article analysis - Assignment Essays

find three different health care articles that use quantitative research. Do not use articles that appear in the topic Resources or textbook. Complete an article analysis for each using the “Article Analysis 2” template.

While APA style is not required for the body of this assignment, solid academic writing is expected, and documentation of sources should be presented using APA formatting guidelines, which can be found in the APA Style Guide, located in the Student Success Center.

Article Analysis

and Permalink

(APA format)

Description

Article Citation

Article 1

Article 2

Point

Description

Broad Topic Area/Title

Define Hypotheses

Define Independent and Dependent Variables and Types of Data for Variables

Population of Interest for the Study

Sample

Sampling Method

How Were Data Collected?

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Published on 24.11.2020 in Vol 9 , No 11 (2020) :November

 Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/19112, �rst published April 04,
2020.

Family Members’ Perspectives on Family and Social
Support Available to Suicidal Patients, and Health
Systems’ Interactions and Responses to Suicide
Cases in Alberta: Protocol for a Quantitative
Research Study
Rabab M Abou El-Magd 1 ;  Liana Urichuk 2 ;  Shireen Surood 2 ;  Daniel Li 2 ;
Andrew Greenshaw 1 ;  Mara Grunau 3 ;  Laureen MacNeil 4 ;  Ione Challborn 5 ;
David Grauwiler 5 ;  Robert Olson 3 ;  Vincent Israel Opoku Agyapong 1, 2

Article Authors Cited by Tweetations (1) Metrics

Abstract

Introduction

Methods

Results

Discussion

References

Abbreviations

Abstract

Background:
Suicide is a major cause of preventable death globally and a leading cause of death by injury in Canada. To support people who
experience suicidal thoughts and behaviors and to ultimately prevent people from dying by suicide, it is important to understand
individual and familial experiences with the health care system.

Objective:
We present the protocol for a study, the objective of which is to explore how people who died by suicide, and their family members,
interacted with the health care system.

Methods:
This is a quantitative research study. Data will be collected through a self-administered paper-based or online survey of the family
member of patients who died by suicide. The sample size was calculated to be 385 (margin of error ±3%).

Results:
Data collection will start in October 2020 and results will be available by March 2021. We expect the results to shed light on the
experiences of individuals who died by suicide and their family members with the health care system. The study has received
ethical clearance from the Health Ethics Research Board of the University of Alberta (Pro00096342).

Conclusions:



Articles Search artic

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https://orcid.org/0000-0002-6931-927

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https://orcid.org/0000-0003-1013-5921

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https://orcid.org/0000-0002-9776-234

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https://orcid.org/0000-0003-2644-53

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https://orcid.org/0000-0002-9097-900X

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https://orcid.org/0000-0002-6145-0801

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https://orcid.org/0000-0003-2607-9063

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https://orcid.org/0000-0002-2743-0372

https://www.researchprotocols.org/2020/11/e19112/

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Our study may inform practice, policy, and future research. The �ndings may shape how members of the health care system
respond to people who are at risk of suicide and their families.

International Registered Report Identi�er (IRRID):
PRR1-10.2196/191

JMIR Res Protoc 2020;9(11):e19112

doi:10.2196/19112

Keywords

suicide in Alberta (1); suicide (30); family members’ perspectives; social support (49); health systems interactions (1)

Introduction

Background

Suicide is a serious global public health problem, with an estimated 800,000 people reported to die by suicide every year [ ]. In
Canada, suicide remains the 9th leading cause of death and the second leading cause of death among children, youth, and young
adults [ ]. Suicide impacts people of all ages and backgrounds in Canada. Every day, an average of more than 10 Canadians die by
suicide. There are close to 6000 emergency department visits and 2000 hospitalizations every year for self-in�icted injuries [ ]. For
every person lost to suicide, many more experience thoughts of suicide or suicide attempts. For every death by suicide, a large circle
of survivors are signi�cantly affected by the loss. Each suicide results in 135 people exposed (ie, who knew the person), who may
need clinician services or support following exposure [ ].

We also recommend

Family Members’ Perspectives of Health Care System Interactions
With Suicidal Patients and Responses to Suicides: Protocol for a
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Erin Bryksa et al., JMIR Res Protoc, 2019

Exploring Suicidal Ideation Using an Innovative Mobile App-Strength
Within Me: The Usability and Acceptability of Setting up a Trial
Involving Mobile Technology and Mental Health Service Users

Ashley Jane Bruen et al., JMIR Mental Health, 2020

Designing Messaging to Engage Patients in an Online Suicide
Prevention Intervention: Survey Results From Patients With Current
Suicidal Ideation

Ursula Whiteside et al., J Med Internet Res, 201

Youth Experiences With Referrals to Mental Health Services in
Canada: Protocol for a Web-Based Cross-Sectional Survey Study

Shalini Lal et al., JMIR Res Protoc, 2020

Using New and Emerging Technologies to Identify and Respond to
Suicidality Among Help-Seeking Young People: A Cross-Sectional
Study

Frank Iorfino et al., J Med Internet Res, 20

Long-Term Safety and Tolerability of Daridorexant in Patients with
Insomnia Disorder

Dieter Kunz et al., ., 2022

The crosstalk between the caspase family and the cGAS‒STING
signaling pathway

Yongai Xiong et al., Journal of Molecular Cell Biology, 2021

SARS-CoV-2 immunity and functional recovery of COVID-19
patients 1-year after infection

Yan Zhan et al., Selections from Signal Transduction and Targeted
Therapy, 2021

COVID-19 in early 2021: current status and looking forward

Chengdi Wang et al., Selections from Signal Transduction and
Targeted Therapy, 2021

Investigation and analysis of psychological stress among non-
severe COVID-19 patients

Xiangmin Liu et al., Journal of Molecular Cell Biology, 2021

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There were 4000 suicides in Canada in 2018 [ ], with more than 500 of these deaths occurring in Alberta. Suicide is consistently a
leading cause of death among Albertans. Suicide claims more lives annually than other causes such as motor vehicle collisions and
homicides. Over 75% of those deaths occur among men, most between the ages of 30-69 years [ ]. Health care systems play a vital
role in suicide prevention. One study in Alberta, for instance, found that the majority of people who died by suicide used a health
service in the year prior to their death. They were also more likely to use the emergency department, in-patient services, or
community mental health services than those who died from other causes; they typically used health services for mental disorders
as well [ ].

In Alberta, which is the site of this study, suicide prevention initiatives, including Living Hope, are underway to enhance aspects of
the health care system, as evidenced by the Implementation Plan for the Edmonton Suicide Prevention Strategy [ ]. Living Hope
promotes a comprehensive preventative approach that seeks to enhance access to the protective factors that decrease the risk of
suicide. The implementation plan upholds the inherent value of every person and recognizes that residents of Alberta, both as
service providers and as community members, can offer the compassion, respect, and hope needed to increase resilience and
nurture hope for those contemplating suicide [ ].

The Mental Health Commission of Canada, in collaboration with the Canadian Association for Suicide Prevention, the Centre for
Suicide Prevention, the Public Health Agency of Canada, alongside an Advisory Committee comprising people with lived experience
related to suicide, have developed toolkits to support individuals who have been impacted by suicide. One toolkit is tailored for
people who have attempted suicide, and the other is focused on resources for people who have lost someone to suicide [ ].

Beyond stakeholder engagement [ ] and an understanding of the dimensions of service quality [ ], little is known locally about the
personal, family, and social circumstances of people who died by suicide in Alberta. Similarly, little is known about how individuals
who died by suicide and those close to them experienced the health care system. The current mechanism by which Alberta Health
Services (AHS) investigates suicide is through the Quality Assurance Review (QAR). A QAR of an adverse event utilizes the Systems
Analysis Methodology, which aims to determine what happened, how it happened, and what can be done to improve care for future
patients. This type of review generally involves engaging a multidisciplinary team to examine all of the health care system
components (eg, environment, task, policy, etc) as they relate to an event (or group of similar events). This process often results in
recommendations aimed at improving the quality and safety of health care delivery. The focus is on improving structures,
processes, and/or practices within AHS [ ]. QARs are done following a suicide on a case-by-case basis, and the results are not
shared beyond those directly involved. The privacy of the QAR limits case comparison and knowledge translation. Additionally, an
understanding of the context of death by suicide is needed, as it is thought to differ from the context of a suicide attempt. QARs
usually focus on the health systems’ contributions to the suicide and do not place much emphasis on examining the personal,
familial, and societal factors that also contribute to deaths by suicide. One study found that while individuals who attempt suicide
generally exhibited similar levels of depression, those who died by suicide were signi�cantly more likely to have experienced
signi�cant job stress and �nancial problems, left a suicide note, and used alcohol and drugs prior to the act [ ]. AHS is committed
to patient- and family-centered care [ ], which highlights the importance of talking to families about both their own and their
relatives’ experiences with the health care system. Ultimately, insight into the experiences of people who died by suicide, and their
family members, has the potential to inform policy and practice, and shape how members of the health care system, and AHS
speci�cally, respond to individuals who are at risk of suicide.

