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Clinical Trial
. 2016 Sep 13;134(11):806-16.
doi: 10.1161/CIRCULATIONAHA.116.023042. Epub 2016 Aug 19.

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

Rajat Deo  1 , Faye L Norby  2 , Ronit Katz  2 , Nona Sotoodehnia  2 , Selcuk Adabag  2 , Christopher R DeFilippi  2 , Bryan Kestenbaum  2 , Lin Y Chen  2 , Susan R Heckbert  2 , Aaron R Folsom  2 , Richard A Kronmal  2 , Suma Konety  2 , Kristen K Patton  2 , David Siscovick  2 , Michael G Shlipak  2 , Alvaro Alonso  2
Affiliations
Clinical Trial

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

Rajat Deo et al. Circulation. .

Abstract

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 ARIC study (Atherosclerosis Risk in Communities) (n=13 677) and the CHS (Cardiovascular Health Study) (n=4207) 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 with non-SCD and all-cause mortality in both the derivation and validation cohorts. Furthermore, we evaluated whether the SCD prediction equation was better at predicting SCD than the 2013 American College of Cardiology/American Heart Association Cardiovascular Disease 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, black race, current smoking, systolic blood pressure, use of antihypertensive medication, diabetes mellitus, serum potassium, serum albumin, high-density lipoprotein, estimated glomerular filtration rate, and QTc interval. During 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 American College of Cardiology/American Heart Association 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 black participants without a clinical diagnosis of cardiovascular disease.

Keywords: arrhythmia; population; risk prediction; sudden cardiac death.

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Figures

Figure 1
Figure 1
Study Overview
Figure 2A
Figure 2A. ARIC 10 year calibration plot using the SCD prediction model
The x-axis refers to deciles of predicted SCD risk. Each bar in the graph represents the average observed and predicted SCD risk.
Figure 2B
Figure 2B. CHS 10 year calibration plot using the SCD prediction model
The x-axis refers to deciles of predicted SCD risk. Each bar in the graph represents the average observed and predicted SCD risk.
Figure 3
Figure 3
SCD Prediction stratified by Sex and Race
See this image and copyright information in PMC

Comment in

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