Profiling missing data in electronic health records for diabetes care research

摘要 目前的糖尿病护理指南建议对复杂的患者制定个性化的治疗计划，因为 控制糖化血红蛋白(A1c)可能是不合适的。然而，几乎没有证据支持这一观点 以病人为中心的决策。电子健康记录(EHR)是临床证据的重要来源 改善糖尿病护理，但存在可用性不足的问题。尤其是实验室检查和生命体征 有间歇性的缺失值，其中不规则的访问模式可能是关于患者的信息 潜在的用药状况。由于连锁错误，患者特征也不完整。我们的目标是 补充EHR缺失值，提高数据质量，强化糖尿病证据基础 指导方针。拟议的工作是由正在进行的临床研究推动的，以检查患者的角色 A1c严格控制和不良事件风险之间关系的复杂性，使用预先存在的 2003-2011年间，UW Health护理的8,304名糖尿病患者的EHR数据集。我们提出了贝叶斯方法 多重归因下的潜在轮廓模型以解释潜在不可忽略的访问过程， 方便对大量混合类型的EHR变量进行建模，并开发可伸缩计算 算法。具体地说，首先，我们通过联合建模A1c值、患者特征和 健康结果。其次，我们用多个模式指数对潜在轮廓进行泛化，并结合 具有间歇性缺失值的多个实验室测量和生命体征的轨迹，以及解释 患者的社会人口统计数据不完整。第三，我们发布开源计算软件并发布 医疗保健提供系统的新临床发现。调查结果将推动统计 对纵向研究中缺失数据的方法学开发，增加可用数据的兼容性 患者病历和加强证据基础，以支持现有的糖尿病指南。

Abstract Current guidelines for diabetes care recommend individualized treatment plans for complex patients since tight control of glycosylated hemoglobin (A1c) may not be appropriate. However, little evidence exists to support the patient-centered decisions. Electronic health records (EHRs) provide an important source for clinical evidence on improving diabetes care, but suffer from usability deficiencies. Particularly the lab measures and vital signs have intermittent missing values where the irregular visit patterns may be informative about the patients' underlying medication status. Patient characteristics are also incomplete due to linkage error. We aim to impute the missing values in EHRs and improve the data quality to strengthen the evidence base for diabetes guidelines. The proposed work is motivated by ongoing clinical research to examine the role of patient complexity in the relationship between tight A1c control and the risk of adverse events, using a pre-existing EHR dataset of 8,304 patients with diabetes cared by the UW Health during 2003-2011. We propose Bayesian latent profile models under multiple imputation to account for the potentially non-ignorable visiting process, facilitate modeling a large number of EHR variables of mixed types and develop scalable computation algorithms. Specifically, first we build latent profiles by jointly modeling A1c values, patient characteristics and health outcomes. Second, we generalize the latent profiles by multiple pattern indices and combine the trajectories of multiple lab measures and vital signs with intermittent missing values, as well as accounting for incomplete patient sociodemographics. Third, we release open source computation software and disseminate new clinical findings to the healthcare delivery system. The investigation results will advance statistical methodology development for missing data in longitudinal studies, increase the compatibility of available patient medical records and strengthen the evidence base to support existing diabetes guidelines.