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La, Jennifer; DuMontier, Clark; Hassan, Hamza; Abdallah, Maya; Edwards, Camille; Verma, Karina; Ferri, Grace; Dharne, Mayuri; Yildirim, Cenk; Corrigan, June; Gaziano, J. Michael; Do, Nhan V.; Brophy, Mary T.; Driver, Jane A.; Munshi, Nikhil C.; Fillmore, Nathanael R.

Validation of algorithms to select patients with multiple myeloma and patients initiating myeloma treatment in the national Veterans Affairs Healthcare System

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  • Medientyp: E-Artikel
  • Titel: Validation of algorithms to select patients with multiple myeloma and patients initiating myeloma treatment in the national Veterans Affairs Healthcare System
  • Beteiligte: La, Jennifer; DuMontier, Clark; Hassan, Hamza; Abdallah, Maya; Edwards, Camille; Verma, Karina; Ferri, Grace; Dharne, Mayuri; Yildirim, Cenk; Corrigan, June; Gaziano, J. Michael; Do, Nhan V.; Brophy, Mary T.; Driver, Jane A.; Munshi, Nikhil C.; Fillmore, Nathanael R.
  • Erschienen: Wiley, 2023
  • Erschienen in: Pharmacoepidemiology and Drug Safety, 32 (2023) 5, Seite 558-566
  • Sprache: Englisch
  • DOI: 10.1002/pds.5579
  • ISSN: 1053-8569; 1099-1557
  • Entstehung:
  • Anmerkungen:
  • Beschreibung: AbstractBackgroundWe aimed to evaluate and compare the performance of multiple myeloma (MM) selection algorithms for use in Veterans Affairs (VA) research.MethodsUsing 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).ResultsA 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).ConclusionOur VA Recommended Algorithm optimizes sensitivity and PPV for cohort selection and treatment classification.