Frailty and Long-Term Survival Among Older Adults with Blood Cancers
Bibliographic record
Abstract
INTRODUCTION: Formal geriatric assessment is now recommended for all older (65+) adults with cancer who are receiving systemic therapy (Dale, JCO, 2023). While many tools have been validated in cancer populations, the association of geriatrics-driven frailty assessment tools with long-term survival for older adults with hematologic malignancies has yet to be characterized. METHODS: The Older Adult Hematologic Malignancy (OHM) Program at Dana-Farber Cancer Institute aims to assess the utility of three widely-studied frailty assessment tools-deficit accumulation method, phenotypic model and 4-meter gait speed (4MGS)-for older adults with blood cancers. From February 2015 to July 2024, we approached patients aged ≥ 73 years presenting for an initial consultation for MDS/leukemia, myeloma, or lymphoma at our institution. A trained research assistant conducted a frailty assessment consisting of 42 patient-reported and objective measures, spanning domains of comorbidity, functional status (e.g., instrumental activities of daily living, IADLs), physical performance (e.g., 4MGS and grip strength), and cognition (e.g., delayed recall). The majority of assessments were performed in-person, with a portion conducted virtually (DuMontier, Blood Advances, 2022). The deficit accumulation method (Rockwood, Journals of Gerontology, 2007) counts aging-related health deficits across multiple domains to compute a frailty index (FI) as the proportion of deficits present out of the total number of possible deficits measured. Patients were classified as robust if the FI was less than 0.2, pre-frail if between 0.2 and 0.35, and frail if greater than 0.35. The phenotypic model (Fried, Journals of Gerontology, 2001) uses five criteria to define a frailty syndrome (slow gait speed, weakness measured by grip strength, self-reported exhaustion, low physical activity, and weight loss). Patients were classified as robust if they had no deficits, pre-frail if they had one or two deficits, and frail if they had 3 or more deficits. For gait speed, patient's normal 4MGS was analyzed as a categorical variable (>0.8, >0.6 to 0.8, < 0.6) consistent with a priori cutoffs from the literature. Patients were followed from the time of initial consultation through the date of death or last follow-up, after which they were censored. Demographic and clinical variables were descriptively summarized, and multivariable Cox proportional hazards regression was used to estimate hazard ratios for the frailty assessment tools adjusted for age and gender. RESULTS: As of July 18, 2024, frailty was assessed for 1271 patients; all three measures of interest-deficit accumulation method, phenotypic model and 4MGS-were available for 945 patients. Among these, median age was 78 years (IQR, 76 to 82) and 36% were female. 32% had MDS/AML, 34% lymphoma, and 34% myeloma. Median follow-up was 30 months (IQR, 10 to 59) among all patients and 44 months (IQR, 0.03 to 110) among 490 patients alive at last follow-up. Median survival was 56 months and 5-year overall survival was 48% (95% CI 45%, 52%). According to the deficit accumulation method, 34% were pre-frail and 8% were frail. According to the phenotypic model, 61% were pre-frail and 6% were frail. In terms of 4MGS, 33% were >0.6 to 0.8 m/s and 14% were < 0.6 m/s. Over half (52%) reported weak grip strength, 31% unintentional loss of at least 10 pounds within the past year, and 10% “exhaustion.” All three frailty assessment tools were associated with mortality, independent of age and gender (deficit accumulation method: robust ref; pre-frail HR 1.90 [95% CI 1.55, 2.32]; frail HR 2.46 [1.83, 3.31]; phenotypic model: robust ref; pre-frail HR 2.07 [1.64, 2.62], frail HR 3.19, [2.19, 4.66]); 4MGS: >0.8 m/s ref; >0.6 to 0.8 HR 1.47 [1.19, 1.81], ≤ 0.6 HR 2.13 [1.63, 2.78]). CONCLUSIONS: In this large cohort of older adults with blood cancers and long-term follow-up, pre-frail and frail states were prevalent as measured with gold standard geriatric tools. Impaired mobility, weakness, and weight loss were more prevalent than in general populations of community-dwelling adults (e.g., 31% of OHM patients reported weight loss, while this has been found to be only 6% in a general population; Fried, Journals of Gerontology, 2001). All three tools showed dose-response relationships with survival. These data underscore the importance of measuring and addressing frailty in older adults undergoing treatment for blood cancer.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".