Objectives

The purpose of this study is to understand better the family and social circumstances of individuals who died by suicide, and how
those who died by suicide and their family members interacted with the health care system. This study extends the knowledge to be
gained from a recently completed qualitative study that examined family members’ perspectives on health system interactions with
those who died by suicide [ ].

Our speci�c quantitative research questions are:

1. What factors related to family, society, and health systems contribute to death by suicide in Alberta?

2. How do individuals impacted by the suicide of a family member perceive their own interactions with the health care system?

To the best of our knowledge, no previous province-wide study has examined the personal, familial, societal, and health systems
factors that contribute to suicide deaths in Canada. One study was conducted by Schaffer et al [ ] to investigate the population-
based analysis of health care contacts among suicide decedents prior to death by suicide. It was a systematic extraction of data
from records at the O�ce of the Chief Coroner of Ontario of each person who died by suicide in the city of Toronto from 1998 to

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2011 [ ]. Consequently, this work, the �rst of its kind in Alberta and in Canada, could help identify important factors that are
associated with deaths by suicide in the province of Alberta.

Methods

Study Design

This study utilizes a quantitative research design. Data will be collected through a self-administered paper-based or online survey of
the family members of patients who died by suicide ( ). A sample size of 385 was predetermined on the
assumption that with an annual average of 500 people dying by suicide in Alberta, a 95% CI, and one family member per suicide
decedent completing the survey, the sample size needed to estimate family members’ perspectives on health system interactions,
as well as family and social support for suicidal patients, with the margin of error ±3%, is 385. Data will be collected via both paper
format and online. Prospective participants will be provided with paper-based or online information lea�ets.

Participants

Participants will be adults; they should also have a close family member who has died by suicide in the previous 12 months and had
regular contact with this family member prior to their suicide, such that they are reasonably aware of their personal, family, and
social situation prior to their suicide as well as their interaction with the health care system. Participants do not have to identify
themselves and their submission of the survey implies their consent.

Data Collection

We initially designed a survey form that re�ected risk factors for dying by suicide identi�ed in the published literature as well as
additional factors to help answer our research questions. The draft survey questions were reviewed by the Canadian Mental Health
Association (CMHA), Alberta Division, and the Centre for Suicide Prevention, and changes were made based on the feedback
received. The survey was then pretested on two volunteer family members of patients who had died by suicide before being further
revised and �nalized for use in the study. The survey questions take 10-55 minutes to complete, and no incentives will be offered to
participants who complete the survey. Paper-based recruitment will be done in collaboration with the CMHA regional o�ces in
Alberta. The association runs focus groups for family members of people who died by suicide, with hundreds of people attending
annually. Information lea�ets and posters advertising the study will be distributed among prospective participants attending these
focus groups. Those interested in participating in the study will be provided with guidance on how to access the survey questions.

In addition, online versions of the survey will be promoted through the websites and social media feeds of AHS, the CMHA, the
Centre for Suicide Prevention, the Edmonton Mental Health Foundation, and the University of Alberta’s Faculty of Medicine and
Dentistry. The online survey is designed in accordance with the CHERRIES (Checklist for Reporting Results of Internet E-Surveys)
checklist [ ]. Prospective participants will be invited to review the online version of the information lea�et and proceed to complete
the survey. Information identifying participants will not be collected for the online survey, and completion and submission of the
survey denotes consent. The study will be conducted in accordance with the Declaration of Helsinki (Hong Kong Amendment) and
Good Clinical Practice (international guidelines). Informed consent will be obtained from each participant. The study has received
ethical clearance from Health Ethics Research Board of the University of Alberta (Pro00096342).

Data Analysis

Quantitative data will be analyzed using SPSS, version 26 (IBM Corp), using descriptive statistics and correlational analyses [ ]. A
chi-square test will be used to explore differences in responses between demographic variables of the suicide decedent and the
respondent.

Results

Multimedia Appendix 1

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Data collection is expected to commence in October 2020. Results will be available by March 2021. Findings from the study will
help illuminate factors related to family, society, and health systems, and the role they play in death by suicide in Alberta.

The study results will be disseminated at several levels, including to participants, practitioners, academics/researchers, and health
care organizations.

Our team will plan an organizational engagement strategy to advance discussions about feasibility and effectiveness prior to the
conclusion of the trial. This will help ensure the �ndings are a relevant part of decision-making processes. In addition, this may
facilitate the planning of a larger study that is endorsed at both leadership and operational levels so that the potential bene�ts of
the study results can reach participants in a timelier fashion.

Discussion

The main objective of this study is to investigate the familial, societal and health systems–based support available to individuals
who die by suicide in Alberta. It also aims to examine the support offered by the health care system in Alberta to family members of
patients who die by suicide.

Studies in other jurisdictions suggests that personal, familial, and social factors such as stigma [ ], public education [ , ],
psychiatric illness [ , ], age [ , ], gender [ , ], marital status [ ], positive support [ , ], familial history of suicide [ – ],
and alcohol consumption [ , ] are associated with death by suicide. Similarly, health system factors such as staff attitude toward
suicidal persons [ ], recency of hospitalization for suicide attempt and recent health care contact [ , , – ], underdiagnoses of
mental disorders and major depressions [ ], brevity of interactions with medical staff [ ], ignoring suicide-related warning signs
by health care providers [ ], lack of trust in health care services [ ], and relatives’ feelings of exclusion from information on
treatment [ ] have all been positively associated with deaths by suicide in studies conducted in other jurisdictions. The results of
our study will provide us with information on familial, societal, and health systems–related in�uences in Alberta as well as aspects
of care in need of further improvement and re�nement. The recommendations arising from this study have the potential to lead to
signi�cant system enhancements and reductions in suicide rates in Alberta and beyond.

Acknowledgments

This study is supported by Alberta Health Services, the Centre for Suicide Prevention, and the Canadian Mental Health Association.

The authors received no �nancial support for the research, authorship, and/or publication of this paper.

Authors’ Contributions

RMAE-M, contributed to the study design and drafted the initial and �nal versions of manuscript. LU, SS, DL, AG, MG, LM, IC, DG, and
RO contributed to the study design and reviewed the initial and �nal drafts of the manuscript. VA conceived and designed the study
and contributed to drafting the initial and �nal versions of the manuscript.

Con�icts of Interest

None declared.

Multimedia Appendix 1

Quantitative research study survey.

DOCX File , 36 KB

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Abbreviations

AHS: Alberta Health Services
CHERRIES: Checklist for Reporting Results of Internet E-Surveys
CMHA: Canadian Mental Health Association
QAR: Quality Assurance Review

Edited by G Eysenbach; submitted 04.04.20; peer-reviewed by E Kleiman, K Fox; comments to author 12.06.20; revised version received
02.08.20; accepted 18.08.20; published 24.11.20

©Rabab M Abou El-Magd, Liana Urichuk, Shireen Surood, Daniel Li, Andrew Greenshaw, Mara Grunau, Laureen MacNeil, Ione
Challborn, David Grauwiler, Robert Olson, Vincent Israel Opoku Agyapong. Originally published in JMIR Research Protocols
(http://www.researchprotocols.org), 24.11.2020. 

