Public Health Burden of Sudden Cardiac Death in the United States

Stecker, Eric C.; Reinier, Kyndaron; Marijon, Éloi; Narayanan, Kumar; Teodorescu, Carmen; Uy‐Evanado, Audrey; Gunson, Karen; Jui, Jonathan; Chugh, Sumeet S.

doi:10.1161/circep.113.001034

Cited by 278 publications

(188 citation statements)

References 15 publications

Supporting

Mentioning

159

Contrasting

Unclassified

Order By: Relevance

“…The annual incidence of SCD increases as a function of advancing age, being 100-fold less frequent in individuals <30 years of age (0.001 %) than it is in adults >35 years. 4,5 There is a similar incidence in Europe, with reports of out-of-hospital cardiac arrest ranging from 0.04 % to 0.1 %. [6][7][8] When the aetiological definition is limited to coronary artery disease (CAD) and its tachyarrhythmic burden, the estimate is <200,000 events per year.…”

Section: Epidemiologymentioning

confidence: 88%

A Clinical Perspective on Sudden Cardiac Death

2016

Arrhythmia &Amp; Electrophysiology Review

View full text Add to dashboard Cite

This article presents the epidemiology, aetiology and pathophysiology of sudden cardiac death. The modern management of survivors as well as of family members of victims is discussed, as are the relevant recommendations of guidelines prepared by learned societies. KeywordsOut-of-hospital cardiac arrest, sports-related sudden death, sudden cardiac death, sudden unexplained death syndrome Disclosure: The authors have no conflicts of interest to declare.

show abstract

Section: Epidemiologymentioning

confidence: 88%

A Clinical Perspective on Sudden Cardiac Death

2016

Arrhythmia &Amp; Electrophysiology Review

View full text Add to dashboard Cite

show abstract

“…518 s udden cardiac death is a major public health hazard, accounting for >200 000 deaths annually and 40% to 50% of years of potential life lost from heart disease. 1 The implantable cardioverter-defibrillator (ICD) can abort sudden cardiac death by terminating malignant arrhythmias and thereby confers a survival benefit to at-risk patients. [2][3][4][5] Heart failure (HF) with reduced left ventricular ejection fraction is one of the major risk factors for sudden cardiac death in current clinical practice.…”

Section: Discussionmentioning

confidence: 99%

Sex and Race/Ethnicity Differences in Implantable Cardioverter-Defibrillator Counseling and Use Among Patients Hospitalized With Heart Failure

et al. 2016

View full text Add to dashboard Cite

Original research article BACKGROUND: Previous studies have found that women and black patients eligible for a primary prevention implantable cardioverterdefibrillator (ICD) are less likely than men or white patients to receive one. METHODS:We performed an observational analysis of the Get With The Guidelines-Heart Failure Program from January 1, 2011, to March 21, 2014. Patients admitted with heart failure and an ejection fraction ≤35% without an ICD were included. Rates of ICD counseling among eligible patients and ICD receipt among counseled patients were examined by sex and race/ethnicity. RESULTS:Among 21 059 patients from 236 sites, 4755 (22.6%) received predischarge ICD counseling. Women were counseled less frequently than men (19.3% versus 24.6%, P<0.001, adjusted odds ratio [OR], 0.84; 95% confidence interval [CI], 0.78-0.91). Racial and ethnic minorities were less likely to receive counseling than white patients (black 22.6%, Hispanic 18.6%, other race/ethnic group 14.4% versus white 24.3%, P<0.001 for each): adjusted OR versus white, 0.69; 95% CI, 0.63 to 0.76 for black patients; adjusted OR, 0.62; 95% CI, 0.55 to 0.70 for Hispanic patients; adjusted OR, 0.53; 95% CI, 0.43 to 0.65 for other patients. Among the 4755 counseled patients, 2977 (62.6%) received an ICD or had one planned for placement after hospital stay. Among those counseled, women and men were similarly likely to receive an ICD (adjusted OR, 1.13; 95% CI, 0.99-1.29). However, black (adjusted OR, 0.70; 95% CI, 0.56-0.88) and Hispanic patients (adjusted OR, 0.68; 95% CI, 0.46-1.01) were less likely to receive an ICD. CONCLUSIONS:Up to 4 of 5 hospitalized patients with heart failure eligible for ICD counseling did not receive it, particularly women and minority patients. Among counseled patients, ICD use differences by race and ethnicity persisted. 518 s udden cardiac death is a major public health hazard, accounting for >200 000 deaths annually and 40% to 50% of years of potential life lost from heart disease. 1 The implantable cardioverter-defibrillator (ICD) can abort sudden cardiac death by terminating malignant arrhythmias and thereby confers a survival benefit to at-risk patients. [2][3][4][5] Heart failure (HF) with reduced left ventricular ejection fraction is one of the major risk factors for sudden cardiac death in current clinical practice. As a consequence, professional societies recommend that certain patients with reduced left ventricular ejection fraction receive an ICD. 6 In 2012, ICD counseling was established as a metric of HF quality of care by the American College of Cardiology and the American Heart Association. 7Previous studies documented low rates of ICD use among those eligible, particularly among women and minorities. In a previous analysis of Get With The Guidelines (GWTG)-HF, only 1 in 3 potentially eligible patients actually received an ICD or was discharged with plans to receive one. 8 After accounting for other demographic and hospital factors, women and black patients were approximately one-third less likely...

