ArticlePharmacoepidemiology and drug safety2023
Validation of algorithms to select patients with multiple myeloma and patients initiating myeloma treatment in the national Veterans Affairs Healthcare System.
Article in Pharmacoepidemiology and drug safety, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 11 citations in OpenAlex.
- More intensive therapy as more effective treatment for frail patients with multiple myeloma [corrected].Blood advances · 2023Trial
- Evolution of real-world frontline treatment patterns in multiple myeloma.Blood advances · 2026Article
- Quantifying the contribution of modifiable risk factors for progression of MGUS to multiple myeloma in a Veteran population.International journal of cancer · 2026Article
- Cumulative Excess Body Mass Index and MGUS Progression to Myeloma.JAMA network open · 2025Article
- Use of disease modifying anti-rheumatic drugs and risk of multiple myeloma in US Veterans with rheumatoid arthritis.BMC rheumatology · 2025Article
- Association between glucagon-like peptide-1 receptor agonist use and progression of monoclonal gammopathy of uncertain significance to multiple myeloma among patients with diabetes.JNCI cancer spectrum · 2024Article
- Natural Language Processing Algorithm to Extract Multiple Myeloma Stage From Oncology Notes in the Veterans Affairs Healthcare System.JCO clinical cancer informatics · 2024Article
- Developing Large Language Model-based Pipeline for Identification of Disease Diagnosis: A Case Study on Identifying Newly Diagnosed Multiple Myeloma and its Precursor Disease in Veterans Health Administration Electronic Health Records.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Article
- Natural Language Processing-Assisted Classification Models to Confirm Monoclonal Gammopathy of Undetermined Significance and Progression in Veterans' Electronic Health Records.JCO clinical cancer informatics · 2023Article
Corrections and comments
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Authors and funding
16 authors at 7 institutions in 1 country.
Funding
Abstract
backgroundWe 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.
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