Electronic health records (EHRs) are increasingly used for clinical and comparative effectiveness research but suffer from missing data. Motivated by health services research on diabetes care, we seek to increase the quality of EHRs by focusing on missing values of longitudinal glycosylated hemoglobin (A1c), a key risk factor for diabetes complications and adverse events. Under the framework of multiple imputation (MI), we propose an individualized Bayesian latent profiling approach to capture A1c measurement trajectories subject to missingness. The proposed method is applied to EHRs of adult patients with diabetes in a large academic Midwestern health system between 2003 and 2013 and had Medicare A and B coverage. We combine MI inferences to evaluate the association of A1c levels with the incidence of acute adverse health events and examine patient heterogeneity across identified patient profiles. We investigate different missingness mechanisms and perform imputation diagnostics. Our approach is computationally efficient and fits flexible models that provide useful clinical insights.

中文翻译：

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电子健康记录中缺失血红蛋白值的贝叶斯分析多重插补

电子健康记录 (EHR) 越来越多地用于临床和比较有效性研究，但存在数据缺失的问题。受糖尿病护理卫生服务研究的推动，我们寻求通过关注纵向糖化血红蛋白 (A1c) 的缺失值来提高 EHR 的质量，A1c 是糖尿病并发症和不良事件的关键风险因素。在多重插补 (MI) 的框架下，我们提出了一种个性化的贝叶斯潜在分析方法来捕获受缺失影响的 A1c 测量轨迹。所提出的方法应用于 2003 年至 2013 年间大型学术中西部卫生系统中成年糖尿病患者的 EHR，并具有医疗保险 A 和 B 覆盖范围。我们结合 MI 推论来评估 A1c 水平与急性不良健康事件发生率的关联，并检查已识别患者概况中的患者异质性。我们调查了不同的缺失机制并进行了插补诊断。我们的方法计算效率高，适用于提供有用临床见解的灵活模型。