ArticleHemaSphere2026
Dynamic risk stratification in smoldering multiple myeloma: Integrating evolving biomarkers with the 2/20/20 Model.
Article in HemaSphere, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
20 authors.
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Abstract
Smoldering multiple myeloma (SMM) is a heterogeneous precursor with variable risk of progression to multiple myeloma (MM). Current risk models rely on baseline features, limiting predictive accuracy. We aimed to define evolving risk factors and integrate them with the 2/20/20 model to improve risk stratification. We analyzed 323 SMM patients diagnosed per IMWG 2014 criteria at Memorial Sloan Kettering Cancer Center (2002-2019). Serial M-protein and free light chain-ratio (FLCr) measurements were assessed during the first year following diagnosis. Using a multivariable Cox proportional hazards model adjusted for baseline 2/20/20 risk, age, and sex, we defined optimal cut-offs for evolving M-protein (eMP) as an increase of ≥0.4 g/dL and evolving FLCr (eFLCr) as an increase of ≥40% within the first year. Both eMP and eFLCr were independently associated with progression to MM (hazard ratio [HR] 2.8, 95% confidence interval [CI] 1.5-5.5; and HR 2.9, 95% CI 1.8-4.8). Median time to progression was 18 months for eMP and 41 months for eFLCr, compared to 130 versus 201 months without eMP and eFLCr, respectively. Integrating evolving biomarkers into the 2/20/20 model improved performance (c-index 0.79 vs. 0.70). Patients with both high baseline risk and evolving markers had 2- and 5-year progression rates of 41% and 83%, respectively, while low-risk patients without evolving disease exhibited a stable course resembling monoclonal gammopathy of undetermined significance (MGUS) with a 5-year progression rate of 8%. These findings support incorporating dynamic biomarkers into existing models to improve risk stratification and inform clinical decision-making in SMM.
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