在加利福尼亚州两个大型卫生系统中开发和验证预测算法,以识别结核病
Heidi Fischer1, Lei Qian2, Zhuoxin Li2
1Department of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, CA, USA. Heidi.Fischer@kp.org.
Nature communications
|April 9, 2025
概括
加利福尼亚州加利福尼亚州加利福尼亚州.
科学领域:
- 公共卫生 公共卫生
- 流行病学 流行病学
- 临床信息学 临床信息学
背景情况:
- 加利福尼亚州的潜在结核病查没有有效地预防结核病的进展.
- 电子健康记录 (EHR) 为改善结核病风险评估提供了一个潜在的数据来源.
研究的目的:
- 使用EHR数据开发和验证结核病的临床风险预测模型.
- 将预测模型的效率与当前的结核病查方法进行比较.
主要方法:
- 利用了考克斯对EHR数据的比例危险回归,这些数据来自Kaiser Permanente南加州和北加州 (2008-2019).
- 纳入了一个模拟的结核病结果,以解释查预防的病例.
- 使用Harrell的C统计学来评估模型性能,并比较灵敏度和所需的屏幕数 (NNS).
主要成果:
- 最终的预测模型实现了0.816.8的C统计值.
- 基于模型的查确定了高风险个体,敏感度为70%,NNS为662.
- 目前的查方法显示36%的灵敏度,NNS为1632.
结论:
- 开发的临床风险预测模型显著提高了加利福尼亚州结核病查效率.
- 这种基于EHR的方法提供了一种更有效的策略,用于识别患结核病高风险的个人.
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