Profiling missing data in electronic health records for diabetes care research

分析电子健康记录中缺失的数据以进行糖尿病护理研究

• 批准号: 9169147
• 负责人: Yajuan Si
• 金额: $ 22.31万
• 依托单位: UNIVERSITY OF WISCONSIN-MADISON
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-01 至 2018-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9169147
• 关键词: Accounting; Adverse event; Affect; Algorithms; American; Bayesian Modeling; Caring; Characteristics; Chronic; Clinical; Clinical Research; Clinical Trials; Clinical effectiveness; Complex; Computer software; Computerized Medical Record; Data; Data Quality; Data Set; Data Sources; Databases; Dependency; Development; Diabetes Mellitus; Documentation; Electronic Health Record; Evidence based treatment; Exclusion; Glycosylated Hemoglobin; Glycosylated hemoglobin A; Guidelines; Health; Health Personnel; Incentives; Investigation; Joints; Knowledge; Laboratories; Lead; Longitudinal Studies; Measurement; Measures; Mediator of activation protein; Medical Records; Methodology; Methods; Modeling; Morbidity - disease rate; Outcome; Pathway interactions; Patient Care; Patient-Centered Care; Patients; Pattern; Pharmaceutical Preparations; Physicians; Process; Property; Quality of Care; Research; Risk; Role; Source; Statistical Models; Structure; System; Testing; Time; Uncertainty; Visit; Work; abstracting; adverse outcome; base; care seeking; comparative effectiveness; effectiveness research; evidence base; flexibility; glycemic control; health care delivery; health care quality; improved; indexing; individual patient; individualized medicine; mortality; novel; open source; patient oriented; treatment planning; usability

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.

抽象的 当前的糖尿病护理指南建议对复杂患者的个性化治疗计划，因为 控制糖基化血红蛋白（A1C）可能不合适。但是，几乎没有证据支持 以患者为中心的决定。电子健康记录（EHRS）为临床证据提供了重要的来源 关于改善糖尿病护理，但遭受可用性缺陷。特别是实验室措施和生命体征 在不规则的访问模式可能有关患者的信息方面有间歇性缺失值 潜在的药物状况。由于连锁误差，患者特征也不完整。我们的目标 将EHR中的缺失值算并提高数据质量以加强糖尿病的证据基础 指南。拟议的工作是通过正在进行的临床研究进行的，以检查患者的作用 使用预先存在的紧密A1C控制与不良事件风险之间关系的复杂性 在2003 - 2011年期间，由UW Health护理的8,304例糖尿病患者的EHR数据集。我们提出了贝叶斯人 在多个插入下的潜在剖面模型来解释潜在的不可忽视的访问过程， 促进建模混合类型的大量EHR变量并开发可扩展的计算 算法。具体而言，首先，我们通过共同建模A1C值，患者特征和 健康结果。其次，我们通过多种模式索引概括了潜在轮廓，并结合了 具有间歇性缺失值的多个实验室测量和生命体征的轨迹 不完整的患者社会人口统计学。第三，我们发布开源计算软件并传播 医疗保健提供系统的新临床发现。调查结果将提高统计 纵向研究中缺失数据的方法论，增加了可用的兼容性 患者病历记录并加强证据基础，以支持现有的糖尿病指南。