REVEAL-MM: retrospective evaluation of variables in early assessment and landmark trends in multiple myeloma—a US claims-based case-control study

Abstract Multiple myeloma (MM) frequently presents with non-specific symptoms that overlap other common conditions, often leading to diagnostic delays and poorer clinical outcomes. Although diagnostic delays in MM are well recognized, the healthcare utilization patterns that precede MM diagnosis are not well defined. This retrospective, 1:1 matched case-control study used US administrative claims data from 9466 patients to identify early signals of MM and evaluate whether such data could be used to predict individuals at risk of MM prior to their formal diagnosis. All diagnostic, procedural, prescription, and physician-visit claims were evaluated at 12, 9, and 6 months within the two years before diagnosis. Interpretable predictive models (LASSO and Random Forest) identified distinct encounter patterns associated with MM as early as 12-months before diagnosis. Predictive performance across all models increased as diagnosis approached, with the machine learning model reaching a peak area under the curve (AUC) of 0.826 at 6 months. Claims consistent with typical MM manifestations, including anemia-, musculoskeletal-, and M-protein–related testing, were more common prior to MM diagnosis. These findings suggest that routinely collected claims data could support earlier identification and evaluation of individuals at risk of MM, enabling more timely diagnosis and improved outcomes.