Sudden Cardiac Death Risk Stratification

Deyell, Marc W.; Krahn, Andrew D.; Goldberger, Jeffrey J.

doi:10.1161/circresaha.116.304493

Cited by 106 publications

(75 citation statements)

References 120 publications

(126 reference statements)

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“…11 Expert panels have highlighted the lack of a baseline model that estimates an individual’s risk for SCD as a major obstacle to primary prevention efforts that target SCD risk stratification among the general population. 12, 13 Multiple population-based studies have demonstrated independent associations between specific risk factors and biomarkers of inflammation, myocyte injury, and neurohormonal activation with risk for SCD. 2, 14–16 However, there is no widely accepted model of individual risk estimation for SCD.…”

Section: Introductionmentioning

confidence: 99%

“…2, 14–16 However, there is no widely accepted model of individual risk estimation for SCD. 12, 13, 17 We sought to develop and evaluate a SCD prediction model among US adults without a history of cardiovascular disease (CVD). As part of this approach, we evaluated a comprehensive panel of traditional and novel cardiovascular risk factors in two large, bi-racial, population-based cohorts.…”

Section: Introductionmentioning

confidence: 99%

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Development and Validation of a Sudden Cardiac Death Prediction Model for the General Population

et al. 2016

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Background Most sudden cardiac death (SCD) events occur in the general population among persons who do not have any prior history of clinical heart disease. We sought to develop a predictive model of SCD among US adults. Methods We evaluated a series of demographic, clinical, laboratory, electrocardiographic, and echocardiographic measures in participants in the Atherosclerosis Risk in Communities (ARIC) Study (n=13,677) and the Cardiovascular Health Study (CHS) (n=4,207) who were free of baseline cardiovascular disease. Our initial objective was to derive a SCD prediction model using the ARIC cohort and validate it in CHS. Independent risk factors for SCD were first identified in the ARIC cohort to derive a 10-year risk model of SCD. We compared the prediction of SCD to non-SCD and all-cause mortality in both the derivation and validation cohorts. Further, we evaluated whether the SCD prediction equation was better at predicting SCD than the 2013 ACC/AHA CVD Pooled Cohort risk equation. Results There were a total of 345 adjudicated SCD events in our analyses, and the 12 independent risk factors in the ARIC study included age, male sex, African American race, current smoking, systolic blood pressure, use of antihypertensive medication, diabetes, serum potassium, serum albumin, HDL, estimated GFR, and QTc interval. Over a 10-year follow-up period, a model combining these risk factors showed good to excellent discrimination for SCD risk (C statistic 0.820 in ARIC and 0.745 in CHS). The SCD prediction model was slightly better in predicting SCD than the 2013 ACC/AHA Pooled Cohort risk equations (C statistic 0.808 in ARIC and 0.743 in CHS). Only the SCD prediction model, however, demonstrated similar and accurate prediction for SCD using both the original, uncalibrated score and the recalibrated equation. Finally, in the echocardiographic subcohort, a left ventricular ejection fraction <50% was present in only 1.1% of participants and did not enhance SCD prediction. Conclusions Our study is the first to derive and validate a generalizable risk score that provides well-calibrated, absolute risk estimates across different risk strata in an adult population of white and African American individuals without a clinical diagnosis of cardiovascular disease.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Development and Validation of a Sudden Cardiac Death Prediction Model for the General Population

et al. 2016

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“…There is compelling evidence on the association between cold weather and acute myocardial infarction, but less is known on the mode of death1415. Acute myocardial infarction and sudden cardiac death seem not to respond to mitigation efforts in unison13161718. For this reason, specific studies on the risk factors and mechanisms of SCD are particularly warranted13.…”

Section: Discussionmentioning

confidence: 99%

Cold spells and ischaemic sudden cardiac death: effect modification by prior diagnosis of ischaemic heart disease and cardioprotective medication

Ryti

Mäkikyrö

Antikainen

et al. 2017

Sci Rep

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Sudden cardiac death (SCD) is the leading cause of death. The current paradigm in SCD requires the presence of an abnormal myocardial substrate and an internal or external transient factor that triggers cardiac arrest. Based on prior mechanistic evidence, we hypothesized that an unusually cold weather event (a cold spell) could act as an external factor triggering SCD. We tested potential effect modification of prior diagnoses and select pharmacological agents disrupting pathological pathways between cold exposure and death. The home coordinates of 2572 autopsy-verified cases of ischaemic SCD aged ≥35 in the Province of Oulu, Finland, were linked to 51 years of home-specific weather data. Based on conditional logistic regression, an increased risk of ischaemic SCD associated with a cold spell preceding death (OR 1.49; 95% CI: 1.06–2.09). Cases without a prior diagnosis of ischaemic heart disease seemed more susceptible to the effects of cold spells (OR 1.70; 95% CI: 1.13–2.56) than cases who had been diagnosed during lifetime (OR 1.14; 95% CI: 0.61–2.10). The use of aspirin, β-blockers, and/or nitrates, independently and in combinations decreased the risk of ischaemic SCD during cold spells. The findings open up new lines of research in mitigating the adverse health effects of weather.

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“…Deyell, Krahn and Goldberger take on the challenge of integrating the outcomes of population studies and information regarding arrhythmia mechanisms to understand risk stratification and prediction of SCD/SCA. 4 Their review focuses on existing and novel risk stratification tools for SCD due to VT/VF. In patients with structural heart disease, they evaluate the role of advances in imaging, assessment of autonomic tone and measures of repolarization in refining risk.…”

Section: Scope Of the Problem – Epidemiology Risk Stratificationmentioning

confidence: 99%

Introduction to a Compendium on Sudden Cardiac Death

Tomaselli

2015

Circulation Research

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Sudden Cardiac Death Risk Stratification

Cited by 106 publications

References 120 publications

Development and Validation of a Sudden Cardiac Death Prediction Model for the General Population

Development and Validation of a Sudden Cardiac Death Prediction Model for the General Population

Cold spells and ischaemic sudden cardiac death: effect modification by prior diagnosis of ischaemic heart disease and cardioprotective medication

Introduction to a Compendium on Sudden Cardiac Death

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