Division of Cardiology, University of Miami Miller School of Medicine, Miami, Florida.
JAMA Cardiol. 2017 Jun 1;2(6):689-694. doi: 10.1001/jamacardio.2017.0266.
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.
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.
对个体发生心搏骤停 (SCA) 的风险进行高分辨率分层是将基于人群的研究产生的数据有效且高效应用的必要工具。这一概念是将成本效益与最大化临床效率和个体患者治疗相结合的举措的核心。
在这项综述中,我们分析了来自人群研究的关于心源性猝死和 SCA 的数据,这些研究包括大型纵向和横断面数据库、观察性队列研究和随机临床试验。在人群科学的背景下,我们将临床试验视为小型、科学严格的人群研究,这些研究产生的结果集中在人群的特定部分。将这些可用来源的概率结果应用于个体患者,特别是 SCA 和心源性猝死风险患者,受到研究人群基于纳入标准的多样性和/或缺乏统一的大效应大小的限制。关于定义将导致足够高的 SCA 发生概率以对临床决策产生重大影响的个体的小高危密度亚组的要求的信息有限。
对现有人群和临床科学数据的综合分析表明,预测和预防 SCA 和心源性猝死存在局限性,并为提高个体患者风险预测的研究任务提供了依据。这导致了提出新的数据生成方法和所需研究资金的建议,以解决这一重大公共卫生负担。