Sudden Cardiac Arrest (SCA): Prediction and Prevention

项目概述：信息技术和生物技术的最新进展是一个机会， 大大改善医疗保健。然而，为了利用数据的力量使患者受益， 必须设计、测试和实施决策支持工具和新颖的个性化干预措施。 虽然统计/机器学习方法的发展取得了进展，但许多 但仍存在着将其定制并转化为有用的临床决策支持工具的挑战。 心脏骤停（SCA）占所有成人死亡的15 - 20%，是工业世界的主要原因 死亡之SCA的临床研究对危险因素和多种不同类型的 随着时间的推移。我们将这些数据称为 生存、纵向和多变量（SLAM）数据。 在这个项目中， 我们将为SLAM数据开发新的统计学习方法，并将其应用于两个不同的方面。 SCA问题。首先，我们建议开发新的统计学习算法，更好地预测 个体的多变量纵向数据，重点关注首次和后续SCA的风险。二是 建议发展微随机化， 适时适应性干预试验 设计减少 高危人群中SCA的行为危险因素。 我们提出的方法将适用于医学的许多领域。但他们 在这个项目中受到SCA的启发并应用于SCA。我们的团队拥有统计学方面的专业知识，包括因果推断， 纵向数据和生存分析，加上机器学习，流行病学，心脏病学和行为 通过移动的保健采取干预措施。这一拟议的合作有以下具体目标： 目标1：开发和测试统计学习工具，用于实时预测生存风险，纵向， 和多变量（SLAM）结果数据。 目标2：估计SCA的风险及其对动态可修改和不可修改因素的依赖性 在人群和临床队列中。 目标3：计划并执行微随机化和即时自适应的可行性-可用性研究 干预试验设计，以改变行为，降低SCA风险。 在成功完成这些目标后，我们将为医疗服务的进步做出贡献 将计算统计学应用于医学。! !

Project Summary: The recent advances in information technologies and biotechnologies is an opportunity to substantially improve healthcare. To exploit the power of data to benefit patients, however, effective clinical decision support tools and novel, individualized interventions must be designed, tested, and implemented. Although there has been progress in the development of statistical/machine learning methods, numerous challenges remain to tailor and translate them into useful clinical decision support tools. Sudden cardiac arrest (SCA) accounts for 15-20% of all adult deaths and is the industrial world’s leading cause of death. Clinical studies of SCA produce repeated measures on risk factors and multiple different kinds of events over time. We refer to these data as survival, longitudinal, and multivariate (SLAM) data. In this project, we will develop novel statistical learning methods for SLAM data and apply them to two distinct aspects of the SCA problem. First, we propose to develop novel statistical learning algorithms that better predict an individual’s multivariate longitudinal data with a focus on the risk of first and subsequent SCA. Second, we propose to develop micro-randomization and just-in-time adaptive intervention trial designs to reduce behavioral risk factors for SCA among persons at high risk. The methods that we propose to develop will be applicable in many areas of medicine. However, they are motivated by and applied to SCA in this project. Our team has expertise in statistics including causal inference, longitudinal data and survival analyses, plus machine learning, epidemiology, cardiology, and behavioral interventions through mobile health (mHealth). This proposed collaboration has the following specific aims: Aim 1: Develop and test statistical learning tools for real-time risk prediction of survival, longitudinal, and multivariate (SLAM) outcome data. Aim 2: Estimate the risk of SCA and its dependence on dynamic modifiable and non-modifiable factors in population-based and clinical cohorts. Aim 3: Plan and conduct a feasibility-usability study of micro-randomization and just-in-time adaptive intervention trial designs for behavioral change to reduce SCA risk. Upon successful completion of these aims, we will have contributed to the progress of healthcare delivery through the application of computational statistics to medicine. ! !