High-resolution stratification of risk of sudden cardiac arrest (SCA) in individual patients is a tool that is necessary for achieving effective and efficient application of data generated by population-based research. This concept is at the core of initiatives for merging cost effectiveness with maximized clinical efficiency and individual patient treatment.
For this review, we analyzed data on sudden cardiac death and SCA available from population studies that included large longitudinal and cross-sectional databases, observational cohort studies, and randomized clinical trials. In the context of population science, we treated clinical trials as small, scientifically rigid population studies that generate outcomes focused on defined segments of the population. Application of probabilistic outcomes from these available sources to individual patients generally and patients at risk for SCA and sudden cardiac death in particular is limited by the diversity of the study population based on inclusion criteria and/or the absence of uniformly large effect sizes. Limited information is available on the requirements for defining small high-risk density subgroups that would lead to identification of individuals at a sufficiently high probability of SCA to have a significant effect on clinical decision making.
Conclusions and Relevance
Synthesis of available population and clinical science data demonstrates the limitations for prediction and prevention of SCA and sudden cardiac death and provides justification for a research mandate for improving risk prediction at the level of individual patients. This leads to suggested approaches to new data generation and required research funding to address this large public health burden.