The Modified Pre-Transplant EBMT Risk Score Is Superior To The HCT-CI Score In Predicting Overall Survival and Non-Relapse Mortality After Allogeneic Hematopoietic Cell Transplantation In Patients With Acute Myeloid Leukemia
Bibliographic record
Abstract
Abstract A variety of factors have been investigated for their influence on outcomes post-allogeneic hematopoietic cell transplantation (HCT). Pre-HCT risk scores have been developed such as the Hematopoietic Cell Transplantation-Comorbidity Index (HCT-CI) and the modified EBMT score (mEBMT) for acute leukemias (involving age, CR status, donor type and gender mismatch). The purpose of this single-center study was to investigate the influence of these scores on outcome post-HCT in 350 patients transplanted between 1999 and 2012 for acute myeloid leukemia (AML) and to compare the predictive value of these scores. Median age of all patients at transplant was 49 years (range 18-71 years), 174 patients (50%) were female. HCT was performed in first complete remission (CR1) for 245 patients (70%) and in second complete remission (CR2) for 105 patients (30%). Cytogenetics at diagnosis were available in 289 patients (83%). Donors were matched related (n=213, 61%) or matched unrelated (n=137, 39%). Cytomegalovirus (CMV) serostatus was negative for both donor and recipient in 113 patients (32%). Peripheral blood stem cells were used as a graft source in 272 patients (78%). Myeloablative conditioning was administered to 239 (68%) patients, 111 (32%) received reduced-intensity conditioning (RIC) regimens. The HCT-CI scores were grouped as 0, 1-2 and ≥3 (94, 137 and 119 patients respectively). The mEBMT scores were grouped as 0-1, 2, 3 and 4-5 (32, 134, 120 and 64 patients respectively). Median follow-up duration among survivors was 62 months (range 12-156 months). Univariate analysis demonstrated a significant difference of overall survival (OS) according to the HCT-CI score (p=0.03), 3-year OS 52%, 53% and 39% in HCT-CI score 0, 1-2 and ≥3 (Figure 1). The mEBMT score also demonstrated a significant difference of OS among the patients with scores 0-1, 2, 3 and 4-5 (p=0.002), 3-year OS 75%, 53%, 40% and 39% respectively (Figure 2). The HCT-CI and mEBMT scores could not stratify the patients according to the cumulative incidence of relapse (CIR) with p-value of 0.99 and 0.50 respectively. For cumulative incidence of non-relapse mortality (NRM), the HCT-CI showed a trend of statistical significance (p=0.07), while the mEBMT score could stratify the patients according to the risk of NRM (p=0.01). Multivariable analysis was performed including the HCT-CI and mEBMT scores as previously defined. Covariates already incorporated in the mEBMT score (age, CR status, related donors) were not included in the multivariable analysis. For OS, the HCT-CI score was removed from the final model due to statistical insignificance (p=0.17). However, the mEBMT score was confirmed as an independent prognostic variable for OS (p=0.00002, HR=1.5, 95%CI=1.2-1.8). For NRM, HCT-CI did not maintain significance (p=0.11) while mEBMT again was confirmed as an independent prognostic factor (p=0.0003, HR=1.5, 95%CI=1.2-1.9). The current study showed that the mEBMT score was confirmed as an independent prognostic factor for OS and NRM in patients with AML undergoing HCT, while the HCT-CI score was not confirmed. In conclusion, the mEBMT risk score is superior to the HCT-CI score in predicting OS and NRM following allogeneic HCT in AML patients. Disclosures: No relevant conflicts of interest to declare.
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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 | 0.001 | 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".