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为复杂患者制定个性化的治疗计划， 糖化血红蛋白（A1c）的控制可能不合适。然而，几乎没有证据支持 以病人为中心的决定。电子健康记录（EHR）为临床证据提供了重要来源 改善糖尿病护理，但存在可用性不足的问题。特别是实验室测量和生命体征 具有间歇性缺失值，其中不规则的访视模式可能提供有关患者 基础用药状态。由于链接错误，患者特征也不完整。我们的目标是 填补EHR中的缺失值，提高数据质量，以加强糖尿病的证据基础 指南拟议的工作是出于正在进行的临床研究，以检查病人的作用， 严格A1c控制与不良事件风险之间关系的复杂性，使用预先存在的 2003 - 2011年期间由UW Health护理的8，304名糖尿病患者的EHR数据集。我们提出贝叶斯 在多个插补下的潜在简档模型以解释潜在的不可验证的访问过程， 便于对大量混合类型的EHR变量进行建模，并开发可扩展的计算 算法具体来说，首先，我们通过联合建模A1c值，患者特征和 健康成果。其次，我们通过多个模式指数来概括潜在的轮廓，并将联合收割机 多个实验室测量和生命体征的轨迹，具有间歇性缺失值，以及解释 不完整的患者社会人口统计学资料。第三，我们发布开源计算软件， 新的临床发现，以医疗保健提供系统。调查结果将推进统计 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 患者的医疗记录，并加强证据基础，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.