Slow and Steady Wins the Race: Optimization of Older Adult Allogeneic Hematopoietic Cell Transplant (Allo-HCT) Candidates
Bibliographic record
Abstract
Background: Allo-HCT is the only potentially curative therapy available for many hematologic malignancies, but due to its intensity and associated toxicities, is infrequently offered to older adults. This study explored the feasibility and efficacy of individualized, geriatric assessment (GA)-guided intervention among older adult candidates for allo-HCT. Methods: Patients ≥ 60 years old that were candidates for allo-HCT were recruited between June 11, 2021 and July 1, 2022. Exclusion criteria included inability to speak English, diagnosis of dementia, and planned allo-HCT in < 1 month or > 6 months. Baseline GA was completed at time of enrollment. Subjects were prescribed multi-domain personalized geriatric intervention based on results of their individual GA. Patients were continued on geriatric intervention for up to 6 months or until transplant. A post-intervention GA was conducted after 6 months or at time of admission for allo-HCT, whichever occurred first. Metrics included in GA are defined in table 1. Subjects were provided wearable accelerometer device for activity tracking. Primary outcome of interest was change in 6-minute walk test (6MWT) and secondary outcomes included improved or maintained short physical performance battery (SPPB), receipt of allo-HCT, and overall survival (OS). Wilcoxon signed rank test was used to compare GA metrics pre- and post-intervention, and OS was estimated using the Kaplan-Meier method. Exploratory analyses included univariable modeling of association of baseline GA metrics with receipt of allo-HCT and OS. Results: A total of 63 allo-HCT eligible patients were approached, 30 consented and were evaluable. Of 33 not enrolled, 17 declined (7 due to requirement to wear accelerometer device), 7 were planned for allo-HCT in < 1 month, 4 were not interested in allo-HCT, 3 had virtual appointments with incomplete GA, 1 had dementia and 1 with anticipated allo-HCT date > 6 months. The majority of enrolled subjects were male (80%) and all were non-Hispanic, white. Median age at enrollment was 68.9 years (range: 60.8-79.2). Half (n=15) were diagnosed with acute myeloid leukemia, 40% (n=12) with myelodysplastic syndrome, and one each with chronic myelomonocytic leukemia, myelofibrosis, and non-Hodgkin lymphoma. The median time from allo-HCT-eligible diagnosis to baseline GA was 4.1 months (range: 1.2-118.9). More than half (n=16, 53.3%) of subjects received allo-HCT. Median time to allo-HCT from diagnosis was 13.8 months (range: 3.3-122.1) and from baseline GA was 4.6 months (range: 0.5-10.8). No one baseline GA metric was associated with receipt of allo-HCT (table 2). Median OS from study enrollment for all subjects was not reached (95% CI: 9.8 month-NR) with median follow-up of 14.8 months (range: 10.7-19.3). At baseline, better gait speed on 6MWT (HR: 0.72; 95% CI: 0.59-0.89, p=0.0018), SPPB (HR: 0.76; 95% CI: 0.60-0.97; p=0.02), and Montreal Cognitive Assessment (MOCA; HR: 0.72; 95% CI: 0.55-0.95; p = 0.02) scores were all associated with improved OS in univariable analysis (table 2). Pre and post-intervention GA metrics are presented in table 1. Only 14 subjects completed at least partial post-intervention GA, 12 of whom received allo-HCT. Reasons for not completing post-intervention GA included subjects declining to schedule (n=6), issues of care coordination (e.g.: frequent or prolonged hospital admissions, changes to allo-HCT admission plan; n=6), and death (n=4). Twelve subjects had both pre and post-intervention 6MWT data, 83.3% (n=10) improved by at least 50 feet. Ten subjects had both pre and post-intervention SPPB and all improved by at least 1 point or maintained a normal score (>9). Discussion: Improvement in or maintenance of 6MWT and SPPB were demonstrated in the majority of subjects with measured pre and post-intervention GA. Unfortunately, attrition was a major limitation of the study. Patterns of loss to follow-up, as well as reasons for screen failure, will be important areas for further exploration and potential targets for intervention at institutional and provider levels. Importantly, age was not associated with OS. Early education about allo-HCT paired with patient-centered goals of care conversations may be useful in identifying older adults most motivated and likely to benefit from GA-guided intervention.
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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".