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Abou El-Magd RM, Urichuk L, Surood S, Li D, Greenshaw A, Grunau M, MacNeil L, Challborn I, Grauwiler D, Olson R, Agyapong VIO
Family Members’ Perspectives on Family and Social Support Available to Suicidal Patients, and Health Systems’ Interactions and
Responses to Suicide Cases in Alberta: Protocol for a Quantitative Research Study
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Real world effectiveness of primary implantable cardioverter defibrillators implanted
during hospital admissions for exacerbation of heart failure or other acute co-morbidities

Author(s): Chih-Ying Chen, Lynne Warner Stevenson, Garrick C Stewart, Deepak L Bhatt,
Manisha Desai, John D Seeger, Lauren Williams, Jessica J Jalbert and Soko Setoguchi

Source: BMJ: British Medical Journal , 13 Jul 2015 – 19 Jul 2015, Vol. 351 (13 Jul 2015 –
19 Jul 2015)

Published by: BMJ

Stable URL: https://www.jstor.org/stable/10.2307/2652233

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the bmj | BMJ 2015;101h;1 29 | doi1 02.00;6/bmj.h;1 2

RESEARCH

open access

1Division of
Pharmacoepidemiology and
Pharmacoeconomics,
Department of Medicine,
Brigham and Women’s Hospital
and Harvard Medical School,
Boston, MA 02120, USA
2Division of Cardiovascular
Medicine, Brigham and
Women’s Hospital and Harvard
Medical School, Boston, MA
02115, USA
3Quantitative Sciences Unit,
Stanford University School of
Medicine, Palo Alto, CA 94305,
USA
4Duke Clinical Research Institute,
Durham, NC 27705, USA
Correspondence to:
S Setoguchi
soko.setoguchi@duke.edu
Additional material is published
online only. To view please visit
the journal online (http://dx.doi.
org/10.1136/bmj.h3529)
Cite this as: BMJ 2015;351:h3529
doi: 10.1136/bmj.h3529

Accepted: 16 Jun 201

Real world effectiveness of primary implantable cardioverter
defibrillators implanted during hospital admissions for
exacerbation of heart failure or other acute co-morbidities:
cohort study of older patients with heart failure
Chih-Ying Chen,1 Lynne Warner Stevenson,2 Garrick C Stewart,2 Deepak L Bhatt,2 Manisha Desai,3
John D Seeger,1 Lauren Williams,1 Jessica J Jalbert,1 Soko Setoguchi4

ABSTRACT
ObjeCtives
To examine the effectiveness of primary implantable
cardioverter defibrillators (ICDs) in elderly patients
receiving the device during a hospital admission for
exacerbation of heart failure or other acute
co-morbidities, with an emphasis on adjustment for
early mortality and other factors reflecting healthy
candidate bias rather than the effect of the ICD.
Design
Retrospective cohort study.
setting
Linked data from the Centers for Medicare and Medicaid
Services and American College of Cardiology-National
Cardiovascular Data Registry ICD registry, nationwide
heart failure registry, and Medicare claims data 2004-09.
POPulatiOn
23 111 patients aged ≥66 who were admitted to
hospital for exacerbation of heart failure or other acute
co-morbidities and eligible for primary ICDs.
Main OutCOMe Measures
All cause mortality and sudden cardiac death. Latency
analyses with Cox regression were used to derive crude

hazard ratios and hazard ratios adjusted for high
dimension propensity score for outcomes after 180
days from index implantation or discharge.
results
Patients who received an ICD during a hospital
admission had lower crude mortality risk than patients
who did not receive an ICD (40% v 60% at three years);
however, with conditioning on 180 day survival and
with adjustment for high dimension propensity score,
the apparent benefit with ICD was no longer evident for
sudden cardiac death (adjusted hazard ratio 0.95,
95% confidence interval 0.78 to 1.17) and had a
diminished impact on total mortality (0.91, 0.82 to
1.00). There were trends towards a benefit with ICD in
reducing mortality or sudden cardiac death in patients
who had had a myocardial infarction more than 40
days previously, left bundle branch block, or low serum
B type natriuretic peptide; however, these trends did
not reach significance.
COnClusiOn
After adjustment for healthy candidate bias and
confounding, the benefits of primary ICD therapy seen
in pivotal trials were not apparent in patients aged 66
or over who received ICDs during a hospital admission
for exacerbation of heart failure or other acute
co-morbidities. Future research is warranted to further
identify subgroups of elderly patients who are more
likely to benefit from ICDs. Recognition of those
patients whose dominant risk factors are from
decompensated heart failure and non-cardiac
co-morbidities will allow better focus on ICDs in those
patients for whom the device offers the most benefit
and provides meaningful prolonging of life.

Introduction
The most recent worldwide survey of cardiac pacing
and implantable cardioverter defibrillators (ICDs)
from 2009 reported a large global rise in the use of
these devices.1 The United States is the world’s largest
consumer of ICDs, with 133 262 implants (or 434 new
implants per one million people), which was 1.5 times
the rate of the world’s second largest implanter.1
Review of US nationwide data on ICD implantation
has shown that real world recipients are typically
older than patients in previous trials, with a median
age of 74,2 similar to reports from other countries.3 As
the population ages, the number of elderly patients
considered for ICD implantation worldwide will most
likely increase.4 5

WhAT IS AlReAdy knoWn on ThIS TopIC
The benefit of primary implantable cardioverter defibrillators (ICDs) has been
shown in outpatients with symptoms of stable mild-to-moderate heart failure
It is unclear how the impact of primary ICDs in preventing sudden cardiac death
translates to overall survival benefits among elderly patients who receive the
devices during acute admissions for exacerbation of heart failure or other acute
co-morbidities
Evaluating survival benefits of ICDs in the real world setting without accounting for
healthy candidate effect could overestimate its effectiveness

WhAT ThIS STudy AddS
This study used multiple analytical approaches (latency analysis and adjustment
for high dimensional propensity scores) to account for healthy candidate bias in
assessing effectiveness of ICDs with observational data
After adjustment for healthy candidate bias and confounding, the benefits of
primary ICD therapy shown in previous trials were not present in elderly patients
who received the device during admission for exacerbation of heart failure or other
acute co-morbidities
The trend was similar in subgroups of patients aged under 80, in both sexes, and in
racial subgroups
There was a trend towards benefit in reducing mortality or sudden cardiac death in
patients who had had a myocardial infarction more than 40 days previously, left
bundle branch block, or low serum B type natriuretic peptide

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doi1 02.00;6/bmj.h;1 29 | BMJ 2015;101h;1 29 | the bmj

RESEARCH

The benefit of primary ICD therapy in landmark trials
was shown in patients with heart failure in their 60s.6 7
The results from these trials, however, might not
directly apply to older populations. Real world recipi-
ents of ICDs generally have more non-cardiac co-mor-
bidities.2 8 Furthermore, primary ICD trials were
conducted in outpatients with symptoms of stable
mild-to-moderate heart failure.6 7 About a third of older
recipients of ICDs have undergone an implantation
during a hospital admission for exacerbation of heart
failure or other acute co-morbidities.9 In patients with
chronic heart failure, the early post-discharge period
after an acute admission is associated with a high risk
of mortality, during which progressive heart failure is
the most likely cause of death.10 It is therefore unclear
how the impact of primary ICDs on the prevention of
sudden cardiac death translates to overall survival ben-
efits among elderly patients who received the devices
during acute admissions.

We examined the effectiveness of ICDs outside the
previously studied populations in ICD trials. Our target
population was elderly patients who received the device
during acute admissions for exacerbation of heart fail-
ure or other acute co-morbidities. Assessment of the
potential impact on survival with real world data
requires caution because of healthy candidate bias.
This type of selection bias could arise when patients at
high risk of complications or deemed to be too sick to
benefit are not selected for an ICD, and when patients
are less interested in preventing sudden death because
of the existing burden of other chronic illness. Evalua-
tion of the clinical effectiveness of ICDs without consid-
eration of the healthy candidate effect could
overestimate its benefit.11 We therefore used specific
design and analytic approaches to account for this bias
when assessing the clinical effectiveness of ICDs
implanted during admission for exacerbation of heart
failure or other acute causes among elderly patients
and potential differences in the effectiveness of ICDs by
demographic and clinical characteristics. This included
adjustment for mortality during the first 180 days, a
period during which previous trials have shown no ben-
efit of ICDs.6

Methods
Data sources
We conducted a retrospective cohort study using the
ICD registry of the Centers for Medicare and Medicaid
Services (CMS) (2005-08); ICD registry of the American
College of Cardiology-National Cardiovascular Data
Registry (ACC-NCDR) (2005-08); a nationwide heart fail-
ure registry aggregated from several quality improve-
ment and accreditation programs, including the
American Heart Association’s Get With the Guidelines
program (2005-08); and Medicare institutional and
non-institutional claims (2004-09).

ACC-NCDR and CMS-ICD registry
The Medicare/Medicaid ICD registry is a subset of the
American College of Cardiology-National Cardiov-
ascular Data Registry’s ICD registry, which is the sole

repository for data on ICD implantation for Medicare
beneficiaries.2 12-14 Hospital personnel enter data on the
registry under routine quality control review.15 The reg-
istry includes information on patient history, clinical
characteristics, drugs, facility information, provider
information, indications for ICD, device information,
and inpatient complications.

