P08 TEMPORAL TRENDS IN PROGNOSIS OF PATIENTS WITH SMOLDERING MULTIPLE MYELOMA (SMM) WHO MEET CRITERIA FOR BIOMARKER-DEFINED EARLY MULTIPLE MYELOMA (SLIM CRAB POSITIVE MM)
Bibliographic record
Abstract
Background: Biomarker-defined pts with SMM were included in the diagnostic criteria of multiple myeloma (MM) in a 2014 update by the International Myeloma Working Group (IMWG). This category includes SMM pts with either ≥60% BMPC or a free light chain (FLC) ratio of ≥100 and an involved FLC concentration of ≥100 mg/dl or > 1 MRI-defined ≥5mm focal lesion. The main reason for revising the diagnostic criteria was the data available at that time, which showed a very short time to progression (TTP) to CRAB positive MM (between 9.2 and 15.3 mos), leading to the recommendation to initiate anti-MM therapy in these pts. Objective: To determine whether the prognosis (median time to progression □TTP□ and 2-year risk of progression) of biomarker-defined early MM or “SliM CRAB” positive MM pts has changed over the past decade compared with data previously available for the consensus group. Recent clinical experience suggests that a substantial proportion of pts meeting SliM CRAB MM criteria do not progress to MM within a short period of time. Methods: We performed a comprehensive literature search and meta-analysis, including studies listed in Embase and PubMed (01/01/2010 - 01/11/2022) on SliM CRAB positive pts, including digitizable progression curves that would allow generation of individualized data. We used WebPlotDigitizerTM to digitize published TTP curves and then applied the algorithm described by Guyot et al (2012) in R to obtain individualized patient outcomes. We generated Kaplan-Meier curves and forest plots using random-effects models from the digitized and published data and compared median TTP, 2-year risk of progression, and odds ratios (ORs) for the comparison of 2-year risk of progression between data published before and after the publication of the IMWG consensus. Results: We found 11 recent studies in addition to the six previously available studies with a total of 3482 pts. Our analysis showed longer TTP (median: 30.3 vs. 9.2 mos) and a reduction in 2-year risk of PD (45.5% vs. 86.2%) in pts with ≥60% BMPCs and in pts with a FLCratio ≥ 100 (48.1 vs. 15.3 mos and 31.6% vs. 73.0%, respectively) in more recent compared with earlier studies. Such analyses were not possible for pts with focal lesions defined by MRI because no further studies were published after 2014 (table 1). A meta-analysis using ORs for the 2-year risk of progression in pts with ≥ 60% BMPC showed a significantly higher OR in the two earlier (OR: 27.01, 95% CI 4.49-162.34, p=0.0003) compared to a later report (OR: 3.27, 95% CI 1.37-7.99, p=0.009). Testing for heterogeneity revealed that the two time periods differed significantly in their ORs (I2 =78.6%, p=0.009). Similar results were obtained for pts with a FLCratio ≥100 compared to those with a FLCratio < 100 in early (OR: 7.03, 95% CI 4.34-11.37, p < 0.0001) and recent publications (OR: 2.69, 95% CI 1.77-4.09, p < 0.0001), I2 = 67.8%, p = 0.005. Conclusions and Relevance: We found an approximately 3-fold longer TTP and 50% lower 2-year risk of progression in SMM patients with ≥60% BMPC or FLC ratio ≥ 100 in recent compared to earlier studies. This phenomenon is likely due to improved diagnostic workup with modern skeletal imaging and exclusion of patients with bone lesions. Therefore, routine treatment of patients meeting SliM criteria CRAB (BMPC or FLC ratio) should be initiated only after careful evaluation and documentation of signs of progression.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".