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John L Gore¹, Phoebe Wright², Vanessa Shih²

¹University of Washington, Seattle, WA.

JCO clinical cancer informatics

|September 19, 2024

概括

一个新的算法使用医疗保健索赔数据准确地将膀癌 (BC) 患者分为非肌肉侵入性 (NMIBC),肌肉侵入性 (MIBC) 和高级阶段. 该工具有助于研究人员为基于行政索赔的研究制定BC阶段.

科学领域:

在瘤学瘤学.
医疗信息学医疗信息学
生物统计学生物统计学

背景情况:

疾病分期对于癌症研究至关重要,特别是在基于行政索赔的研究中.
索赔数据库往往缺乏全面的疾病阶段信息,需要算法方法.
在美国医疗保健索赔数据中,尚未开发出先前的算法来根据病期对膀癌 (BC) 进行分类.

研究的目的:

开发和验证基于索赔的算法,用于诊断膀癌 (BC) 患者的病期.
将BC分为非肌肉侵入性BC (NMIBC),肌肉侵入性BC (MIBC) 和局部晚期/转移性尿路细胞癌 (la/mUC).

主要方法:

使用SEER-Medicare相关数据开发了一个基于索赔的算法.
该算法与SEER注册表进行了验证,并进行了代参数改进.
通过使用协议,科恩卡帕 (κ),特异性,正预测值 (PPV) 和负预测值 (NPV) 来评估绩效.

主要成果:

该研究包括15484名BC患者:71.0%的NMIBC,23.5%的MIBC和5.5%的la/mUC.
优化的算法与SEER数据达成了82.5%的一致性 (κ = 0.58).
对于NMIBC (87.0%) 和MIBC (76.8%),观察到高的PPV,对于la/mUC (98.0%) 的NPV非常好.

结论:

开发的基于索赔的算法是研究中的膀癌患者分期的可行工具.
这种算法可以促进更准确的基于行政索赔的膀癌研究.

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