Pre-Hematopoietic Cell Transplantation (HCT) Frailty Is Associated with Lower Cognitive Function and Health-Related Quality of Life (HRQOL) in Older Adults Undergoing HCT
Bibliographic record
Abstract
Background: Increasing numbers of older adults are undergoing HCT. Pre-HCT frailty is predictive of survival in older adults.1 However, an association between frailty and functional outcomes and HRQOL are unknown in older adults undergoing HCT. Examining potential associations is important to identify higher-risk HCT candidates who will benefit from proactive interventions and help patients and clinicians with treatment decision making. The objective of this study is to examine the association of pre-HCT frailty status with cognitive function and HRQOL at 12-months post-HCT in adults ≥ 60 years undergoing HCT. Methods: This study is a secondary data analysis of a longitudinal cohort study at a single center conducted between 2018-2022, that included adults ≥60 years who have a diagnosis of a hematologic malignancy undergoing HCT. Participants completed the Fried Frailty assessment, the Montreal Cognitive Assessment (MoCA), and the European Quality of Life Questionnaire–Cancer 30 (QLQ-C30) prior to admission for HCT and at 12-months post-HCT. Frailty was defined as possessing three or more of the following: unintentional weight loss, low grip strength, self-reported exhaustion, slow gait speed, and low physical activity.2 Pre-frail was defined as having 1-2 of the criteria. Multinominal modeling with a random effect for subject was used to account for the correlation within patient, and to compare frailty status over time. ANOVA was used to compare 12-month post-HCT cognitive function and HRQOL between pre-HCT frailty statuses, and pair-wise comparisons were adjusted using Tukey's method. All analyses were done in SAS 9.4 and p <0.05 was considered statistically significant. Results: 104 older adults completed pre-HCT assessment. The average age at HCT was 67.7 years (range: 60.2-76.6). There were 69 (66.3%) allogeneic and 35 (33.7%) autologous HCT recipients. Pre-HCT, 10.6% were frail, 63.5% were pre-frail, and 26% were non-frail. At 12-months post-HCT (n=62), the prevalence of frail, pre-frail and non-frail were 25.8%, 67.7% and 6.5% respectively. There was a statistically significant increase in the prevalence of frailty between pre-HCT and 12 months post-HCT (odds ratio= 4.9, p= <0.001). Pre-HCT frailty status was associated with a lower 12-month MoCA score, and lower physical and emotional functioning on the QLQ-C30. The mean 12-month MoCA score for those who were frail pre-HCT was 23.4 compared to 26.2 and 25.4 for those who were pre-frail and non-frail, respectively (p=0.033). The mean score for the physical function sub-score on the QLQ-C30 at 12-month was 68.3 for patients who were frail pre-HCT compared to 83.9 and 83.2 for those who were pre-frail and non-frail, respectively (p=0.034). The mean score for the emotional function sub-score on the QLQ-C30 at 12-month was 76.1 for patients who were frail pre-HCT compared to 90.6 and 87.9 for those who were pre-frail and non-frail, respectively (p=0.034). Conclusions: Pre-HCT frailty is associated with lower cognitive performance and HRQOL at 12-months post-HCT, specifically physical and emotional functioning. At one year, the prevalence of frailty in HCT survivors approaches that of community dwelling older adults ≥ 80 years.3 The increased prevalence reflects the stress of cancer, accumulation of high-intensity therapeutic exposures, and transplant related morbidities. This study highlights the need to provide targeted interventions to mitigate and prevent frailty pre-HCT and early in the recovery process to preserve cognitive function and maximize HRQOL for older adults post-HCT. 1.Sung, A. D., Koll, T., Gier, S. H., Racioppi, A., White, G., Lew, M., ... & McCurdy, S. R. (2024). Preconditioning frailty phenotype influences survival and relapse for older allogeneic transplantation recipients. Transplantation and Cellular Therapy, 30(4), 415-e1. 2.Fried LP, Tangen CM, Walston J, Newman AB, Hirsch C, Gottdiener J, Seeman T, Tracy R, Kop WJ, Burke G, McBurnie MA; Cardiovascular Health Study Collaborative Research Group. Frailty in older adults: evidence for a phenotype. J Gerontol A Biol Sci Med Sci. 2001 Mar;56(3):M146-56. PMID: 11253156. 3.Collard RM, Boter H, Schoevers RA, Oude Voshaar RC. Prevalence of frailty in community-dwelling older persons: a systematic review. J Am Geriatr Soc. 2012 Aug;60(8):1487-92. PMID: 22881367.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".