Fludarabine/Busulfan versus Fludarabine/Melphalan Conditioning in Patients Undergoing Reduced-Intensity Conditioning Hematopoietic Stem Cell Transplantation for Lymphoma
Bibliographic record
Abstract
There is at present little data to guide the choice of conditioning for patients with lymphoma undergoing reduced-intensity conditioning (RIC) allogeneic stem cell transplantation (SCT). In this study, we compared the outcomes of patients undergoing RIC SCT who received fludarabine and melphalan (FluMel), the standard RIC regimen used by the Spanish Group of Transplantation, and fludarabine and busulfan (FluBu), the standard RIC regimen used by the Dana-Farber Cancer Institute/Brigham and Women's Hospital. We analyzed 136 patients undergoing RIC SCT for lymphoma with either FluBu (n = 61) or FluMel (n = 75) conditioning between 2007 and 2014. Median follow-up was 36 months. The cumulative incidence of grades II to IV acute graft-versus-host disease (GVHD) was 13% with FluBu and 36% with FluMel (P = .002). The cumulative incidence of nonrelapse mortality (NRM) at 1 year was 3.3% with FluBu and 31% with FluMel (P < .0001). The cumulative incidence of relapse at 1 year was 29% with FluBu and 10% with FluMel (P = .08). The 3-year disease-free survival rate was 47% with FluBu and 36% with FluMel (P = .24), and the 3-year overall survival rate was 62% with FluBu and 48% with FluMel (P = .01). In multivariable analysis, FluMel was associated with a higher risk of acute grades II to IV GVHD (HR, 7.45; 95% CI, 2.30 to 24.17; P = .001) and higher risk of NRM (HR, 4.87; 95% CI, 1.36 to 17.44; P = .015). The type of conditioning was not significantly associated with relapse or disease-free survival in multivariable models. However, conditioning regimen was the only factor significantly associated with overall survival: FluMel conditioning was associated with a hazard ratio for death of 2.78 (95% CI, 1.23 to 6.27; P = .014) compared with FluBu. In conclusion, the use of FluBu as conditioning for patients undergoing SCT for lymphoma was associated with a lower risk of acute GVHD and NRM and improved overall survival when compared with FluMel in our retrospective study. These results confirm the differences between these RIC regimens in terms of toxicity and efficacy and support the need for comparative prospective studies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".