开发和验证一个简单的模型来预测患者的身高
Emily E Moin1,2, Nicholas J Seewald3, Scott D Halpern1,2,3
1Division of Pulmonary, Allergy, and Critical Care, University of Pennsylvania, Philadelphia.
medRxiv : the preprint server for health sciences
|March 31, 2025
概括
准确的身高预测对于健康评估至关重要. 这项研究开发了一种使用自我报告的身高和年龄的验证模型,以提高人口健康数据的准确性.
科学领域:
- 生物统计学 生物统计学
- 流行病学 流行病学
- 医疗信息学 医疗信息学
背景情况:
- 电子健康记录 (EHR) 经常使用身高进行诊断和BMI计算.
- 成年人经常高估自己的身高,导致EHR数据不准确.
- 现有的高度预测的统计模型需要大量的输入,缺乏外部验证.
研究的目的:
- 开发和验证检查员测量的身高的性别分层预测模型.
- 提高人口健康研究中高度数据的准确性.
- 创建具有最小输入要求和证明外部有效性的模型.
主要方法:
- 利用国家健康和营养检查调查 (NHANES) 进行模型开发 (90%的样本).
- 在10%的NHANES样本中对模型进行了内部验证.
- 在两个独立的队列中对模型进行了外部验证:添加健康和HRS.
- 评估模型性能,使用C指数进行区分,校准图表和RMSE进行准确性.
主要成果:
- 在62,032名NHANES受试者中接受培训的模型在验证队列中显示出优异的歧视 (C指数0.88-0.89).
- 模型的校准很好,与自我报告的身高相比,它们的RMSE较低.
- 高度预测的准确性在45岁及以上的参与者中显著提高,跨越了各种人口统计数据.
结论:
- 使用自我报告的身高和年龄的简单预测模型显著改善了身高估计.
- 这种模型提供了比自我报告的高度更好的准确性,最小的输入.
- 它是第一个增强高度估计的模型,在各种人群中证明了外部有效性.
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