Heart failure registry
The data from the national clinical registries for
patients with heart failure were aggregated from sev-
eral quality improvement and accreditation programs
managed by Outcome Sciences using common data
elements, data clarification procedures, and quality
assurance practices. The aggregate database includes
data from over 800 US hospitals in 50 states with
close to 300 000 patients with a primary diagnosis of
heart failure. The dataset has been successfully used
to assess quality of care outcomes in patients with
heart failure.16 17 Information in the registry includes
demographics, characteristics of heart failure, car-
diac and non-cardiac medical history, laboratory
data, vital signs, findings on relevant physical exam-
inations, drugs on admission and at discharge, and
other relevant treatment/procedures before and
during admission.

Medicare institutional and non-institutional files
Medicare is the national health insurance program
administrated by the US government. Most of its bene-
ficiaries are aged 65 or older. The Medicare institutional
and non-institutional files contain data on final claims
submitted by healthcare providers. Main information
contained in those files includes diagnosis and proce-
dures, dates of service, reimbursement amount, pro-
vider identifiers, and demographic information on
beneficiaries. Appendix 1 provides more details of these
data sources.

Data linkage
We linked the combined ICD registry and the heart
failure registry to Medicare claims data using four
non-unique identifiers: date of birth, sex, admission
date for implantation of the ICD, and provider identifi-
ers, which is described in detail elsewhere18 and in
appendix 1. Briefly, we validated this linkage among
the subset of 196 923 patients who had a unique iden-
tifier in the ICD registry. Our linkage using non-unique
identifiers yielded 98% specificity, 95% sensitivity,
and a 98% positive predictive value compared with the
linkage method using both non-unique and unique
identifiers.1

study population
Our study population consisted of elderly patients
with heart failure with and without ICDs who had
acute hospital admissions for heart failure or any
co-morbidities and were considered eligible for ICD
therapy for primary prevention. The primary cohort
consisted of older patients who were covered by Medi-
care and who could be linked to either the ICD or heart

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RESEARCH

failure registry. The secondary cohort consisted of
older patients who were nested within the heart fail-
ure registry linked to Medicare data. To ensure their
eligibility for primary ICD therapy, all study patients
were required to have an ejection fraction of ≤35% at
the time of admission. We excluded patients with car-
diac arrest or sustained ventricular tachycardia, for
whom ICDs would be secondary prevention. To ensure
our study patients had a sufficient look-back period for
assessing pertinent covariates, we required that
patients had health insurance coverage for one year
before the index procedure or admission. We also
required patients to be aged ≥66 to ensure everyone
had a one year look-back period. The exposure status
(ICD implantation) was defined as a patient having a
record of ICD implantation in the ICD registry. Lastly,
we excluded patients who received cardiac resynchro-
nization therapy with a defibrillator (CRT-D) as these
patients met additional criteria for this indication.19-21
The information on the type of implanted device was
obtained from the ICD registry.

Outcomes
Our primary outcome was all cause mortality. The date
of death was obtained from the Medicare beneficiary
summary file. The secondary outcome was sudden car-
diac death, defined by using a previously validated
algorithm (positive predictive value 87%).22 The desig-
nation of sudden cardiac death was made if the patient
was not staying at a terminal institution (that is, hospi-
tal or nursing home) on the date of death, their code for
underlying cause of death was consistent with sudden
cardiac death (see appendix 2), and they did not have a
“terminal procedure inconsistent with unresuscitated
cardiac arrest, such as radiology, thrombolysis or gen-
eral anesthetic.”22 The cause of death was obtained
from the National Death Index23 (appendix 3). We
obtained index data on all the patients with an ICD and
a randomly selected sample (70%) of those without in
the primary cohort.

Latency analyses
We used latency analysis to adjust for potential healthy
candidate bias, analogous to the healthy worker
effect.24-26 Follow-up began after a prespecified latent
period after the index date. In the current study, the
index date was the date of implantation for the ICD
group or the date of discharge for those without an ICD.
As previous trials have shown that survival benefits of
ICDs are not apparent until 1-1.5 years after implanta-
tion,6 7 we used latency periods of 180 days (primary
latency period) and 365 days (secondary latency
period). All patients were followed until the occurrence
of an outcome event (death or sudden cardiac death) or
the end of the study period (31 December 2009).

Statistical analyses
Patient characteristics were described as percentages
for categorical variables by ICD exposure status.
Medians and interquartile ranges or means and stan-
dard deviations were used for continuous variables.

We plotted observed mortality by ICD exposure status
using Kaplan-Meier estimates. Cox regression was
used to derive crude hazard ratios and hazard ratios
adjusted for high dimensional propensity score for
outcomes using the groups without an ICD (that is,
older people admitted with equivalent indication for
a primary ICD but who did not receive an ICD) as a
reference.

We used the high dimensional propensity score
methods to adjust for surrogates of unmeasured fac-
tors to overcome residual confounding. The algorithm
was used to thoroughly screen Medicare claims data
to identify covariates that could collectively be surro-
gates for unobserved factors influencing the patient
selection for ICDs.27 This allows for maximum control
of potential confounders given the available informa-
tion in our data sources. For example, although we
did not have information on NYHA class for heart fail-
ure, we can adjust for this based on other available
proxies for severity of heart failure in the dataset,
including numbers of previous hospital admissions
for heart failure, ejection fraction, blood pressure,
B type natriuretic peptide, or co-morbidities. To cal-
culate high dimensional propensity scores, we used
data from the year before the index date. The 200
most common codes in each data dimension were
identified, from which 500 likely confounders were
selected based on their prevalence and potential for
confounding in the study population. The scores at
the index date were derived from predicted probabili-
ties from logistic regression models containing all of
the empirically identified covariates and several pre-
defined variables: demographic characteristics; cause
of index admission, admission source, admission
type, and diagnostic/laboratory test results for ejec-
tion fraction, systolic blood pressure, serum sodium,
serum B type natriuretic peptide, and estimated
glomerular filtration rate.28

We handled variables with missing values (such as
systolic blood pressure, serum sodium, B type natri-
uretic peptide, and creatinine) by multiple imputa-
tion29 and assumed an underlying multivariate
normal distribution. Our analysis was based on five
imputed datasets in which the imputation model
included all variables in the outcome model (ICD use
outcomes and potential confounders) as well as vari-
ables related to missingness (see appendix 4 for
potential predictors of missing values included in the
imputation model). We repeated all analyses in the
secondary cohort.

Subgroup analyses
We assessed the heterogeneity of the effects of ICD by
demographic characteristics (age, sex, and race) and
three clinical characteristics (history of myocardial
infarction,30 31 presence of left bundle branch
block,32 33 and type natriuretic peptide concentration
at index admission) using separate Cox models in
each subgroup.

Age was categorized in four groups in five year
increments. We used a previously validated claim

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RESEARCH

based definition to identify myocardial infarction34
and classified this population into three groups:
recent myocardial infarction (one or more myocardial
infarctions within the 40 days before the index date),
old myocardial infarction (one or more myocardial
infarctions 41-365 days before the index date), and no
myocardial infarction within 365 days before the
index admission. We also used a claim based defini-
tion to identify patients with left bundle branch block
within 365 days before the index date (ICD-9 (interna-
tional classification of diseases, ninth revision, clini-
cal modification), 426.2x or 426.3x). We required the
diagnosis of left bundle branch block to be made
during a hospital admission. We classified our
patients into two groups according to B type natri-
uretic peptide concentration (low v high) using a cut-
off value of 800 ng/L.35-37

We adjusted for potential confounding using the high
dimensional propensity score estimated from the entire
cohort.38

Sensitivity analyses
We repeated all analyses in a subset of the population
with complete laboratory data and a subset of the pop-
ulation matched on high dimensional propensity score
(that is, the high dimensional propensity score matched
analyses). This was done to assess the impact of the

missing data assumption on our findings and the
robustness of the adjustment using the high dimen-
sional propensity score.

All analyses were conducted with SAS 9.2 (SAS Insti-
tute, Cary, NC).

Results
study population and characteristics
We identified a cohort of 23 111 patients with heart
failure (5258 with an ICD and 17 853 without) who met
eligibility criteria (figs 1 and 2 ). For over 90% of the
patients, the diagnosis that led to the index admis-
sion was heart failure or other cardiac causes.
Patients with ICDs were younger and were more likely
to be men than patients without ICDs. They also had a
lower ejection fraction, more previous admissions for
cardiac diseases, and more physician visits, and their
heart failure was more likely to have an ischemic
cause. Patients with ICDs also had a higher preva-
lence of non-cardiac admissions, chronic kidney dis-
ease, metastatic cancer, lower estimated glomerular
filtration rate, and higher B type natriuretic peptide
(table1). These findings were similar in the secondary
cohort (appendix 5).

