在美国开发和验证子宫内膜癌算法 索赔数据 数据
Kimberly Daniels1, Cachet Wenziger1, Sampada Gandhi2
1Carelon Research, Wilmington, Delaware, USA.
Pharmacoepidemiology and drug safety
|January 21, 2026
概括
这项研究开发了使用ICD-9-CM和ICD-10-CM代码识别子宫内膜癌病例的准确算法. 这些算法表现出高的积极预测值和灵敏度,对于子宫内膜癌的检测具有最小的错误阳性.
科学领域:
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
- 流行病学 流行病学
背景情况:
- 准确识别子宫内膜癌病例对于流行病学研究和授权后安全评估至关重要.
- 现有的编码系统需要验证,以便在大型数据集中精确地确定案例.
研究的目的:
- 开发和验证使用ICD-9-CM和ICD-10-CM代码识别子宫内膜癌发病率的算法.
- 通过确保可靠的病例识别,支持对激素疗法的许可后安全性研究.
主要方法:
- 利用来自医疗保健综合研究数据库 (HIRD) 的国家索赔数据.
- 开发了基于诊断代码的查算法,并通过专家审查进行裁决.
- 计算的正预测值 (PPV) 和对算法性能的条件灵敏度.
主要成果:
- 使用两个ICD-9-CM代码 (182.0或182.8) 的算法实现了PPV的91.2%和99.3%的灵敏度.
- 使用两个ICD-10-CM代码 (C54.1,C54.8或C54.9) 的算法实现了PPV的97.0%和99.5%的灵敏度.
- 两种算法都显示了所有测试群体的PPV和灵敏度超过75%.
结论:
- 在ICD-9-CM和ICD-10-CM系统中,两种子宫内膜癌的诊断代码准确地识别了已确认的病例.
- 开发的算法表现出高精度和最小的假阳性对子宫内膜癌.
- 经过验证的算法适用于大规模的流行病学和安全研究.
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