Treatment-Specific Prediction Models in Multiple Myeloma: A Critical Review of Current Evidence and Future Directions

Multiple myeloma (MM) is characterized by substantial clinical heterogeneity, leading to wide variability in treatment response and toxicity. Although numerous prognostic tools exist, relatively few models estimate outcomes conditional on a specific therapeutic regimen. Treatment-specific prediction models are an important step toward individualized therapy selection. This review synthesizes the current landscape of treatment-specific clinical prediction models in MM.

A structured search of PubMed and Embase/Scopus identified multivariable clinical prediction models developed within a static treatment framework, evaluating treatment-specific therapeutic or toxicity-related outcomes in MM. Information was extracted on treatment regimens, predictors, modeling methods, validation strategies, and reporting of clinical utility.

Thirteen models were identified, evaluating therapeutic (n = 10) or toxicity-related (n = 3) outcomes across regimens including bortezomib-based induction, daratumumab-containing combinations, ixazomib-based triplets, and CAR-T therapy. Predictors were mainly routine clinical and laboratory variables, with limited integration of cytogenetics or patient-reported outcomes. Most models used traditional regression methods; calibration was inconsistently reported, and external validation was performed in seven studies. Decision curve analysis was included in only two models.

Methodological and translational gaps remain, including limited transparency, scarce external validation, and lack of patient-reported or longitudinal predictors. None of the models have been implemented as online calculators or integrated into electronic decision-support systems, limiting real-world uptake. Addressing these gaps is essential for developing clinically meaningful prediction tools to support personalized treatment in MM.