Development of an algorithm to distinguish smoldering versus symptomatic multiple myeloma in claims-based data sets

Mark A. Fiala, James Dukeman, Sascha A. Tuchman, Matt Keller, Ravi Vij, Tanya M. Wildes

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

Purpose The distinction of patients with symptomatic multiple myeloma (MM) from those with smoldering MM poses a challenge for researchers who use administrative databases. Historically, researchers either have included all patients or used treatment receipt as the distinguishing factor; both methods have drawbacks. We present an algorithm for distinguishing between symptomatic and smoldering MM using ICD-9-CM (International Classification of Diseases, Ninth Revision, Clinical Modification) codes for the classic defining events of symptomatic MM commonly referred to as the CRAB criteria (hypercalcemia, renal impairment, anemia, and bone lesions). Patients and Methods SEER-Medicare-linked data from 4,187 patients with MM diagnosed between 2007 and 2011 were used for this analysis. Results Eighty-four percent had ICD-9-CM codes consistent with CRAB criteria, whereas only 57% received treatment. Overall survival of patients with symptomatic MM defined as receipt of treatment was 32.3 months versus 26.6 months for the overall population and 22.9 months for patients with symptomatic MM defined by CRAB criteria. Conceptually, removal of patients with smoldering MM should result in a reduction in overall survival; however, the cohort of patients who received treatment tended to be younger and healthier than the overall population, which could have skewed the results. Conclusion The algorithm we present resulted in a larger and more representative sample than classification by treatment status and reduced potential bias that could result from including all patients with smoldering MM in the analysis. Although this study was performed using the SEER-Medicare database, the methodology was broad enough that the algorithm could be extended to additional claims-based data sets with relative ease.

Original languageEnglish
Pages (from-to)1-8
Number of pages8
JournalJCO Clinical Cancer Informatics
Volume2017
Issue number1
DOIs
StatePublished - 2017

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