ICD implantations among patients
aged ≥66 at admission, 2005-08 (n=211 229)

Records with complete information
on linkage variables (n=196 923)

Records linked to Medicare fee for
service inpatient records (n=118 047)

Patients with low ejection fraction
and �rst primary ICD (n=70 417)

Information on cause of death requested for 100% of population

Primary ICD recipients eligible for analyses (n=5258)

Excluded:
Previous cardiac arrest (n=17 923)
History of sustained VT (n=2443)
<1 year continuous eligibility before ICD implant (n=3282) No claim indicating ICD implantation (n=145) CRT-D recipients (n=35 711) Patients received ICD implantation during elective hospital admission (n=12 630)

Records with missing or invalid linkage variables
– that is, admission date, provider ID,

date of birth, and sex (n=14 306, 6.8%)

Excluded:
Missing ejection fraction (n=3297)
Ejection fraction >35% (n=13 367)
Secondary prevention (n=35 872)
Repeated ICD implantation (n=1858)

Fig 1 | identification of study population of patients
with heart failure with implantable cardioverter
defibrillator (iCD)

Admissions for heart failure among patients
aged ≥66 at admission, 2005-08 (n=307 505)

Records with complete information and
valid on linkage variables (n=284 638)

Outcome registry records linked to Medicare
fee for service inpatient records (n=210 144)

Patients with low ejection fraction and admitted
for heart failure or comorbidities (n=43 891)

Information on cause of death
requested for about 70% of population

Patients with cause of death information
eligible for analyses (n=17 853)

Patients eligible for primary ICD therapy
but did not received ICDs (n=25 211)

Excluded:
Previous ICD implantation (n=9096)
ICD implantations during admission (n=1784)
Record identi�ed in ICD registry (n=4504)
Previous cardiac arrest in registry (n=8107)
History of sustained VT (n=418)
<1 year continuous eligibility before index admission (n=2622)

Records with suspicious value or missing value on
linkage variable – that is, admission date, date of birth,
or discharge date, provider state (n=22 867, 7.2%)

Excluded:
Missing ejection fraction (n=58 820)
Ejection fraction >35% (n=104 258)

Fig 2 | identification of study population of patients
with heart failure without implantable cardioverter
defibrillator (iCD)

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RESEARCH

Crude mortality risks and Kaplan-Meier curves
During follow-up (average 2.8 years, range 1 day-5
years), 12 293 (53%) patients died. The crude mortality
risk among our Medicare population admitted to the
hospital was 34% (95% confidence interval 33% to 35%)
at one year and 56% (55% to 57%) at three years. The
mortality curves for the patients with and without ICDs
(fig 3) began to diverge immediately after ICD implanta-
tion (2.4% (2.0% to 2.8%) v 12.7% (12% to 13%) at 30
days). Crude mortality at one year was lower for ICD
recipients than for eligible patients without an ICD
(18% (17% to 19%) v 39% (38% to 40%) at one year and
40% (38% to 41%) v 60% (60% to 61%) at three years).
However, the crude mortality in these hospitalized
Medicare patients with an ICD at one year was similar to

the mortality seen at three years in trials of ICDs in
ambulatory recipients.6 7

effectiveness of iCDs
After adjustment for bias with latency analyses and the
high dimension propensity score, patients who received
an ICD during an acute admission for heart failure or
other co-morbidity did not have a substantially differ-
ent risk of mortality (hazard ratio 0.91, 95% confidence
interval 0.82 to 1.00) or sudden cardiac death (0.95, 0.78
to 1.17) than those who had no ICD during their admis-
sion (table 2 , fig 4 ). This trend remained when we
extended the latency period to 365 days (table 2 ) and
when the analyses were restricted to the smaller sec-
ondary cohort (table 2 , fig 4 ). This trend was also simi-
lar in the sensitivity analyses (table 3).

effectiveness of iCDs in demographic subgroups
The proportion of eligible patients who received ICDs
varied among demographic subgroups with notable dif-
ferences in age and sex. Women and the oldest patients
made up smaller fractions of recipients of ICDs: 14%
women versus 29% men, and 12% in the ≥81 age group
versus 28-34% in other age groups (table 4 ). This
suggests that ICD implantation might have been more
selective among these groups. We found no significant
differences in ICD effectiveness among most
demographic subgroups, except in the small group of
patients aged over 81. Among these older patients, ICD
use was associated with a lower mortality risk (hazard
ratio 0.78, 95% confidence interval 0.65 to 0.93) but not
with a significant reduction in risk of sudden cardiac
death (0.74, 0.52 to 1.04; fig 5 , table 4 ). The trend was
similar when we extended the latency period to 365
days (table 4).

effectiveness of iCDs in clinical subgroups
Effectiveness by myocardial infarction status
The findings in the subgroups with and without recent
myocardial infarction showed similar effectiveness of
ICDs (table 5). Among patients with an old myocardial
infarction, ICD therapy was associated with a signifi-
cantly lower risk of mortality (37% reduction, hazard
ratio 0.63, 95% confidence interval 0.45 to 0.86) and a

table 1 | Main baseline characteristics of patients aged ≥66 with heart failure by
exposure status (use of implantable cardioverter defibrillator (iCD)) in primary cohort.
Figures are numbers (percentage) of patients unless stated otherwise

no iCD (n=17 853) iCD (n=5258) P value
Mean (SD) age (years) 80.0 (7.8) 75.5 (6.0) <0.001 Men 9321 (52) 3763 (72) <0.001 White 15 068 (84) 4392 (84) 0.13 Median (IQR) ejection fraction (%) 29 (20-33) 25 (20-30) <0.001 Median (IQR) Charlson scores 3 (1-4) 3 (1-4) 0.68 Patients with ≥1 hospital admission by cause: Any causes 9168 (51) 2912 (55) <0.001 Heart failure 2844 (16) 1139 (22) <0.001 Myocardial infarction (MI) 203 (1) 84 (2) 0.01 Non-MI ischemic heart disease 104 (1) 74 (1) <0.001 Other cardiac disease 51 (0) 39 (1) <0.001 Non-cardiac causes 6501 (36) 1673 (32) <0.001 Mean (SD) No of prior outpatient visits 10.3 (9.4) 11.7 (9.3) <0.001 ≥1 prior outpatient visit 16 167 (91) 4974 (95) <0.001 ≥1 prior skilled nursing facility admission 2679 (15) 375 (7) <0.001 Heart failure due to ischemic causes 14 165 (79) 4587 (87) <0.001 Any cerebrovascular disease 3860 (22) 1224 (23) 0.01 Hemorrhagic stroke 198 (1) 93 (2) <0.001 Ischemic stroke 1376 (8) 462 (9) 0.01 Transient ischemic attack 1175 (7) 424 (8) <0.001 Other cerebrovascular disease 2741 (15) 816 (16) 0.78 Peripheral vascular disease 4147 (23) 1279 (24) 0.

Dementia 3503 (20) 584 (11) <0.001 Depression 2821 (16) 662 (13) <0.001 Any liver disease 1118 (6) 323 (6) 0.77 Gastrointestinal ulcer/bleeding 2755 (15) 736 (14) 0.01 Dialysis 527 (3) 151 (3) 0.78 Chronic kidney disease 8009 (45) 2201 (42) <0.001 Chronic obstructive pulmonary disease 8283 (46) 2436 (46) 0.93 Cancer (except non-melanoma skin cancer) 3020 (17) 898 (17) 0.79 Metastatic cancer 566 (3) 100 (2) <0.001 Diabetes 8648 (48) 2800 (53) <0.001 Median (IQR) systolic blood pressure (SBP) 133 (116-152) 130 (115-146) <0.001 SBP missing 9230 (52) 70 (1) — Median (IQR) serum sodium 138 (136-141) 138 (136-140) 0.004 Sodium missing 12 032 (67) 29 (1) — Median (IQR) serum B type natriuretic peptide (BNP)

1249 (657-2258) 760 (336-1590) <0.001

BNP missing 12 810 (72) 3167 (60) —
Median (IQR) serum creatinine (SCr) 1.3 (1.0-1.8) 1.2 (1.0-1.6) <0.001 SCr missing 11 488 (64) 22 (0) — Median (IQR) estimated glomerular filtration rate 47 (32-64) 56 (41-72) <0.001 IQR=interquartile range

Years since index time

Cr
ud

e
m

or
ta

lit
y

0 1 2 3 4 5
0

0.2

0.4

0.8

1.0
Non-ICD group (n=17 853)
ICD group (n=5258)

Fig 3 | Crude mortality curves for patients with heart failure
with (n=5258) and without implantable cardioverter
defibrillator (iCD) (n=17 853) in primary cohort

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RESEARCH

non-significant 26% reduction in sudden cardiac death
(0.74, 0.40 to 1.35).

