter-defibrillators: cohort study of older patients with heart failure.  BMJ. 2014;348:g2866.PubMedGoogle ScholarCrossref
×