Retrospective study of treatment patterns and outcomes post‐lenalidomide for multiple myeloma in Canada
Bibliographic record
Abstract
Lenalidomide is an important component of initial therapy in newly diagnosed multiple myeloma, either as maintenance therapy post-autologous stem cell transplantation (ASCT) or as first-line therapy with dexamethasone for patients' ineligible for ASCT (non-ASCT). This retrospective study investigated treatment patterns and outcomes for ASCT-eligible and -ineligible patients who relapsed after lenalidomide as part of first-line therapy, based on data from the Canadian Myeloma Research Group Database for patients treated between January 2007 and April 2019. Among 256 patients who progressed on lenalidomide maintenance therapy, 28.5% received further immunomodulatory derivative-based (IMiD-based) therapy (lenalidomide/pomalidomide) without a proteasome inhibitor (PI) (bortezomib/carfilzomib/ixazomib), 26.2% received PI-based therapy without an IMiD, 19.5% received both an IMiD plus PI, 13.5% received daratumumab-based regimens, and 12.1% underwent salvage ASCT. Median progression-free survival (PFS) was longest for daratumumab-based therapy (22.7 months) and salvage ASCT (23.4 months) and ranged from 6.6 to 7.3 months for the other treatments (P < .0001). Median overall survival (OS) was also longest for daratumumab and salvage ASCT. A total of 87 non-ASCT patients received subsequent therapy, with 66.7% receiving bortezomib-based therapy and 13.8% receiving other PI-based therapy. Median PFS was 15.4 and 24.8 months for bortezomib-based and other PI-based therapy, respectively (P = .404). During most of the study period, daratumumab was not funded; in this setting, switching to a different therapeutic class following relapse on lenalidomide produced the longest remissions for non-ASCT patients. Further prospective studies are warranted to determine optimum treatment following relapse on lenalidomide, especially in the light of increased access to daratumumab.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 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".