Frailty assessed by HCT-FS predicts survival and non-relapse mortality across hematologic disorders undergoing allo-HCT: A multicenter Study of 992 patients
Bibliographic record
Abstract
Abstract Introduction: Frailty is an important factor impacting outcomes following allogeneic hematopoietic cell transplantation (allo-HCT). The Hematopoietic Cell Transplantation Frailty Scale (HCT-FS) has been validated as a reliable predictor of transplant-related outcomes in general transplant populations (Ref:BMT2023;58:317-324). However, its prognosis value within specific disease subgroups, including non-malignant disorders, remains less defined. The aim of this multicenter study was to assess the utility of the HCT-FS as a predictor of overall survival (OS) and non-relapse mortality (NRM) across different hematologic disorders in undergoing allo-HCT. Methods: This observational multicenter study was conducted between 2018 to 2023 across sixteen transplant centers (1 in Canada and 15 in Spain). Frailty was prospectively assessed using the HCT-FS during the initial transplant consultation. Based on this tool, patients were classified as fit, pre-frail, or frail. The primary endpoints were OS and NRM. Kaplan-Meier estimates were used for survival analyses, and differences between frailty subgroups were assessed using the log-rank test. Results: A total of 992 adults with a median age of 56 years (range 18-75) were included. Diagnoses included acute myeloid leukemia (AML, n=498), myelodysplastic syndromes (MDS, n=168), acute lymphoblastic leukemia (ALL, n=113), myeloproliferative neoplasms (MPN, n=92), other lymphoid malignancies (LM, n=73), and non-malignant diseases (n=48). Among the cohort, 41.1% were female, 18.1% had an HCT-CI >3, and 25.2% had a KPS <80%. Reduced-intensity conditioning was used in 61.8% of patients; 76.4% received post-transplant cyclophosphamide; 71.2% received HLA-matched donor grafts, 10.4% from 9/10 mismatched unrelated donors, and 18.3% from haploidentical donors. Frailty assessment revealed 318 (32.1%) fit, 543 (54.7%) pre-frail, and 131 (13.2%) frail patients. The prevalence of frailty differed across disease groups (p=0.002), with patients with MPN showing the lowest incidence (3.3%) compared to those with AML, MDS, ALL, LM and non-malignant diseases (15%, 10.7%, 15%, 15.1% and 14.6% respectively). Likewise, the proportion of fit patients also varied among disease groups, with MDS and MPN patients showing the highest proportions (42.3% and 45.7%, respectively). Overall, frailty, as defined by HCT-FS, was significantly associated with worse OS (2-year OS: 78.9% fit, 66.0% pre-frail, 51.7% frail; p<0.001), primarily due to increased NRM (2-year NRM: 10.5%, 18.9%, and 33.2%, respectively; p<0.001). The cumulative incidence of relapse was similar across groups (2-year CIR: 19.0%, 23.5%, and 22.3%; p=0.229). The negative impact of frailty on OS and NRM was consistent across disease groups, although statistical significance varied. In AML, OS was significantly lower in frail patients than in fit and pre-frail ones (2-y: 54.9%, 78.3% and 66.9%, P<0.001), with higher NRM (33.3% vs. 11.0% and 17.1%; p<0.001). In ALL, OS was higher in fit patients than in pre-frail and frail ones (2-y: 87.9%, 64.0% and 52.3% (p=0.032), with NRM rates of 3.0%, 20.2% and 23.5%, respectively (p=0.147). Among LM patients, OS was significantly higher in fit patients than in pre-frail and frail ones (2-y: 73.5%, 45.5% and 27.3%, p=0.021), with NRM rates of 38.6%, 27.5%, and 15.4% (p=0.27). In MDS, OS tent to be higher in fit patients than in pre-frail and frail ones (2-year: 71.6%, 60.4% and 42.8%, p=0.158), while NRM increased with frailty (12.5%, 24.2%, and 39.3%; p=0.051). The incidence of frailty among MPN patients was low (3.3%), likely reflecting stricter patient selection. However, frail MPN patients still had lower OS (2-year: 66.7%) and higher NRM (33.3%) compared to fit (80.5%, 14.7%) and pre-frail (78.4%, 19.4%) patients (p=0.187 and p=0.083). Finally, patients with non-malignant diseases had excellent outcomes overall. However, OS was lower in frail patients (71.4%) compared to pre-frail (79.7%) and fit individuals (100%) (p=0.152), reinforcing the clinical significance of its assessment in this transplant setting. Conclusions: This study sustains that frailty, as assessed by the HCT-FS, is a strong predictor of OS and NRM following allo-HCT, independent of the underlying hematologic disease. Its adverse prognostic impact is consistent across diagnoses and support the incorporation of frailty assessment into clinical practice to improve risk stratification and clinical decision-making.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".