Background Diagnosis-code-based algorithms to identify fall injuries in Medicare data are useful for ascertaining outcomes in interventional and observational studies. However, these algorithms have not been validated against a fully external reference standard, in ICD-10-CM, or in Medicare Advantage (MA) data. Methods We linked self-reported fall injuries leading to medical attention (FIMA) from the Strategies to Reduce Injuries and Develop Confidence in Elders (STRIDE) trial (reference standard) to Medicare fee-for-service (FFS) and MA data from 2015-2019. We measured the area under the receiver operating characteristic curve (AUC) based on sensitivity and specificity of a diagnosis-code-based algorithm against the reference standard for presence or absence of ≥1 FIMA within a specified window of dates, varying the window size to obtain points on the curve. We stratified results by source (FFS versus MA), trial arm (intervention versus control), and STRIDE’s ten participating healthcare systems. Results Both reference standard data and Medicare data were available for 4941 (of 5451) participants. The reference standard and algorithm identified 2054 and 2067 FIMA, respectively. The algorithm had 45% sensitivity (95% confidence interval [CI], 43%-47%) and 99% specificity (95% CI, 99%-99%) to identify reference standard FIMA within the same calendar month. The AUC was 0.79 (95% CI, 0.78-0.81) and was similar by FFS or MA data source or trial arm, but showed variation among STRIDE healthcare systems (AUC range by healthcare system, 0.71 to 0.84). Conclusions An ICD-10-CM algorithm to identify fall injuries demonstrated acceptable performance against an external reference standard, in both MA and FFS data.

中文翻译：

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验证基于规则的 ICD-10-CM 算法以检测医疗保险数据中的跌倒伤害

背景 用于识别医疗保险数据中跌倒伤害的基于诊断代码的算法对于确定介入和观察研究的结果非常有用。然而，这些算法尚未根据 ICD-10-CM 或 Medicare Advantage (MA) 数据中的完全外部参考标准进行验证。方法 我们将减少伤害和增强老年人信心策略 (STRIDE) 试验（参考标准）中自我报告的导致医疗护理的跌倒伤害 (FIMA) 与 2015 年的医疗保险服务收费 (FFS) 和 MA 数据联系起来。 2019.我们根据基于诊断代码的算法的敏感性和特异性，根据指定日期窗口内存在或不存在 ≥1 FIMA 的参考标准，测量了受试者工作特征曲线 (AUC) 下的面积，将窗口大小改变为获取曲线上的点。我们按来源（FFS 与 MA）、试验组（干预与控制）以及 STRIDE 的 10 个参与医疗保健系统对结果进行分层。结果 4941 名（共 5451 名）参与者可以获得参考标准数据和医疗保险数据。参考标准和算法分别识别 2054 和 2067 FIMA。该算法在同一日历月内识别参考标准 FIMA 的灵敏度为 45%（95% 置信区间 [CI]，43%-47%）和 99% 特异性（95% CI，99%-99%）。 AUC 为 0.79（95% CI，0.78-0.81），与 FFS 或 MA 数据源或试验臂相似，但在 STRIDE 医疗保健系统之间表现出差异（按医疗保健系统划分的 AUC 范围为 0.71 至 0.84）。结论 用于识别跌倒伤害的 ICD-10-CM 算法在 MA 和 FFS 数据中均表现出与外部参考标准相比可接受的性能。