Reinier Kyndaron, Chugh Harpriya S, Uy-Evanado Audrey, Heckard Elizabeth, Mathias Marco, Bosson Nichole, Calsavara Vinicius F, Slomka Piotr J, Elashoff David A, Bui Alex A T, Chugh Sumeet S
Center for Cardiac Arrest Prevention, Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center Los Angeles CA USA.
Los Angeles County EMS Agency Los Angeles CA USA.
Int J Cardiol Heart Vasc. 2025 Jan 19;56:101614. doi: 10.1016/j.ijcha.2025.101614. eCollection 2025 Feb.
Out-of-hospital sudden cardiac arrest (SCA) is a major cause of mortality and improved risk prediction is needed. The Observational Study of Sudden Cardiac Arrest Risk (OSCAR) is an electronic health records (EHR)-based cohort study of patients receiving routine medical care in the Cedars-Sinai Health System (CSHS) in Los Angeles County, CA designed to evaluate predictors of SCA. This paper describes the rationale, objectives, and study design for the OSCAR cohort.
The OSCAR cohort includes 379,833 Los Angeles County residents with at least one patient encounter at CSHS in each of two consecutive calendar years from 2016 to 2020. We obtained baseline cohort characteristics from the EHR from 2012 until the start of follow-up, including demographics, vital signs, clinical diagnoses, cardiac tests and imaging, procedures, laboratory results, and medications. Follow-up will continue until Dec. 31, 2025, with an expected median follow-up time of ∼ 7 years. The primary outcome is out-of-hospital SCA of likely cardiac etiology attended by Los Angeles County Emergency Medical Services (LAC-EMS). The secondary outcome is total mortality identified using California Department of Public Health - Vital Records death certificates. We will use conventional approaches (diagnosis code algorithms) and artificial intelligence (natural language processing, deep learning) to define patient phenotypes and biostatistical and machine learning approaches for analysis.
The OSCAR cohort will provide a large, diverse dataset and adjudicated SCA outcomes to facilitate the derivation and testing of risk prediction models for incident SCA.
院外心脏骤停（SCA）是死亡的主要原因，需要改进风险预测。心脏骤停风险观察研究（OSCAR）是一项基于电子健康记录（EHR）的队列研究，研究对象为加利福尼亚州洛杉矶县雪松西奈医疗系统（CSHS）接受常规医疗护理的患者，旨在评估SCA的预测因素。本文描述了OSCAR队列的基本原理、目标和研究设计。
OSCAR队列包括379,833名洛杉矶县居民，他们在2016年至2020年的连续两个日历年中，每年至少在CSHS有一次就诊经历。我们从2012年的EHR中获取了队列的基线特征，直至随访开始，包括人口统计学、生命体征、临床诊断、心脏检查和影像学、手术、实验室检查结果及用药情况。随访将持续至2025年12月31日，预计中位随访时间约为7年。主要结局是由洛杉矶县紧急医疗服务（LAC-EMS）救治的可能由心脏病因导致的院外SCA。次要结局是使用加利福尼亚州公共卫生部生命记录死亡证明确定的总死亡率。我们将使用传统方法（诊断编码算法）和人工智能（自然语言处理、深度学习）来定义患者表型，并采用生物统计学和机器学习方法进行分析。
OSCAR队列将提供一个大型、多样的数据集和经过判定的SCA结局，以促进对新发SCA风险预测模型的推导和测试。
