Sudden Cardiac Arrest (SCA): Prediction and Prevention

项目摘要：信息技术和生物技术的最新进展是一个机会 大幅改善医疗保健。然而，要利用数据的力量造福患者，有效的临床 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。 尽管在开发统计/机器学习方法方面取得了进展，但许多 将它们定制并转化为有用的临床决策支持工具仍然存在挑战。 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 对死亡的恐惧。SCA的临床研究对危险因素和多种不同类型的疾病进行重复测量 随着时间的推移发生的事件。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，并将它们应用于 SCA问题。首先，我们建议开发新的统计学习算法，以更好地预测 个人的多变量纵向数据，重点关注第一次和随后的SCA的风险。第二，我们 建议发展微随机化和 适时适应性干预试验 旨在减少 高危人群中SCA的行为危险因素。 我们建议开发的方法将适用于许多医学领域。然而，他们是 在这个项目中受到SCA的激励并应用于SCA。我们的团队在统计学方面有专长，包括因果推论， 纵向数据和生存分析，加上机器学习、流行病学、心脏病学和行为学 通过移动健康(MHealth)进行干预。这项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、纵向、 和多变量(SLAM)结果数据。 目标2：评估SCA的风险及其对动态、可修改和不可修改因素的依赖 在基于人群的和临床队列中。 目标3：规划并进行微随机化和即时自适应的可行性-可用性研究 干预试验旨在改变行为以降低SCA风险。 在成功实现这些目标后，我们将为医疗保健服务的进展做出贡献 通过将计算统计学应用于医学。好了！ 好了！

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. ! !