Aziza Jamal-Allial, Todd Sponholtz, Shiva K Vojjala, Mark Paullin, Anahit Papazian, Biruk Eshete, Seyed Hamidreza Mahmoudpour, Patrice Verpillat, Daniel C Beachler
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引用次数: 0
摘要
背景：国家死亡指数（NDI）是美国死亡率数据的黄金标准，但存在时间滞后，而且可能需要大量操作。本验证研究用NDI评估了各种死亡率数据来源的准确性。方法：本验证研究是对2010年1月至2018年12月期间美国晚期癌症队列的二次分析，具有确定的NDI关联。死亡率数据来源、住院出院、退院、死亡主档案（DMF）、医疗保险和医疗补助服务中心（CMS）、利用管理数据（U.M.）和在线讣告数据与NDI进行比较。结果：40,692例患者中，25,761例（63.3%）的死亡日期为NDI；复合算法的敏感性为88.9% (95% CI = 88.5%, 89.3%)，特异性为89.1% （95% CI = 88.6%, 89.6%）。同时，阳性预测值（PPV）为93.4% (95% CI = 93.1%, 93.7%)，阴性预测值（NPV）为82.3% (95% CI = 81.7%, 82.9%)，在比较各个来源时，每个来源的PPV都很高，但敏感性有限。结论：复合算法是一种敏感和精确的死亡率测量方法，而单个数据库来源是准确的，但灵敏度有限。
Validation of Mortality Data Sources Compared to the National Death Index in the Healthcare Integrated Research Database.
Background: The National Death Index (NDI) is the gold standard for mortality data in the United States (US) but has a time lag and can be operationally intensive. This validation study assesses the accuracy of various mortality data sources with the NDI.
Methods: This validation study is a secondary analysis of an advanced cancer cohort in the US between January 2010 and December 2018, with an established NDI linkage. Mortality data sources, inpatient discharge, disenrollment, death master file (DMF), Center for Medicare and Medicaid Services (CMS), Utilization management data (U.M.), and online obituary data were compared to NDI.
Results: Among 40,692 patients, 25,761 (63.3%) had a death date using NDI; the composite algorithm had a sensitivity of 88.9% (95% CI = 88.5%, 89.3%), specificity was 89.1% (95% CI = 88.6%, 89.6%). At the same time, positive predictive value (PPV) was 93.4% (95% CI = 93.1%, 93.7%), negative predictive value (NPV) was 82.3% (95% CI = 81.7%, 82.9%), and when comparing each individual source, each had a high PPV but limited sensitivity.
Conclusion: The composite algorithm was demonstrated to be a sensitive and precise measure of mortality, while individual database sources were accurate but had limited sensitivity.
期刊介绍：
Pragmatic and Observational Research is an international, peer-reviewed, open-access journal that publishes data from studies designed to closely reflect medical interventions in real-world clinical practice, providing insights beyond classical randomized controlled trials (RCTs). While RCTs maximize internal validity for cause-and-effect relationships, they often represent only specific patient groups. This journal aims to complement such studies by providing data that better mirrors real-world patients and the usage of medicines, thus informing guidelines and enhancing the applicability of research findings across diverse patient populations encountered in everyday clinical practice.
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