Effectiveness by presence of left bundle branch block
ICD use was associated with a lower risk of mortality
(hazard ratio 0.64, 95% confidence interval 0.34 to 1.17)
and sudden cardiac death (0.51, 0.16 to 1.61) among
patients with left bundle branch block (table 5);
although the confidence intervals were wide.

Effectiveness by serum B type natriuretic peptide
status
A total of 7134 patients had their admission serum B
type natriuretic peptide value documented (2091 (40%)
patients with ICD v 5043 (28%) without). Risks of mor-
tality and sudden cardiac death associated with ICD
therapy were numerically lower among patients with
low serum B type natriuretic peptide (hazard ratios 0.86

(95% confidence interval 0.67 to 1.10) for mortality and
1.11 (0.69 to 1.77) for sudden cardiac death) than those
with high type natriuretic peptide (0.94 (0.78 to 1.13)
and 1.20 (0.85 to 1.69), respectively); however, these risk
estimates were not significant (table 5).

discussion
Main findings
The benefits of primary ICD therapy that had been
previously shown in ambulatory patients with heart
failure do not seem to translate to elderly patients
who receive the device during acute hospital admis-
sions for exacerbation of heart failure or other acute
co-morbidities. Adjustment for potential confounding
and healthy candidate bias11 in this specific popula-
tion reduced the apparent impact of ICD therapy to a
5% reduction in sudden cardiac death and a 9%
reduction in all cause mortality, which were not sig-
nificant. This trend remained similar among sub-
groups of patients aged under 80 and across the sexes
and racial subgroups. There was, however, a trend
towards benefit of ICDs implanted during acute
admissions to hospital in reducing mortality or sud-
den cardiac death in patients who had non-recent
myocardial infarction more than 40 days prior to
implantation, left bundle branch block, or lower
serum B type natriuretic peptide, although these also
did not reach significance.

strength and limitations
Our study is the first to use latency analysis to account
for the healthy candidate bias in assessing effectiveness

table 2 | number of events and incidence rates for death and sudden cardiac death in patients with and without implantable cardioverter defibrillator
(iCD) in latency 180 day* and latency 365 day* analyses

group

Death sudden cardiac death

event/ir†
Hr (95% Ci)

Crude adjusted‡ Crude adjusted
latency 180 day
Primary cohort
ICD (n=5258) 1307/159 0.69 (0.65 to 0.74) 0.91 (0.82 to 1.00) 330/40 0.71 (0.62 to 0.82) 0.95 (0.78 to 1.17)
No ICD (n=17 853) 5299/241 Reference Reference 1326/60 Reference Reference
Secondary cohort
ICD (n=412) 125/201 0.90 (0.71 to 1.14) 1.01 (0.79 to 1.29) 40/64 1.26 (0.82 to 1.93) 1.20 (0.71 to 2.00)
No ICD (n=17 853) 5299/241 Reference Reference 1326/60 Reference Reference
latency 365 day
Primary cohort
ICD (n=5258) 925/155 0.73 (0.68 to 0.79) 0.94 (0.83 to 1.06) 234/39 0.75 (0.65 to 0.87) 0.96 (0.76 to 1.21)
No ICD (n=17 853) 3524/220 Reference Reference 880/55 Reference Reference
Secondary cohort
ICD (n=412) 90/203 0.95 (0.72 to 1.27) 1.02 (0.72 to 1.43) 28/63 1.13 (0.67 to 1.90) 0.94 (0.49 to 1.80)
No ICD (n=17 853) 3524/220 Reference Reference 880/55 Reference Reference

All cause mortality
Primary cohort
Secondary cohort
Sudden cardiac death
Primary cohort
Secondary cohort

0.91 (0.82 to 1.00)
1.01 (0.79 to 1.29)

0.95 (0.78 to 1.17)
1.20 (0.71 to 2.00)

0.5 0.8 1 2

Adjusted hazard
ratio (95% CI)

Fig 4 | Hazard ratios (adjusted for high dimension
propensity score) for death and sudden cardiac death
among primary and secondary cohorts in latency 180 day
analyses

table 3 | Primary and sensitivity analyses of iCD effectiveness. Figures are hazard ratios (95% Ci) adjusted for high dimension propensity score
(hdPs)*Complete case analyses not conducted in secondary cohort because of smaller size of cohort.

Primary cohort secondary cohort

iCD/no iCD Death sudden cardiac death iCD/no iCD Death
sudden cardiac
death

Primary analyses 5258/17 853 0.91 (0.82 to 1.00) 0.95 (0.78 to 1.17) 412/17 853 1.01 (0.79 to 1.29) 1.20 (0.71 to 2.00)
hdPS matched analyses 2254/2254 0.92 (0.77 to 1.09) 1.03 (0.78 to 1.35) 291/291 0.99 (0.72 to 1.37) 1.50 (0.94 to 2.38)
Complete case analyses 1801/3261 0.87 (0.72 to 1.05) 1.15 (0.79 to 1.66) —* —* —*

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of ICDs with observational data. Several lines of
evidence suggest the existence of healthy candidate
bias in the observational studies of ICD. Although the
mortality curves of previous randomized trials show
no ICD benefit until 1-1.5 years,6 7 we observed an
immediate separation of the mortality curves in both
our current and previous study,11 which is probably
caused by healthy candidate bias due to selection of
patients for ICD rather than immediate ICD benefit.
The existence of healthy candidate bias among
patients in hospital has been supported by evidence
that ICD recipients had a 40-50% lower risk of adverse
events, such as non-traumatic hip fracture and admis-
sion to a skilled nursing facility, than similar patients
who did not receive an ICD.11 This bias in patient and
physician selection for ICD implantation cannot be
completely eliminated by adjustment for known risk
factors and can lead to overestimation of the net bene-
fit of ICDs.11 The disparity between groups is further
suggested by a greater difference in total mortality
(including non-cardiac death) than in sudden cardiac
death, which is the only event that is expected to be
reduced by ICDs.

Among strategies that have been developed to
account for healthy candidate bias, latency analysis has
been shown to be useful25 and is suitable to evaluate
ICD effectiveness because of the delayed benefit seen in
trials.6 7 This method allows a less biased evaluation by
focusing on a time period more likely to reflect true ICD

table 4 | effectiveness of implantable cardioverter defibrillators (iCDs) in demographic subgroups in latency 180 day* and latency 365 day* analyses

sample size
(iCD/no iCD)

% of eligible patients
received iCD event (iCD/no iCD) ir† (iCD/no iCD)

adjusted Hr (95% Ci)‡
latency 180 day latency 365 day

Outcome=death
Age (years):
66-70 1322/2534 34 285/568 127/137 0.89 (0.70 to 1.13) 1.02 (0.77 to 1.36)
71-75 1347/2950 31 301/755 136/168 1.11 (0.88 to 1.40) 1.33 (1.02 to 1.74)
76-80 1394/3670 28 362/1068 172/216 0.92 (0.75 to 1.12) 0.82 (0.65 to 1.04)
≥81 1195/8699 12 359/2908 213/345 0.78§ (0.65 to 0.93) 0.78§ (0.63 to 0.96)
Sex:
Men 3763/9321 29 945/2779 161/245 0.92 (0.81 to 1.04) 0.96 (0.82 to 1.11)
Women 1495/8532 14 362/2520 152/237 0.90 (0.75 to 1.07) 0.90 (0.74 to 1.10)
Race:
White 4392/15 068 23 1088/4402 156/241 0.90 (0.81 to 1.01) 0.96 (0.84 to 1.10)
Black 613/1790 26 154/594 172/240 0.93 (0.70 to 1.24) 0.82 (0.57 to 1.16)
Other 253/995 20 65/303 181/240 0.94 (0.60 to 1.47) 0.84 (0.50 to 1.39)
Outcome=sudden cardiac death
Age (years):
66-70 1322/2534 34 73/125 33/30 1.04 (0.63 to 1.72) 1.12 (0.62 to 2.00)
71-75 1347/2950 31 59/176 27/39 0.81 (0.48 to 1.35) 0.73 (0.41 to 1.27)
76-80 1394/3670 28 99/215 47/44 1.43 (0.95 to 2.14) 1.30 (0.82 to 2.07)
≥81 1195/8699 12 99/810 59/96 0.74 (0.52 to 1.04) 0.80 (0.54 to 1.18)
Sex:
Men 3763/9321 29 248/732 42/65 1.00 (0.78 to 1.28) 1.08 (0.82 to 1.42)
Women 1495/8532 14 82/594 35/56 0.86 (0.59 to 1.26) 0.75 (0.50 to 1.15)
Race:
White 4392/15 068 23 288/1121 41/61 0.98 (0.79 to 1.23) 1.06 (0.82 to 1.36)
Black 613/1790 26 31/135 35/54 0.77 (0.40 to 1.47) 0.44 (0.20 to 1.00)
Other 253/995 20 11/70 31/55 0.75 (0.24 to 2.33) 0.60 (0.16 to 2.22)
*Latency 180 day analyses: starting follow-up from 180 days after index time; latency 365 day analyses: starting follow-up from 365 days after index time
†IR=incidence rate per 1000 person years.
‡Adjusted for high dimension propensity score.
§P<0.05.

