Impact of Cytogenetic Abnormalities, Induction and Maintenance Regimens on Outcomes After High-Dose Chemotherapy and Autologous Stem Cell Transplantation in Patients With Newly Diagnosed Multiple Myeloma: A Decade-Long Real-World Experience
Bibliographic record
Abstract
Background: High-dose chemotherapy and autologous stem cell transplant (HDT-ASCT) has become a standard of care for transplant eligible newly diagnosed multiple myeloma (NDMM) patients. While cytogenetic abnormalities have been shown to affect outcomes after HDT-ASCT in clinical trials, these trials often exclude or underrepresent elderly patients with comorbidities and those belonging to ethnic minorities. We describe our institutional experience highlighting the impact of high-risk cytogenetic abnormalities (HRCAs) on outcomes after HDT-ASCT for NDMM patients. Methods: A total of 449 patients with NDMM who underwent HDT-ASCT between February 2012 and August 2022 were included in this retrospective analysis. HRCAs included the presence of one or more of: deletion 17p, t(14;16), t(4;14), and amplification 1q. Survival analyses, including progression-free survival (PFS) and overall survival (OS), were performed using Kaplan-Meier estimator. Results: With a median follow-up of 29 (1 - 128) months for the entire patient population, the best overall response rate for the patients with HRCAs was lower compared to those with standard risk cytogenetics (90% vs. 96%; P = 0.01). Patients with HRCAs had an inferior PFS compared to patients with standard-risk cytogenetics (29 vs. 58 months; P < 0.001) without a difference in OS (70 months vs. not reached; P = 0.13). Conclusions: In a multivariable analysis adjusting for factors including age, race, and comorbidities, HRCAs, non-lenalidomide-based maintenance, non-proteasome inhibitor-based maintenance, and age greater than 65 were associated with inferior PFS. Amongst these factors, only non-lenalidomide-based maintenance was associated with inferior OS.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".