Outcomes after First Rescue Treatment in Patients with Relapsed or Refractory Multiple Myeloma in Colombia
Bibliographic record
Abstract
Abstract Background Proteasome inhibitors (PIs) are approved for treating newly diagnosed and relapsed multiple myeloma (MM) in Colombia. This propensity score matching (PSM) analysis using data from the real-world, was designed to establish the role PIs (bortezomib or carfilzomib) at first relapsed or refractory MM. The primary endpoint was overall response rate (ORR) and secondary endpoint included was overall survival (OS). Moreover, an analysis of OS was done regarding response attained. On Behalf of RENEHOC-GRIMMCO (Colombian Registry for Hemato-Oncological Diseases and Colombian Mieloma Múltiple study group). Methods PSM by nearest neighbor analysis to evaluate the role of PIs used at first relapse in multiple myeloma of patients belonging to RENEHOC registry, between 2010 and 2020. Results 390 patients were identified in the first relapse of the Colombian registry, 269 patients with PI and 121 patients without PI. One hundred and ten patients were included in each group after PSM. Patients were matched for age, ISS, extramedullary disease, and use of lenalidomide to define the influence of this immunomodulatory drug in the PI group. A difference was found in the use of lenalidomide because only 1 patient was treated with PI and lenalidomide concomitantly (0.91%) compared to 31 patients in the group without PI (28.18%), (p <0, 0001). Regarding ORR, no differences were found between the 2 groups 38.18% in PIs vs 37.27% in non-PIs group (p = 0.801). A trend towards better OS was found in the PIs group with a median of 58 months versus 39 months (p = 0.179). Overall survival in patients who achieved at least PR was better compared to those who did not reach 79 months versus 32 months in non-responders (p = 0.0001). Conclusion In this study, we found that the use of PI has a tendency to improve overall survival in real-world in MM patients when used in the first relapse and that this effect could possibly be enhanced with the combination with lenalidomide. Regardless of the treatment used, better responses are associated with better survival. Figure 1 Figure 1. Disclosures Abello: Janssen: Honoraria; Amgen: Honoraria; Dr Reddy's: Research Funding. Sossa: Amgen: Research Funding.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".