在医疗保健数据库中对基于代码的算法进行临床评估,以识别肺动脉高血压患者
Eva-Maria Didden1, Di Lu2, Andrew Hsi3
1Global Epidemiology, Rare Disease Epicenter, Actelion Pharmaceuticals Ltd Janssen Pharmaceutical Company of Johnson & Johnson Allschwil Switzerland.
Pulmonary circulation
|February 9, 2024
概括
使用行政代码识别肺动脉高血压 (PAH) 是一个挑战. 具有时间元素的多组件算法提高了研究数据库中PAH患者识别的准确性.
科学领域:
- 心脏病学 心脏病学
- 医疗信息学 医疗信息学
- 流行病学 流行病学
背景情况:
- 肺动脉高血压 (PAH) 是一种罕见的疾病,这使得研究变得困难.
- 行政数据库对于罕见疾病研究有价值,但缺乏用于PAH识别的验证算法.
- 准确识别PAH患者对于使用索赔和行政数据的研究至关重要.
研究的目的:
- 通过使用行政代码,评估各种用于识别PAH患者的算法的准确性.
- 将基于代码的算法性能与通过右心导管 (RHC) 确认的临床诊断进行比较.
主要方法:
- 评估了16个算法,包括6个已公布的算法,使用链接的电子健康记录和临床数据库.
- 包括720名肺高血压 (PH) 患者,其中558人确诊为PAH.
- 对RHC确认的诊断进行了算法敏感性和特异性的评估.
主要成果:
- 仅使用诊断代码的简单算法具有高灵敏度 (>97%) 但特异性低 (<12%).
- 结合诊断,治疗和入学期的多元组件算法显示出更好的灵敏度-特异性平衡.
- 最好的算法实现了~95%的灵敏度和~38%的特异性,错误分类的患者更少.
结论:
- 算法的复杂性和时间元件的包含显著影响了识别PAH患者的准确性.
- 研究人员应该根据他们的具体研究需求仔细选择或设计算法.
- 精确定义的时间元素对于强大的PAH患者识别算法至关重要.
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