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Development and Validation of a Sudden Cardiac Death Prediction Model for the General Population
Rajat Deo1, Faye L Norby2, Ronit Katz2
1From Section of Electrophysiology, Division of Cardiovascular Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia (R.D.); Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis (F.L.N., A.R.F.); Kidney Research Institute (R.K., B.K., R.A.K.), Division of Cardiology (N.S., K.K.P.), University of Washington, Seattle; Division of Cardiology, Veterans Affairs Medical Center, Minneapolis, MN (S.A.); Division of Cardiology, University of Maryland School of Medicine, Baltimore (C.R.D.); Division of Nephrology, University of Washington, Seattle (B.K.); Division of Cardiology, University of Minnesota Medical School, Minneapolis (L.Y.C., S.K.); Department of Epidemiology and Cardiovascular Health Research Unit, University of Washington, Seattle (S.R.H.); Department of Biostatistics (R.A.K.), The New York Academy of Medicine, New York, NY (D.S.); General Internal Medicine Section, Veterans Affairs Medical Center, San Francisco, CA, Departments of Medicine, Epidemiology and Biostatistics, University of California, San Francisco (M.G.S.); and Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA (A.A.). Rajat.Deo@uphs.upenn.edu.
Insights
A new predictive model identifies 12 risk factors for sudden cardiac death (SCD) in adults without heart disease. This model accurately predicts SCD risk over 10 years, improving upon existing cardiovascular risk equations.
Area of Science:
- Cardiology
- Preventive Medicine
- Epidemiology
Background:
- Sudden cardiac death (SCD) often occurs in individuals without a prior history of heart disease.
- Developing a predictive model for SCD in the general adult population is crucial for early intervention.
Purpose of the Study:
- To develop and validate a predictive model for 10-year SCD risk in US adults without diagnosed cardiovascular disease.
- To compare the performance of the new SCD prediction model against existing cardiovascular risk equations.
Main Methods:
- Utilized data from the Atherosclerosis Risk in Communities (ARIC) study for model derivation and the Cardiovascular Health Study (CHS) for validation.
- Identified 12 independent risk factors for SCD, including demographic, clinical, and laboratory measures.
- Compared SCD prediction accuracy with non-SCD and all-cause mortality, and with the 2013 ACC/AHA Pooled Cohort risk equation.
Main Results:
- A 10-year SCD risk model incorporating 12 factors demonstrated good to excellent discrimination (c-statistics 0.820 in ARIC, 0.745 in CHS).
- The developed SCD model slightly outperformed the 2013 ACC/AHA Pooled Cohort risk equations.
- Left ventricular ejection fraction did not significantly improve SCD prediction in the echocardiographic subcohort.
Conclusions:
- This study presents the first generalizable risk score for predicting SCD in adults without pre-existing cardiovascular disease.
- The model provides well-calibrated, absolute risk estimates across diverse populations.
- The validated risk score aids in identifying individuals at risk for sudden cardiac death.
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.
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