Dataset Information

Sudden Cardiac Death (SCD) - risk stratification and prediction with molecular biomarkers.

ABSTRACT: Sudden cardiac death (SCD) is a sudden, unexpected death that is caused by the loss of heart function. While SCD affects many patients suffering from coronary artery diseases (CAD) and heart failure (HF), a considerable number of SCD events occur in asymptomatic individuals. Certain risk factors for SCD have been identified and incorporated in different clinical scores, however, risk stratification using such algorithms is only useful for health management rather than for early detection and prediction of future SCD events in high-risk individuals. In this review, we discuss different molecular biomarkers that are used for early detection of SCD. This includes genetic biomarkers, where the majority of them are genomic variants for genes that encode for ion channels. Meanwhile, protein biomarkers often denote proteins that play roles in pathophysiological processes that lead to CAD and HF, notably (i) atherosclerosis that involves oxidative stress and inflammation, as well as (ii) cardiac tissue damage that involves neurohormonal and hemodynamic regulation and myocardial stress. Finally, we outline existing challenges and future directions including the use of OMICS strategy for biomarker discovery and the multimarker panels.

SUBMITTER: Osman J

PROVIDER: S-EPMC6530025 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

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Sudden Cardiac Death (SCD) - risk stratification and prediction with molecular biomarkers.

Osman Junaida J Tan Shing Cheng SC Lee Pey Yee PY Low Teck Yew TY Jamal Rahman R

Journal of biomedical science 20190522 1

Sudden cardiac death (SCD) is a sudden, unexpected death that is caused by the loss of heart function. While SCD affects many patients suffering from coronary artery diseases (CAD) and heart failure (HF), a considerable number of SCD events occur in asymptomatic individuals. Certain risk factors for SCD have been identified and incorporated in different clinical scores, however, risk stratification using such algorithms is only useful for health management rather than for early detection and pre ...[more]

PMID: 31118017

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Project description:BackgroundStudies of out-of-hospital cardiac arrest and sudden cardiac death (SCD) use emergency medical services records, death certificates, or definitions that infer cause of death; thus, the true incidence of SCD is unknown. Over 90% of SCDs occur out-of-hospital; nonforensic autopsies are rarely performed, and therefore causes of death are presumed. We conducted a medical examiner-based investigation to determine the precise incidence and autopsy-defined causes of all SCDs in an entire metropolitan area. We hypothesized that postmortem investigation would identify actual sudden arrhythmic deaths among presumed SCDs.MethodsBetween February 1, 2011, and March 1, 2014, we prospectively identified all incident deaths attributed to out-of-hospital cardiac arrest (emergency medical services primary impression, cardiac arrest) between 18 to 90 years of age in San Francisco County for autopsy, toxicology, and histology via medical examiner surveillance of consecutive out-of-hospital deaths, all reported by law. We obtained comprehensive records to determine whether out-of-hospital cardiac arrest deaths met World Health Organization (WHO) criteria for SCD. We reviewed death certificates filed quarterly for missed SCDs. Autopsy-defined sudden arrhythmic deaths had no extracardiac cause of death or acute heart failure. A multidisciplinary committee adjudicated final cause.ResultsAll 20 440 deaths were reviewed; 12 671 were unattended and reported to the medical examiner. From these, we identified 912 out-of-hospital cardiac arrest deaths; 541 (59%) met WHO SCD criteria (mean 62.8 years, 69% male) and 525 (97%) were autopsied. Eighty-nine additional WHO-defined SCDs occurred within 3 weeks of active medical care with the death certificate signed by the attending physician, ineligible for autopsy but included in the countywide WHO-defined SCD incidence of 29.6/100 000 person-years, highest in black men (P<0.0001). Of 525 WHO-defined SCDs, 301 (57%) had no cardiac history. Leading causes of death were coronary disease (32%), occult overdose (13.5%), cardiomyopathy (10%), cardiac hypertrophy (8%), and neurological (5.5%). Autopsy-defined sudden arrhythmic deaths were 55.8% (293/525) of overall, 65% (78/120) of witnessed, and 53% (215/405) of unwitnessed WHO-defined SCDs (P=0.024); 286 of 293 (98%) had structural cardiac disease.ConclusionsForty percent of deaths attributed to stated cardiac arrest were not sudden or unexpected, and nearly half of presumed SCDs were not arrhythmic. These findings have implications for the accuracy of SCDs as defined by WHO criteria or emergency medical services records in aggregate mortality data, clinical trials, and cohort studies.

Project description: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.

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

Sudden Cardiac Death (SCD) - risk stratification and prediction with molecular biomarkers.

Publications

Sudden Cardiac Death (SCD) - risk stratification and prediction with molecular biomarkers.

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