比较宽泛和狭窄的表型算法:性能特征和不朽时间的差异
Joel N Swerdel1,2, Mitchell M Conover1,2
1Observational Health Data Analytics, Global Epidemiology, Janssen Research and Development, Titusville, NJ, United States.
概括
将第二个诊断代码添加到表型算法中增加了特异性,但也导致了不朽的时间. 这种权衡对于使用电子健康记录进行准确的观察性研究至关重要.
科学领域:
- 观察性的健康研究.
- 医疗信息学 医疗信息学
- 生物统计学 生物统计学
背景情况:
- 开发准确的表型算法用于观测研究往往涉及到敏感性和特异性之间的权衡.
- 现有的算法可能无法充分平衡这些性能特征,这可能会影响研究的有效性.
研究的目的:
- 为了评估旨在增强特异性的表型算法的性能特征.
- 量化与这些特异性增强算法相关的不朽时间量.
主要方法:
- 在五个数据库中检查了11种慢性健康状况的算法.
- 每个条件开发了五个算法:一个宽 (单个代码) 和四个窄 (需要在不同的时间窗口内进行第二个代码).
- 评估灵敏度,积极预测值 (PPV) 和相对于危险时间 (TAR) 的永生时间的比例.
主要成果:
- 与单个代码算法相比,狭窄算法显示了更高的PPV和更低的灵敏度.
- 增加第二个诊断代码的时间窗口提高了算法灵敏度.
- 第二个诊断代码的更大窗口增加了不朽时间,在较短的TAR分析中观察到的比例最高.
结论:
- 使用第二个诊断代码可以有效地提高健康状况算法的特异性.
- 这种方法引入了不朽的时间,代表了一个重要的权衡,必须在研究设计中考虑.
- 需要仔细考虑算法设计,以平衡特异性,尽量减少观察性研究中的不朽时间.
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