Death
Age (years):
66-70
71-75
76-80
≥81
Men
Women
Ethnicity:
White
Black
Other
Sudden cardiac death
Age (years):
66-70
71-75
76-80
≥81
Men
Women
Ethnicity:
White
Black
Other

0.89 (0.70 to 1.13)
1.11 (0.88 to 1.40)
0.92 (0.75 to 1.12)
0.78 (0.65 to 0.93)
0.92 (0.81 to 1.04)
0.90 (0.75 to 1.07)

0.90 (0.81 to 1.01)
0.93 (0.70 to 1.24)
0.94 (0.60 to 1.47)

1.04 (0.63 to 1.72)
0.81 (0.48 to 1.35)
1.43 (0.95 to 2.14)
0.74 (0.52 to 1.04)
1.00 (0.78 to 1.28)
0.86 (0.59 to 1.26)

0.98 (0.79 to 1.23)
0.77 (0.40 to 1.47)
0.75 (0.24 to 2.33)

0.2 0.5 21 4

Adjusted hazard
ratio (95% CI)

Fig 5 | Hazard ratios (adjusted for high dimension
propensity score) for death and sudden cardiac death
among demographic subgroups in latency 180 day analyses

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RESEARCH

effectiveness rather than preferential selection of ICDs
for and by healthier patients. A few caveats should be
noted. Healthy candidate bias can continue to influence
outcomes beyond the initial chosen latency period,
which is likely given our conservative 180 day latency
period. Therefore, our latency analysis might still over-
estimate ICD survival benefit. Additionally, latency
analyses could underestimate the survival benefit of
ICDs if lifesaving events occurred more frequently
during the prespecified latency period. Nevertheless,
trial data have indicated such an underestimation is
likely minimal.6 7

Several limitations need to be considered when our
findings are interpreted. First, and foremost, our find-
ings are not applicable to elderly patients who would
undergo ICD implantation electively as outpatients.
Our study population was limited to elderly patients
who received ICDs during acute admissions to hospital
for reasons other than ICD implantation. We selected
this population for analysis of effectiveness, as in a
previous study by Hernandez and colleagues,39
because data are available for comparison of similar
patients admitted with heart failure who did not
receive ICDs. Many elderly patients, however, receive
the device as an elective procedure. The effectiveness
of ICD among these healthier patients is more likely to
be comparable with that of the trials on which the
guideline recommendations are based. Additionally,
our findings cannot be generalized to patients who
received cardiac resynchronization therapy with their
ICD (CRT-D) as this is likely to decrease heart failure
events. We also did not include patients who received

the ICD as secondary prevention, for which lifesaving
ICD therapies are more likely to occur.

We could not identify all patients with a history of
myocardial infarction, left bundle branch block, or
low serum B type natriuretic peptide values because
we used a claims based definition and because of
missing data on B type natriuretic peptide. Therefore,
we did not have a sufficient sample size to confirm the
trends seen for those subgroups thought to derive
more benefit from ICDs.30-33 35 Neither did we have
complete information on all the recognized risk fac-
tors for death from heart failure, such as the New York
Heart Association class and duration of QRS. Residual
confounding is possible. Regarding the general prob-
lem of missing values in registry variables, our results
were robust to the missingness assumption, as the
results based on imputed datasets were similar to
complete case analyses.40

Comparison with other studies
Our study of patients admitted to hospital failed to
show survival benefits of primary ICD therapy similar
to those seen in trials of healthier ambulatory
patients, in whom there was a 23-31% reduction in
mortality6 7 ; this is likely explained by differences in
the patient population. The median age of the SCD-
HeFT population was 60 and the mean age of the
MADIT II population was 64.6 7 The patients in our
study were older, with a mean age of 75 in the ICD
recipients and 80 in those who did not receive an ICD.
It is not clear that age alone is the major difference as
benefit has been shown in subsets of elderly patients

table 5 | effectiveness of implantable cardioverter defibrillators (iCDs) in clinical subgroups in latency 180 day* and latency 365 day* analyses

sample size
(iCD/no iCD)

% of eligible patients
received iCD event (iCD/no iCD) ir† (iCD/no iCD)

adjusted Hr (95% Ci)‡
latency 180 day latency 365 day

Outcome=death
Myocardial infarction:
Recent 1685/8160 17 565/4476 165/333 0.92 (0.77 to 1.09) 0.94 (0.76 to 1.16)
Old 448/904 33 186/648 226/532 0.63§ (0.45 to 0.86) 0.74 (0.51 to 1.08)
No 3125/8789 26 1108/5317 172/369 0.90 (0.79 to 1.03) 0.91 (0.78 to 1.07)
Left bundle branch block:
No 5143/17 406 23 1809/10 161 173/358 0.92 (0.83 to 1.01) 0.94 (0.83 to 1.06)
Yes 115/447 20 50/280 213/401 0.64 (0.34 to 1.17) 0.75 (0.37 to 1.49)
B type natriuretic peptide:
<800 1085/1604 40 359/905 157/302 0.86 (0.67 to 1.10) 0.86 (0.65 to 1.15) ≥800 1006/3439 23 490/2373 278/464 0.94 (0.78 to 1.13) 0.91 (0.72 to 1.13) Outcome=sudden cardiac death Myocardial infarction: Recent 1685/8160 17 115/542 34/40 1.03 (0.76 to 1.39) 0.92 (0.64 to 1.33) Old 448/904 33 27/83 33/68 0.74 (0.40 to 1.35) 1.12 (0.56 to 2.21) No 3125/8789 26 188/701 29/49 0.91 (0.70 to 1.18) 1.00 (0.73 to 1.38) Left bundle branch block: No 5143/17 406 23 325/1289 31/45 0.97 (0.80 to 1.17) 1.02 (0.81 to 1.29) Yes 115/447 20 5/37 21/53 0.51 (0.16 to 1.61) 0.42 (0.09 to 2.05) B type natriuretic peptide: <800 1085/1604 40 92/199 40/66 1.11 (0.69 to 1.77) 1.42 (0.82 to 2.46) ≥800 1006/3439 23 132/539 75/105 1.20 (0.85 to 1.69) 1.17 (0.76 to 1.80) *Latency 180 day analyses: starting follow-up from 180 days after index time; latency 365 day analyses: starting follow-up from 365 days after index time. †IR=incidence rate per 1000 person years. ‡Adjusted for high dimension propensity score. §P<0.05.

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the bmj | BMJ 2015;101h;1 29 | doi1 02.00;6/bmj.h;1 29

RESEARCH

in randomized trials.41 More importantly, previous tri-
als were conducted among ambulatory patients with
symptoms of stable mild-to-moderate heart failure,
many of whom had not previously been admitted to
hospital with heart failure. Our study focused on a
population of patients admitted for exacerbation of
heart failure or other acute causes. This particular
subset of patients has a higher baseline burden of
heart failure and other co-morbidities than trial pop-
ulations. Older age,42-44 advanced heart failure,42 45 46
and non-cardiac co-morbidities46 increase the likeli-
hood of mortality that will not be prevented by an
ICD, and thus present competing risks for prolonged
survival with an ICD.

