Risk factors for sudden cardiac death in heart failure: A systematic review and meta-analysis of risk factors derived from prediction models
Abstract
Background: Heart failure (HF) is a global health burden with high mortality. Sudden cardiac death (SCD) remains a major complication, highlighting the need for accurate risk prediction.
Methods: We conducted a systematic review and meta-analysis, guided by the Population, Intervention, Comparator, Outcome, Timing, and Setting framework, to identify risk factors for SCD in HF and assess prediction models. Searches across 8 databases yielded eligible studies. Data extraction, risk of bias assessment using the Prediction Model Risk of Bias Assessment Tool, and statistical analyses were performed.
Results: Twelve studies met inclusion criteria, with 8 included in the meta-analysis. New York Heart Association classification and left ventricular ejection fraction emerged as the most robust predictors of SCD. Additional significant factors included age, sex, ischemic etiology, diabetes, heart rate, sodium, potassium, creatinine, estimated glomerular filtration rate, and hemoglobin. Considerable heterogeneity was observed among studies.
Conclusion: New York Heart Association class and left ventricular ejection fraction are key predictors of SCD in HF, while demographic, etiological, and laboratory factors further refine risk assessment. Current models show limitations due to heterogeneity and lack of external validation. Future work should integrate refined predictors, treatment responses, and diverse populations to improve the accuracy and clinical utility of SCD risk…
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