Dataset Information

A Simple Community-Based Risk-Prediction Score for Sudden Cardiac Death.

ABSTRACT: BACKGROUND:Although sudden cardiac death is a leading cause of death in the United States, most victims of sudden cardiac death are not identified as at risk prior to death. We sought to derive and validate a population-based risk score that predicts sudden cardiac death. METHODS:The Atherosclerosis Risk in Communities (ARIC) Study recorded clinical measures from men and women aged 45-64 years at baseline; 11,335 white and 3780 black participants were included in this analysis. Participants were followed over 10 years and sudden cardiac death was physician adjudicated. Cox proportional hazards models were used to derive race-specific equations to estimate the 10-year sudden cardiac death risk. Covariates for the risk score were selected from available demographic and clinical variables. Utility was assessed by calculating discrimination (Harrell's C-index) and calibration (Hosmer-Lemeshow chi-squared test). The white-specific equation was validated among 5626 Framingham Heart Study participants. RESULTS:During 10 years' follow-up among ARIC participants (mean age 54.4 years, 52.4% women), 145 participants experienced sudden cardiac death; the majority occurred in the highest quintile of predicted risk. Model covariates included age, sex, total cholesterol, lipid-lowering and hypertension medication use, blood pressure, smoking status, diabetes, and body mass index. The score yielded very good internal discrimination (white-specific C-index 0.82; 95% confidence interval [CI], 0.78-0.85; black-specific C-index 0.75; 95% CI, 0.68-0.82) and very good external discrimination among Framingham participants (C-index 0.82; 95% CI, 0.79-0.86). Calibration plots indicated excellent calibration in ARIC (white-specific chi-squared 5.3, P = .82; black-specific chi-squared 4.1, P = .77), and a simple recalibration led to excellent fit within Framingham (chi-squared 2.1, P = 0.99). CONCLUSIONS:The proposed risk scores may be used to identify those at risk for sudden cardiac death within 10 years and particularly classify those at highest risk who may merit further screening.

SUBMITTER: Bogle BM

PROVIDER: S-EPMC5910195 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

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A Simple Community-Based Risk-Prediction Score for Sudden Cardiac Death.

Bogle Brittany M BM Ning Hongyan H Goldberger Jeffrey J JJ Mehrotra Sanjay S Lloyd-Jones Donald M DM

The American journal of medicine 20171219 5

<h4>Background</h4>Although sudden cardiac death is a leading cause of death in the United States, most victims of sudden cardiac death are not identified as at risk prior to death. We sought to derive and validate a population-based risk score that predicts sudden cardiac death.<h4>Methods</h4>The Atherosclerosis Risk in Communities (ARIC) Study recorded clinical measures from men and women aged 45-64 years at baseline; 11,335 white and 3780 black participants were included in this analysis. Pa ...[more]

PMID: 29273191

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Project description:BackgroundHypertrophic cardiomyopathy is the leading cause of sudden cardiac death (SCD) in children and young adults. Our objective was to develop and validate a SCD risk prediction model in pediatric hypertrophic cardiomyopathy to guide SCD prevention strategies.MethodsIn an international multicenter observational cohort study, phenotype-positive patients with isolated hypertrophic cardiomyopathy <18 years of age at diagnosis were eligible. The primary outcome variable was the time from diagnosis to a composite of SCD events at 5-year follow-up: SCD, resuscitated sudden cardiac arrest, and aborted SCD, that is, appropriate shock following primary prevention implantable cardioverter defibrillators. Competing risk models with cause-specific hazard regression were used to identify and quantify clinical and genetic factors associated with SCD. The cause-specific regression model was implemented using boosting, and tuned with 10 repeated 4-fold cross-validations. The final model was fitted using all data with the tuned hyperparameter value that maximizes the c-statistic, and its performance was characterized by using the c-statistic for competing risk models. The final model was validated in an independent external cohort (SHaRe [Sarcomeric Human Cardiomyopathy Registry], n=285).ResultsOverall, 572 patients met eligibility criteria with 2855 patient-years of follow-up. The 5-year cumulative proportion of SCD events was 9.1% (14 SCD, 25 resuscitated sudden cardiac arrests, and 14 aborted SCD). Risk predictors included age at diagnosis, documented nonsustained ventricular tachycardia, unexplained syncope, septal diameter z-score, left ventricular posterior wall diameter z score, left atrial diameter z score, peak left ventricular outflow tract gradient, and presence of a pathogenic variant. Unlike in adults, left ventricular outflow tract gradient had an inverse association, and family history of SCD had no association with SCD. Clinical and clinical/genetic models were developed to predict 5-year freedom from SCD. Both models adequately discriminated between patients with and without SCD events with a c-statistic of 0.75 and 0.76, respectively, and demonstrated good agreement between predicted and observed events in the primary and validation cohorts (validation c-statistic 0.71 and 0.72, respectively).ConclusionOur study provides a validated SCD risk prediction model with >70% prediction accuracy and incorporates risk factors that are unique to pediatric hypertrophic cardiomyopathy. An individualized risk prediction model has the potential to improve the application of clinical practice guidelines and shared decision making for implantable cardioverter defibrillator insertion. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT0403679.

Dataset Information

A Simple Community-Based Risk-Prediction Score for Sudden Cardiac Death.

Publications

A Simple Community-Based Risk-Prediction Score for Sudden Cardiac Death.

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