Effect of Obesity on Outcomes after Autologous Hematopoietic Stem Cell Transplantation (Auto HCT) for Multiple Myeloma (MM): An Analysis from the Center for International Blood and Marrow Transplant Research (CIBMTR).
Bibliographic record
Abstract
Abstract Background: Obesity is increasing in prevalence worldwide and has potential implications on chemotherapy dosing and selection of patients for therapy. Auto HCT improves outcomes for patients with MM, but optimal chemotherapy dosing for obese patients is poorly defined. Methods: We identified 1087 patients reported to the CIBMTR between 1995 and 2003 who underwent auto HCT for MM as part of initial therapy, defined as within 18 months of diagnosis, and received high-dose melphalan conditioning, with or without total body irradiation (TBI). We categorized patients by body mass index (BMI) as normal (18.5– 24.9), overweight (25–29.9), obese (30–34.9), or severely obese (≥35). Underweight patients (BMI <18.5, N=9) were excluded from analysis. We analyzed overall survival (OS) and progression-free survival (PFS) from date of transplant, using Kaplan-Meier curves and the log-rank test for univariate analyses and using Cox proportional hazards models for multivariate analyses. Results: Cases were reported from 114 centers in 10 countries. There were 292 patients of normal weight (27%); 472 were overweight (43%), 198 were obese (18%), and 125 severely obese (11%). Median follow-up of survivors was 63, 61, 60 and 59 months, respectively. Significant baseline differences among BMI groups indicate that obese patients selected for transplant were younger (median age 58, 58, 56, and 55 years, respectively, p=0.005) and had less severe disease at diagnosis, with lower bone marrow plasmacytosis and less frequent renal failure, hypercalcemia, and severe anemia. Obese patients received higher total melphalan doses but lower doses per square meter of body surface area (calculated based on actual body weight). Univariate analyses show no significant effect of BMI category on either OS or PFS. However, among patients who received TBI as part of conditioning, multivariable analyses show a significant effect of BMI on PFS (p-value for interaction 0.006). In this subgroup, a higher BMI was associated with longer PFS (p=0.006, Figure 1). Among patients who received melphalan alone, no effect of BMI was apparent (Figure 2). The difference in PFS for patients receiving melphalan/TBI was due to a decreased risk of relapse among obese patients. Pairwise comparisons of conditioning regimen (TBI vs. no TBI) within BMI categories showed significant reduction in risk of treatment failure for obese (HR=0.54, p=0.04) and severely obese (HR=0.32, p=0.001) patients who received TBI. No differences in OS were apparent in multivariate analyses. Relative risks (RR) for PFS from a multivariable model adjusting for possible confounders are shown below: TBI No TBI n RR (95% CI) P n RR (95% CI) P Normal 44 1.00 Poverall=0.006 248 1.00 Poverall=0.18 Overweight 62 0.92 (0.60–1.40) 0.69 405 0.90 (0.75–1.08) 0.24 Obese 21 0.49 (0.27–0.90) 0.021 177 0.85 (0.68–1.07) 0.16 Severely obese 22 0.39 (0.20–0.76) 0.005 100 1.12 (0.86–1.45) 0.42 Conclusion: Obesity, when measured by BMI, has no statistically significant effect on OS among patients with myeloma receiving high-dose melphalan. Among patients receiving melphalan with TBI, a higher BMI is associated with improved PFS. The reason for the restriction of this effect to TBI-containing conditioning regimens requires further investigation. The current common strategy of reducing melphalan doses (i.e. calculating based on ideal or adjusted body weight) does not appear to impair outcomes for obese patients. Obesity should not exclude patients from consideration of autologous transplantation. Figure Figure
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".