show abstract

“…In‐hospital cardiac arrest is a major burden to public health, which affects patient safety 1, 2, 3. More than a half of cardiac arrests result from respiratory failure or hypovolemic shock, and 80% of patients with cardiac arrest show signs of deterioration in the 8 hours before cardiac arrest 4, 5, 6, 7, 8, 9.…”

Section: Introductionmentioning

confidence: 99%

An Algorithm Based on Deep Learning for Predicting In‐Hospital Cardiac Arrest

Kwon

Lee²,

Lee³

et al. 2018

JAHA

234

205

View full text Add to dashboard Cite

BackgroundIn‐hospital cardiac arrest is a major burden to public health, which affects patient safety. Although traditional track‐and‐trigger systems are used to predict cardiac arrest early, they have limitations, with low sensitivity and high false‐alarm rates. We propose a deep learning–based early warning system that shows higher performance than the existing track‐and‐trigger systems.Methods and ResultsThis retrospective cohort study reviewed patients who were admitted to 2 hospitals from June 2010 to July 2017. A total of 52 131 patients were included. Specifically, a recurrent neural network was trained using data from June 2010 to January 2017. The result was tested using the data from February to July 2017. The primary outcome was cardiac arrest, and the secondary outcome was death without attempted resuscitation. As comparative measures, we used the area under the receiver operating characteristic curve (AUROC), the area under the precision–recall curve (AUPRC), and the net reclassification index. Furthermore, we evaluated sensitivity while varying the number of alarms. The deep learning–based early warning system (AUROC: 0.850; AUPRC: 0.044) significantly outperformed a modified early warning score (AUROC: 0.603; AUPRC: 0.003), a random forest algorithm (AUROC: 0.780; AUPRC: 0.014), and logistic regression (AUROC: 0.613; AUPRC: 0.007). Furthermore, the deep learning–based early warning system reduced the number of alarms by 82.2%, 13.5%, and 42.1% compared with the modified early warning system, random forest, and logistic regression, respectively, at the same sensitivity.ConclusionsAn algorithm based on deep learning had high sensitivity and a low false‐alarm rate for detection of patients with cardiac arrest in the multicenter study.

show abstract

Public Health Burden of Sudden Cardiac Death in the United States

Cited by 278 publications

References 15 publications

A Clinical Perspective on Sudden Cardiac Death

A Clinical Perspective on Sudden Cardiac Death

Sex and Race/Ethnicity Differences in Implantable Cardioverter-Defibrillator Counseling and Use Among Patients Hospitalized With Heart Failure

An Algorithm Based on Deep Learning for Predicting In‐Hospital Cardiac Arrest

Contact Info

Product

Resources

About