Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), Boston, Massachusetts, USA.
VA Boston CSP Center, Boston, Massachusetts, USA.
Pharmacoepidemiol Drug Saf. 2023 May;32(5):558-566. doi: 10.1002/pds.5579. Epub 2022 Dec 28.
We aimed to evaluate and compare the performance of multiple myeloma (MM) selection algorithms for use in Veterans Affairs (VA) research.
Using the VA Corporate Data Warehouse (CDW), the VA Cancer Registry (VACR), and VA pharmacy data, we randomly selected 500 patients from 01/01/1999 to 06/01/2021 who had (1) either one MM diagnostic code OR were listed in the VACR as having MM AND (2) at least one MM treatment code. A team reviewed oncology notes for each veteran to annotate details regarding MM diagnosis and initial treatment within VA. We evaluated inter-annotator agreement and compared the performance of four published algorithms (two developed and validated external to VA data and two used in VA data).
A total of 859 patients were reviewed to obtain 500 patients who were annotated as having MM and initiating MM treatment in VA. Agreement was high among annotators for all variables: MM diagnosis (98.3% agreement, Kappa = 0.93); initial treatment in VA (91.8% agreement; Kappa = 0.77); and initial treatment classification (87.6% agreement; Kappa = 0.86). VA Algorithms were more specific and had higher PPVs than non-VA algorithms for both MM diagnosis and initial treatment in VA. We developed the "VA Recommended Algorithm," which had the highest PPV among all algorithms in identifying patients diagnosed with MM (PPV = 0.98, 95% CI = 0.95-0.99) and in identifying patients who initiated their MM treatment in VA (PPV = 0.93, 95% CI = 0.90-0.96).
Our VA Recommended Algorithm optimizes sensitivity and PPV for cohort selection and treatment classification.
我们旨在评估和比较多发性骨髓瘤（MM）选择算法在退伍军人事务部（VA）研究中的性能。
使用 VA 公司数据仓库（CDW）、VA 癌症登记处（VACR）和 VA 药房数据，我们从 1999 年 1 月 1 日至 2021 年 6 月 1 日随机选择了 500 名患者，这些患者（1）要么有一个 MM 诊断代码，要么在 VACR 中列出患有 MM，并且（2）至少有一个 MM 治疗代码。一个团队审查了每位退伍军人的肿瘤学记录，以注释 VA 中 MM 诊断和初始治疗的详细信息。我们评估了注释者之间的一致性，并比较了四种已发表算法（两种在 VA 数据之外开发和验证，两种在 VA 数据中使用）的性能。
共审查了 859 名患者，以获得在 VA 中被注释为患有 MM 并开始 MM 治疗的 500 名患者。对于所有变量，注释者之间的一致性都很高：MM 诊断（98.3%的一致性，Kappa=0.93）；VA 中的初始治疗（91.8%的一致性；Kappa=0.77）；以及初始治疗分类（87.6%的一致性；Kappa=0.86）。VA 算法在 MM 诊断和 VA 中的初始治疗方面比非 VA 算法具有更高的特异性和更高的阳性预测值。我们开发了“VA 推荐算法”，该算法在识别被诊断患有 MM 的患者（PPV=0.98，95%CI=0.95-0.99）和识别在 VA 中开始 MM 治疗的患者方面具有所有算法中最高的阳性预测值（PPV=0.93，95%CI=0.90-0.96）。
我们的 VA 推荐算法优化了队列选择和治疗分类的敏感性和阳性预测值。
