ArticleEClinicalMedicine2026
Temporal trends in progression risk in smoldering myeloma: a systematic review.
Article in EClinicalMedicine, 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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9 authors.
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Abstract
Background: Diagnostic advancements and classification updates appear to have reshaped the natural history of smoldering multiple myeloma (SMM), though this evolution has not been empirically quantified. We conducted a systematic review to evaluate temporal changes in SMM prognosis. Methods: PubMed, EMBASE and the Cochrane library databases were searched from January 1990 to November 2025, yielding 1415 reports, of which 14 studies met inclusion criteria. To reconstruct individual-level data, published time to progression (TTP) curves were digitized using a Shiny web application. Kaplan-Meier (KM) survival curves and meta-analyses were generated in RStudio. PROSPERO ID 1068697. Findings: Progression risk at two and ten years for all-risk patients was 22.8% and 60.1%, respectively; these increased to 44.7% and 85.6% in high-risk patients. Landmark analysis from year five revealed attenuated progression rates: 14.2% and 30.8% for all-risk, and 22.5% and 50.6% for high-risk patients at two and five years post-landmark, respectively. Studies enrolling most patients before 2014 showed higher progression rates than in more recent cohorts (Spearman's rho = 0.645, p = 0.034), but this trend did not reach statistical significance in high-risk groups (rho = 0.360, p = 0.272). Meta-analytic data further supported elevated progression in older cohorts. Interpretation: SMM appears to follow a more indolent trajectory in recent years, likely due to improved diagnostic precision and exclusion of biomarker-defined or subclinical MM. Enhanced predictive models may better guide therapeutic decision-making, targeting treatment to those most likely to benefit and avoiding the burden of unnecessary intervention in low-risk individuals. Funding: Supported by the Austrian Forum Against Cancer.
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