Development and Validation of an Algorithm to Identify Patients with Multiple Myeloma Using Administrative Claims Data

Abstract

PurposeThe objective was to expand on prior work by developing and validating a new algorithm to identify multiple myeloma (MM) patients in administrative claims.MethodsTwo files were constructed to select MM cases from MarketScan Oncology Electronic Medical Records (EMR) and controls from the MarketScan Primary Care EMR during January 1, 2000–March 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 (203.0×) 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.ConclusionThree 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.

Validating an algorithm for multiple myeloma based on administrative data using a SEER tumor registry and medical record review

“…Our finding that ICD‐9‐CM codes alone are not enough to accurately identify cases of MM is consistent with the work of Princic and colleagues, who found that a combination of multiple diagnoses and laboratory tests or a single diagnosis and chemotherapy was needed to accurately identify MM patients. All Princic algorithms first identified for evaluation potential index MM diagnosis codes as those preceded by >90 days of continuous enrolment and followed by >30 days of continuous enrollment.…”

“…During the first stage of the study, we developed, tested, and validated algorithms at HFHS. At the time of algorithm development, there were no published studies of validated algorithms to identify cases of MM in administrative databases (the two previously validated algorithms had not been published). Therefore, we developed algorithms based on previous work, National Comprehensive Cancer Network Guidelines for MM diagnosis, clinical input, and review of patient profiles (chronological summaries of administrative data consisting of diagnosis codes, procedures/tests, and other care received).…”

“…Few studies have validated or applied code‐based algorithms to identify hematologic malignancies. Of these studies, only two validated algorithms to identify MM cases . Princic and colleagues validated three algorithms in MarketScan claims (using MarketScan electronic medical records as the reference standard).…”

“…Of these studies, only two validated algorithms to identify MM cases . Princic and colleagues validated three algorithms in MarketScan claims (using MarketScan electronic medical records as the reference standard). The algorithms required either a combination of successive International Classification of Diseases (ICD), 9th Revision, Clinical Modification (ICD‐9‐CM) diagnosis codes for MM and diagnostic tests or a single ICD‐9‐CM diagnosis code for MM followed by chemotherapy.…”

Identifying multiple myeloma patients using data from the French health insurance databases

“…[33] However, literature concerning multiple myeloma is very poor, and only one resource could be identified. [34] According to this study, on MarketScan databases linked to Medicare claims databases, at least 2 diagnoses provided a sensitivity of 95%, a specificity of 73%, and a positive predictive value (PPV) of 76%.…”