Journal: Nature medicine
This study develops and validates new risk models (PANGEA-SMM) to predict progression from smoldering multiple myeloma (SMM) to symptomatic multiple myeloma using longitudinal data rather than only static baseline features.
Researchers pooled data from 2,344 SMM patients across seven international centers, incorporating repeated clinical and laboratory measurements over time.
They identified four “evolving” biomarkers independently associated with shorter time to progression:
- Rise in M-protein ≥ 0.2 g/dl
- Increase in involved/uninvolved serum free light chain ratio ≥ 20
- Increase in creatinine > 25%
- Hemoglobin drop ≥ 1.5 g/dl
Using these dynamic changes, they built PANGEA-SMM models that more accurately predicted progression than commonly used risk tools (including 20/2/20 and IMWG models), achieving a C-statistic of 0.79.
Performance remained strong even when biomarker history or a recent bone marrow biopsy was unavailable (C-statistic 0.78 in each case).
The authors provide PANGEA-SMM as an open-access tool to support individualized risk stratification in SMM and offer validation tools so clinicians and researchers can compare it directly to existing models.