| Summary: | Purpose: The objective was to expand on prior work by developing and validating a new algorithm to identify multiple myeloma (MM) patients in administrative claims. Methods: Two files were constructed to select MM cases from MarketScan Oncology EMR and controls from the MarketScan Primary Care EMR during 1/1/2000-3/31/2014. Patients were linked to MarketScan claims databases and files were merged. Eligible cases were age >18, had a diagnosis and visit for MM in the Oncology EMR, and were continuously enrolled in claims for >90 days preceding and >30 days after diagnosis. Controls were age >18, had >12 months of overlap in claims enrollment (observation period) in the Primary Care EMR and >1 claim with an ICD-9-CM diagnosis code of MM (203.0x) during that time. Controls were excluded if they had chemotherapy; stem cell transplant; or text documentation of MM in the EMR during the observation period. A split sample was used to develop and validate algorithms. A maximum of 180 days prior to and following each MM diagnosis was used to identify events in the diagnostic process. Of 20 algorithms explored, the baseline algorithm of 2 MM diagnoses and the 3 best performing were validated. Values for sensitivity, specificity, and positive predictive value (PPV) were calculated. Conclusions: Three claims-based algorithms were validated with ~10% improvement in PPV (87%-94%) over prior work (81%) and the baseline algorithm (76%) and can be considered for future research. Consistent with prior work it was found that MM diagnoses before and after tests were needed.
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