Predicting diabetes clinical outcomes using longitudinal risk factor trajectories
Predicting diabetes clinical outcomes using longitudinal risk factor trajectories
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DOI：
10.1186/s12911-019-1009-3
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发表时间：
2020-01-08
影响因子：
3.5
通讯作者：
Caraballo, Pedro J.
中科院分区：
文献类型：
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作者：
Simon, Gyorgy J.;Peterson, Kevin A.;Caraballo, Pedro J.
背景 电子健康记录 (EHR) 的普及为长期观察实验室结果和生命体征的轨迹提供了机会。本研究评估了电子健康记录中可用的危险因素轨迹对预测 2 型糖尿病事件的价值。研究设计和方法 分析基于 71,545 名成人非糖尿病患者的 13 年大型回顾性队列，基线时间为 2005 年，中位随访时间为 8 年。计算基线前三个时间段（2000-2001、2002-2003、2004）的空腹血糖、血脂、BMI 和血压的轨迹。开发了一种新方法“累积暴露 (CE)”，并使用 Cox 比例风险回归进行评估，以评估 2 型糖尿病发生的风险。我们使用弗雷明汉糖尿病风险评分 (FDRS) 模型作为对照。结果 新模型优于 FDRS 模型（0.802 vs.660；p 值
Background The ubiquity of electronic health records (EHR) offers an opportunity to observe trajectories of laboratory results and vital signs over long periods of time. This study assessed the value of risk factor trajectories available in the electronic health record to predict incident type 2 diabetes. Study design and methods Analysis was based on a large 13-year retrospective cohort of 71,545 adult, non-diabetic patients with baseline in 2005 and median follow-up time of 8 years. The trajectories of fasting plasma glucose, lipids, BMI and blood pressure were computed over three time frames (2000-2001, 2002-2003, 2004) before baseline. A novel method, Cumulative Exposure (CE), was developed and evaluated using Cox proportional hazards regression to assess risk of incident type 2 diabetes. We used the Framingham Diabetes Risk Scoring (FDRS) Model as control. Results The new model outperformed the FDRS Model (.802 vs .660; p-values