Four previous studies reported that primary ICD
implantation in routine clinical practice was associ-
ated with a sizable survival benefit comparable with
those seen in major trials,39 47-49 but only one39
included the subset of elderly patients who under-
went device implantation during an acute hospital
admission. In addition, two of these studies enrolled
participants from outpatient cardiology clinics,
where the ICD was generally implanted as an elective
procedure.48 49 The previous observational stud-
ies39 48 49 also found immediate separation of survival
curves at a point in time unlikely to be strongly influ-
enced by ICDs.

implications for practice and future research
Our results extend the understanding of the clinical
effectiveness of ICDs in elderly patients admitted to
hospital, who are not typical of patients in trials.
Patients admitted for acute exacerbation of heart fail-
ure or other co-morbidities might be at greater risk
both during ICD implantation and for non-arrhythmic
events after discharge. For early risk, it is possible that
there is some similarity between patients admitted for
heart failure and patients early after myocardial infarc-
tion, who also showed no benefit from ICD and an
increased risk of non-sudden cardiac death.30 31 50 For
patients non-electively admitted to hospital for heart
failure or their comorbidities, it might be appropriate
to delay the decision to implant a primary ICD until
they have been discharged and can be re-evaluated in
the outpatient setting.

Our findings provide no reason to restrict access to
ICDs for older patients with heart failure who other-
wise seem similar to patients in pivotal ICD trials.
The subset of elderly patients who received primary
prevention ICDs in trials in outpatient settings were
previously shown to derive benefit.41 While an ideal
study would be a randomized trial in elderly patients,
this is not likely to be performed in time to inform
imminent clinical decision making. We failed to
show a benefit only for those older patients receiving
ICDs during an urgent admission for exacerbation
of heart failure or other acute causes. Future research
is warranted to identify other groups of older
patients who have a high or low likelihood of benefit
from ICDs to maximize the lifesaving potential of
their use.

The efficacy of ICDs in women has also been ques-
tioned, in large part because they were under-repre-
sented in previous trials.6 7 51 In our study of 10 027
eligible women, we observed no heterogeneity in effec-
tiveness of ICDs between the sexes; however, it has
been shown that female candidates for primary ICD are
likely have a lower risk of sudden cardiac death than
male candidates.52 Women also more often experience
complications with ICDs.53 54 Thus, the benefit-risk
equation of ICDs among women might require further
investigation.

The higher survival associated with ICDs in patients
aged over 81 further emphasizes the likelihood of resid-
ual bias that cannot be adjusted for using currently
reported patient characteristics. The oldest group of
patients in our study comprised the lowest proportion
of ICD recipients out of eligible recipients. This proba-
bly indicates a particularly rigorous selection of healthy
ICD recipients in this age group, excluding patients
with obvious co-morbidities and more general frailty.
Therefore, despite the use of latency analysis and
adjustment for high dimension propensity scores,
accounting for patient and physician selection is still
challenging in assessing real world clinical
effectiveness.

Shared decision making regarding primary preven-
tion ICD has been recommended to involve explicit
consideration of patient preferences and the likelihood
of competing risks for mortality in all patients.55 These
decisions require particular scrutiny for patients
admitted to hospital for exacerbation of heart failure or
other acute causes. Recognition of those patients
whose dominant risks are from decompensated heart
failure and non-cardiac co-morbidities will allow for
focused ICD therapy in those patients for whom the
device offers the most benefit to provide meaningful
prolonging of life.
We thank Jeptha Curtis of Yale School of Medicine and Yale-New
Haven Hospital, Sherri Dodd of Medtronic, Kenneth Ellenbogen of
Virginia Commonwealth University School of Medicine, Marcel E
Salive of the National Institute of Aging at National Institutes of
Health, and Lynette Voshage-Stahl of Boston Scientific for the
guidance and expertise they contributed to this project by serving
on the technical expert panel. We also thank Drew Pratt of the
National Cancer Institute at the National Institutes of Health for his
review of the manuscript. The views expressed in this article are
those of the authors and do not represent opinions of the panel
members.

This article was presented in part at the AHA Scientific Sessions 2013
on 18 November 2013 in Dallas, TX, and at the 2014 International
Conference of Pharmacoepidemiology on 25 October 2014 in Taipei,
Taiwan.

The views expressed in this article are those of the authors and do
not represent policies of the AHRQ, CMS or the US DHHS. This
manuscript was prepared while CYC was employed at Brigham
and Women’s Hospital/Harvard Medical School. CYC is now a
visiting scientist at the Division of Epidemiology II, Office of
Pharmacovigilance and Epidemiology, Office of Surveillance and
Epidemiology, Center for Drug Evaluation and Research, US Food
and Drug Administration. The opinions expressed in this work are
the author’s own and do not reflect the view of the Food and Drug
Administration, the Department of Health and Human Services, or
the US government.

Contributors: SS and LWS contributed to the acquisition of the
data. C-YC, SS and LWS developed the study design. C-YC, SS, and
MD contributed to the analysis of the data. C-YC and SS drafted the
manuscript. All authors contributed to the interpretation of the data

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doi1 02.00;6/bmj.h;1 29 | BMJ 2015;101h;1 29 | the bmj

RESEARCH

and the revision of the work, and all approved the final version to
be published. CYC and SS are the guarantors. The authors of this
report are responsible for its content. Statements in the report
should not be construed as endorsement by the Agency for
Healthcare Research and Quality or the US Department of Health
and Human Services.
Funding: This project is funded by contract No HHSA290-2005-
0016-I -TO3 from the Agency for Healthcare Research and Quality
(AHRQ), US Department of Health and Human Services (DHHS) as
part of the Developing Evidence to Inform Decisions about
Effectiveness (DEcIDE) program, IAA Contract 500-2010-00001I TO6
and CEA Contract 500-2010-00001I TO2 from the Centers for
Medicare and Medicaid Services (CMS), US DHHS. The funding
agency had no role in the design and conduct of the study and in the
collection, analysis, and interpretation of the data. The manuscript
was based on a report done under contract to AHRQ; AHRQ had the
draft report reviewed by independent peer reviewers before
acceptance of the final report.

Competing interests: All authors have completed the ICMJE uniform
disclosure form at http://www.icmje.org/coi_disclosure
(available on request from the corresponding author) and declare no
financial relationships with any organizations that might have an
interest in the submitted work in the previous 3 years; no other
relationships or activities that could appear to have influenced the
submitted work. SS is supported by a mid-career development award
grant K02-HS017731 from the AHRQ, US DHHS. She also reported
receiving research support from Johnson and Johnson and receiving
personal income for consulting from Sanofi-Aventis. SS has made
available online a detailed listing of financial disclosures (http://
www.dcri.duke.edu/about-us/conflict-of-interest/). JDS is a paid
consultant to Optum Insight and WHISCON. DBL discloses the
following relationship— advisory board: Cardax, Elsevier Practice
Update Cardiology, Medscape Cardiology, Regado Biosciences;
board of directors: Boston VA Research Institute, Society of
Cardiovascular Patient Care; chair: American Heart Association Get
With The Guidelines Steering Committee; data monitoring
committees: Duke Clinical Research Institute, Harvard Clinical
Research Institute, Mayo Clinic, Population Health Research Institute;
honorariums: American College of Cardiology (senior associate
editor, Clinical Trials and News, ACC.org), Belvoir Publications (editor
in chief, Harvard Heart Letter), Duke Clinical Research Institute
(clinical trial steering committees), Harvard Clinical Research
Institute (clinical trial steering committee), HMP Communications
(editor in chief, Journal of Invasive Cardiology), Journal of the
American College of Cardiology (associate editor), Population Health
Research Institute (clinical trial steering committee), Slack
Publications (Chief Medical Editor, Cardiology Today’s Intervention),
WebMD (CME steering committees); other: Clinical Cardiology
(deputy editor); research funding: Amarin, AstraZeneca, Biotronik,
Bristol-Myers Squibb, Eisai, Ethicon, Forest Laboratories, Ischemix,
Medtronic, Pfizer, Roche, Sanofi Aventis, St. Jude Medical, The
Medicines Company; trustee: American College of Cardiology;
unfunded research: FlowCo, PLx Pharma, Takeda.
Ethical approval: This study was approved by the institutional review
board of Brigham and Women’s Hospital (IRB No 2009P002819).
Transparency: The lead authors, CYC and SS, affirm that this
manuscript is an honest, accurate, and transparent account of the
study being reported; that no important aspects of the study have
been omitted; and that any discrepancies from the study as planned
(and, if relevant, registered) have been explained.
Data sharing: No additional data available.
This is an Open Access article distributed in accordance with the
Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license,
which permits others to distribute, remix, adapt, build upon this work
non-commercially, and license their derivative works on different terms,
provided the original work is properly cited and the use is non-
commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

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Appendix 1: Detailed description of the databases and
data linkage
Appendix 2: Codes for underlying cause of death
diagnosis for considering as sudden cardiac death
cases
Appendix 3: Description of National Death
Appendix 4: Potential predictors of missing values in
the imputation model
Appendix 5: Main baseline characteristics by
exposure status in secondary cohort

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