Sociodemographic and patient-related determinants of outcomes after unrelated donor hematopoietic cell transplantation using posttransplant cyclophosphamide-based GVHD prophylaxis
Bibliographic record
Abstract
Abstract Background Allogeneic hematopoietic cell transplantation (allo-HCT) is a curative therapy for several hematologic disorders. While disease- and transplant-related factors significantly impact post-transplant outcomes, sociodemographic and patient-related determinants may also play a crucial role in the success of allo-HCT. We aimed to investigate the influence of sociodemographic and patient-related determinants on outcomes after unmatched allo-HCT using post-transplant cyclophosphamide (PT-Cy)-based graft-versus-host disease (GVHD) prophylaxis. Methods A retrospective multicenter analysis was conducted using the publicly available Center for International Blood and Marrow Transplant (CIBMTR) registry from 2017 to 2021 using P-5891 data by Shaffer et al. We evaluated the impact of recipient age, gender, race and ethnicity, graft source, disease type, disease risk index, donor age, donor-recipient CMV status, karnofsky performance status (KPS), and Hematopoietic Cell Transplantation-specific Comorbidity Index (HCT-CI) on overall survival (OS), relapse, non-relapse mortality (NRM), grade II-IV acute GVHD (aGVHD), grade III-IV aGVHD, moderate/severe chronic GVHD (cGVHD), and GVHD-free relapse-free survival (GRFS). Patient-, disease- and transplant-related factors were compared between groups using the Chi-square test for categorical variables and the Wilcoxon two-sample test for continuous variables. Cox proportional hazards regression analyses were performed to assess outcomes. Hazard ratios (HR) with 95% confidence intervals (CI) were calculated. Variables with p < 0.1 in univariable analysis and clinically relevant variables were included in the multivariable models. Statistical analyses were conducted using Stata version 18, and significance was defined at p < 0.05. Results We included a total of 2,271 unrelated allo-HCT recipients receiving PT-Cy-based GVHD prophylaxis. Mean age was 57.0 (SD 13.9) years, and 56.1% were male. The graft source was bone marrow in 90% of patients and peripheral blood in 10%. The primary hematologic malignancies included acute myeloid leukemia (AML) (54%), myelodysplastic syndrome (MDS) (29.5%), and acute lymphoblastic leukemia (ALL) (16.5%). The race of patients was Caucasian (87%), African American (4.8%), Asian, and others (9%). The conditioning regimen was myeloablative in 39.8% of patients. The KPS was <90 in 44.8% of patients. The HCT-CI was ≥3 in 53% patients. The median OS was 24 (IQR 11.6-36) months. Grade II-IV aGVHD, grade III-IV aGVHD, moderate/severe cGVHD, relapse, and mortality rates were 29.1%, 6.4%, 11.4%, 27.1%, and 36.9%, respectively. After adjusting for significant variables in multivariate regression analysis, recipient age (HR = 1.010, 95% CI 1.004-1.017; p = 0.003), <90 KPS at transplant (HR = 1.20, 95% CI 1.04-1.38; p = 0.013), and higher HCT-CI (HR = 1.11, 95% CI 1.08-1.15; p < 0.001) predicted inferior OS. Lower risk of relapse was observed in patients with ALL (HR = 0.71, 95% CI 0.54-0.93; p = 0.014). However, risk of relapse was higher among patients receiving non-myeloablative conditioning (HR = 1.43, 95% CI 1.12-1.82; p = 0.004). In addition, recipient age (HR = 1.03, 95% CI 1.02-1.04; p < 0.001), <90 KPS (HR = 1.27, 95% CI 1.03-1.56; p = 0.025), ALL (HR = 1.58, 95% CI 1.17-2.14; p = 0.003), and higher HCT-CI (HR = 1.15, 95% CI 1.10-1.20; p < 0.001) predicted worse NRM. For GRFS, higher risk was observed with Black race (HR = 1.38, 95% CI 1.06-1.78; p = 0.016), higher HCT-CI (HR = 1.08, 95% CI 1.04-1.10; p < 0.001), donor age (HR = 1.008, 95% CI 1.000-1.015; p = 0.042), and reduced intensity conditioning (HR = 1.18, 95% CI 1.01-1.37; p = 0.034). Moreover, higher HCT-CI also predicted a higher risk of aGVHD III-IV (HR = 1.12, 95% CI 1.04-1.20; p = 0.003). High and very high-risk disease status at the time of transplant predicted worse OS, relapse rate, and GRFS. Conclusion In this multicenter analysis, we found that recipient age, performance, and comorbidity status, disease type, disease risk, and conditioning intensity consistently predict worse outcomes in patients undergoing unmatched allo-HCT with PT-Cy prophylaxis. Donor age and race may also impact outcomes; however, further investigation is warranted. Sex, graft source, donor-recipient CMV status, and ethnicity do not appear to influence outcomes.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".