Survival Analysis with Electronic Health Record Data: Experiments with Chronic Kidney Disease

This paper presents a detailed survival analysis for chronic kidney disease (CKD). The analysis is based on the EHR data comprising almost two decades of clinical observations collected at New York-Presbyterian, a large hospital in New York City with one of the oldest electronic health records in the United States. Our survival analysis approach centers around Bayesian multiresolution hazard modeling, with an objective to capture the changing hazard of CKD over time, adjusted for patient clinical covariates and kidney-related laboratory tests. Special attention is paid to statistical issues common to all EHR data, such as cohort definition, missing data and censoring, variable selection, and potential for joint survival and longitudinal modeling, all of which are discussed alone and within the EHR CKD context.

本研究针对慢性肾脏病（Chronic Kidney Disease, CKD）开展了详尽的生存分析。本分析的数据来源于纽约长老会医院（New York-Presbyterian）收集的近二十年临床观测记录，该医院作为纽约市大型医疗机构，拥有全美历史最悠久的电子健康档案（Electronic Health Record, EHR）系统之一。本研究的生存分析方法以贝叶斯多分辨率风险模型为核心，旨在捕捉慢性肾脏病随时间推移的动态发病风险，并针对患者临床协变量与肾脏相关实验室检测指标进行校正。研究重点关注了电子健康档案数据普遍存在的各类统计学问题，包括队列定义、数据缺失与删失、变量选择，以及联合生存与纵向建模的潜在可行性，上述所有问题均单独展开探讨，并结合电子健康档案背景下的慢性肾脏病研究场景进行了整合分析。