PS1376 LENALIDOMIDE MAINTENANCE POSITIVELY IMPACTS OUTCOMES IN MULTIPLE MYELOMA WITHOUT NEGATIVE IMPACTS IN RELAPSE: AN ANALYSIS OF REAL WORLD DATA FROM THE MYELOMA CANADA RESEARCH NETWORK NATIONAL DATABASE
Bibliographic record
Abstract
Background: Randomized trials have demonstrated the positive impact of lenalidomide maintenance (LM) on outcomes following autologous stem cell transplant (ASCT) in multiple myeloma (MM). This finding has led to its wide‐spread adoption as a standard of care. However, data examining its effect in the real‐world setting is lacking. Aims: Using the Myeloma Canada Research Network Canadian Multiple Myeloma Database (MCRN CMM‐DB) we have examined the impact of LM at a national level. Methods: We retrospectively reviewed data from patients tracked using the MCRN CMM‐DB. This web‐based centralized platform characterizes real‐world outcomes in patients treated at 13 major Canadian academic institutionsand includes comprehensive data on >6000 patients dating back to 2007 with ongoing prospective data collection. This analysis examined patients treated with bortezomib‐based induction therapy prior to ASCT. Subjects were included up until January 2016 to ensure at least 2 years of follow‐up. The analysis was performed based on intention‐to‐treat with LM. Overall survival (OS) was defined as time from pre‐ASCT induction therapy to death or last follow‐up (F/U). Progression‐free survival (PFS) was defined as the time from induction therapy to progression, death or last F/U. We also calculated outcomes of relapsing patients in both arms, including 2nd PFS (from second‐line therapy to second relapse, death or last F/U) and PFS2 (time from induction therapy to second relapse, death or last F/U). Lastly, given regional variation in LM dosing schedules, we examined outcomes based on a 21/28 days or continuous 28/28 days dosing strategy. Results: Data from 1,256 patients across 10 Canadian centers was included (723 with LM and 533 without). Median follow‐up in the LM group was 49 months and 45 months in the non‐LM group. The median OS was not reached in the LM cohort and 98 months in the non‐LM group (p <0.0001, figure 1a). The median PFS also favoured patients treated with LM (58 months versus 35 months respectively, p <0.0001, figure 1b). Response rates were high with 98% achieving ≥PR in the LM group compared to 96% in the non‐LM group (p = 0.06). In LM patients 94% achieved ≥VGPR compared to 81% in the non‐LM group (p <0.01). At the time of analysis, 45% of LM and 63% of non‐LM patients have relapsed. Treatment for relapsed disease resulted in a similar median 2nd PFS between LM vs non‐LM patients (12 months vs 16 months respectively, p = 0.07, figure 1c). The median PFS2 remained in favour of LM compared with non‐LM (not reached vs 68 months, p < 0.01, figure 1d). Median duration of maintenance therapy was 20.5 months (0.0 ‐ 102.48+). Analysis of patients treated with a 28/28 days (n = 356) versus a 21/28 (n = 257) days LM dosing schedule showed no difference in estimated 5‐year OS (81% and 80% respectively, p = 0.66) or 5‐year PFS (47% and 52% respectively, p = 0.75). Summary/Conclusion: Using the MCRN CMM‐DB we present one of the largest real‐world cohorts demonstrating the dramatic impact on outcomes using LM following bortezomib‐based induction. Notable improvements are seen in PFS, OS and depth of response. The truncated 21/28 day dosing strategy did not negatively impact these endpoints. Importantly, LM did not demonstrate a negative effect on the outcomes with second‐line therapy, as the median 2nd PFS was similar and the PFS2 remained in favour of LM. Overall, this large real‐world cohort supports the ongoing use of LM in the frontline management of MM. image
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".