开发和验证电子健康记录算法,以预测慢性阻塞性肺病的存在
Brian J Wells1, Amit K Saha2, Jill A Ohar3
1Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston Salem, NC, USA.
概括
一个电子健康记录算法准确地预测慢性阻塞性肺病 (COPD) 患者的呼吸道气流阻塞 (AFO). 这种工具有助于向螺旋计,改善诊断和治疗,以更好地管理COPD.
科学领域:
- 肺部医学 肺部医学
- 医疗信息学 医疗信息学
- 诊断算法 诊断算法
背景情况:
- 慢性阻塞性肺病 (COPD) 的诊断往往依赖于临床怀疑,缺乏 expiratory空气流阻塞 (AFO) 的螺旋测量确认.
- 这种诊断差距可能导致过度诊断,不必要的治疗和不足诊断,错过了及时干预的机会.
- 慢性阻塞性肺病 (GOLD) 全球倡议的指导方针倡导有针对性的病例发现策略.
研究的目的:
- 开发和验证使用电子健康记录 (EHR) 预测AFO的自动化算法.
- 为指导COPD风险患者的定向螺旋测试.
- 为了提高COPD诊断的准确性和效率.
主要方法:
- 对15065名接受肺功能检测的患者 (2016-2022) 的分析.
- 排除患有囊性纤维化或α-1抗素缺乏症等特定疾病的患者.
- 使用65个变量,分为培训和验证集的物流回归模型的开发,以根据EHR数据预测AFO.
主要成果:
- AFO的主要预测因素包括年龄较大,男性性别,较低的BMI,吸烟史,先前的COPD诊断和特定的医疗保健利用模式.
- 最终的算法在验证数据集中实现了接收器运行特征曲线 (AUC) 下的面积为0.82.
- 该模型表现出良好的校准,表明可靠的AFO预测.
结论:
- 一个基于EHR的算法成功地开发出来,可以使用例行收集的数据准确预测AFO.
- 该工具提供了一种实用的方法,用于识别那些可以从针对性COPD病例发现中受益的患者.
- 未来的工作包括外部验证和集成到临床工作流程中,以实现自动警报和提高肺功能测试率.
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