基于索赔的模型的开发和验证,以预测肥胖类别
American journal of epidemiology
|August 31, 2023
概括
这项研究创建了一个基于索赔的算法,使用电子健康记录和保险索赔数据将患者分为肥胖类别. 当身体质量指数 (BMI) 测量无法使用时,该模型可以准确识别肥胖症.
科学领域:
- 医疗信息学 医疗信息学
- 流行病学 流行病学
- 生物统计学 生物统计学
背景情况:
- 准确的肥胖分类对于公共卫生研究和临床实践至关重要.
- 电子健康记录 (EHR) 和保险索赔数据提供了大量的患者信息,但往往缺乏直接的体重指数 (BMI) 测量.
- 开发从索赔数据中推断BMI的方法对于大规模的肥胖研究至关重要.
研究的目的:
- 开发和验证基于声明的算法,用于将患者分类为肥胖类别 (BMI ≥25,≥30,≥40).
- 通过使用医疗保险和医疗补助申请数据,评估模型在识别不同水平的肥胖患者方面的表现.
- 在没有直接BMI测量的人群中进行肥胖研究.
主要方法:
- 使用的医疗保险和医疗补助索赔数据与马萨诸塞州波士顿的EHR系统相关.
- 使用规范回归来从137个候选项中选择预测变量.
- 建立并验证了通用线性模型,以根据BMI值对患者进行分类,使用一个EHR系统进行培训,另一个用于验证.
主要成果:
- 最终的模型包括97个 (医疗保险) 和95个 (医疗补助) 变量,包括诊断代码,药物和并发症.
- 经验证的模型性能显示,接收器运行特征曲线下的区域在不同的BMI类别和人群中从0.66到0.83不等.
- 积极的预测值在62.5%至81.6%之间,证明了该模型在从索赔数据中识别肥胖类别的实用性.
结论:
- 一个经过验证的基于索赔的算法可以有效地将患者分为肥胖类别,即使没有直接的BMI测量.
- 这种方法促进了大规模的流行病学研究,混调整和在有可用的索赔数据的人群中进行子组分析.
- 该模型为研究人员和公共卫生官员提供了一种有价值的工具,用于研究肥胖的流行率和影响.
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