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Record W4417005160 · doi:10.1182/blood-2025-4530

Longitudinal assessments of simple frailty tools can help predict outcomes of patients undergoing chimeric antigen receptor T-cell (CAR-T) therapy: A prospective pilot study at Princess Margaret Cancer Centre

2025· article· en· W4417005160 on OpenAlexaff
Anca Prica, Antonette Pillainayagam, Manjula Maganti, Tiana Coley, Abi Vijenthira, Samantha Mayo, John Kuruvilla, Michael Crump, Sita Bhella, Robert Kridel, Vishal Kukreti, Chloe Yang, Shabbir M.H. Alibhai, Christine Chen

Bibliographic record

VenueBlood · 2025
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsUniversity Health NetworkPrincess Margaret Cancer Centre
Fundersnot available
KeywordsProportional hazards modelProspective cohort studyCohortComorbidityFollicular lymphomaCancerClinical trial

Abstract

fetched live from OpenAlex

Abstract Introduction Chimeric Antigen Receptor T-Cell (CAR-T) therapy has become the preferred treatment for patients with relapsed/refractory B-cell lymphomas, however outcomes remain suboptimal, and patient selection is key. There is no upper age limit for treatment eligibility, and frailty may be an important factor in assessing fitness for treatment. This study aims to determine if frailty assessments pre-CART can identify those at higher risk for acute toxicities, and predict PFS, and OS, as well as evaluate changes in frailty over time. Methods We performed a cohort study of consecutive patients with lymphoma undergoing CAR-T therapy at our institution, from April 2021 to date. Frailty was evaluated using ECOG performance status, Clinical Frailty Scale (CFS), Grip Strength, Gait Speed, Mini-Cog, and Patient Health Questionnaire (PHQ-2/PHQ-9) at 5 time points: baseline (clearance visit), and 1, 3, 6 and 12 months post-CART. At baseline, additional frailty assessments were completed: the Vulnerable Elders Survey (VES-13), Hematopoietic Cell Transplantation-specific Comorbidity Index (HCT-CI) and Cumulative Illness Rating Scale (CIRS). Clinical variables were also collected. Both standard and time-dependent Cox models (incorporating time-varying covariates) were used to identify predictors of toxicity, and overall and progression-free survival (OS; PFS). Results Seventy-nine patients have been included. Median age is 60 yrs (range 22-83) and 59% were male. 49% had de novo diffuse large B-cell lymphoma, 30% transformation from follicular lymphoma (FL), 9% HGBCL, 8% PMBCL and 4% FL. Most patients (85%) received axicabtagene ciloleucel. Median follow-up was 12.9 months (range 1.5-49 mo). All 79 patients completed assessments at baseline, 63 at 1-mo, 40 at 3-mo, 28 at 6-mo and 19 at 12-mo. Median scores (and range) for the HCT-CI, CIRS, VES-13, Mini-Cog, and CFS at baseline were 1.0 (0-7), 4.0 (0-13), 1.0 (0-7), 4.0 (1-5) and 3.0 (1-7) respectively. The median 4m walk test speed was 1.1 m/s (range 0.5-1.7). There were clinically significant changes observed in CFS over time (p=<0.001), with mean scores of 3.2 at baseline, 3.6 at 1 month, 2.8 at 3 months and 2.4 at 12 months. There were also significant changes (p=0.001) between timepoints for gait speed. Twenty-two patients (28%) experienced immune effector cell neurotoxicity syndrome (ICANS) (4% Grade 4, 4% Grade 3, 6% Grade 2, 14% Grade 1) and forty-nine patients (87%) experienced cytokine release syndrome (CRS) (1% Grade 3, 55% Grade 2, 35% Grade 1) during the 30 days following cell re-infusion. Eight patients (10%) were admitted to the ICU. Of the frailty and clinical variables tested (sex, bridging therapy, LDH, CRP), most were not significantly associated with the development or grade of CRS or ICANS, except for lower risk of CRS with increasing age: OR 0.91 (95%CI 0.84-0.98, p=0.018). The 12-mo OS of the whole cohort is 66% and PFS is 58.1%. On univariable analysis (UV) of baseline data, ECOG, LDH level, and the CFS score were predictive of PFS, while ECOG, LDH, CRP, VES-13 score, CFS, HCT-CI, CIRS score and the 4m walk speed were all predictive of OS. On multivariable (MV) analysis of PFS, only baseline LDH (p=0.009) remained significant. Models were tested on MV analysis of OS, and most frailty measures lost their significance, except for CFS (HR 1.39, 95%CI 1.08-1.79, p=0.011). Analyses incorporating repeated measures were performed, and on UV analysis, the CFS, 4m walk test, LDH, VES-13 (≥3 vs. <3) and Mini-Cog were significantly associated with PFS; the same variables, HCT-CI and CRP were associated with OS. On MV repeated measurements analyses, the CFS (1.508, 95%CI: 1.11-2.05, p=0.008) and Mini-Cog (0.68, 95%CI: 0.50- 0.92, p=0.012) were still associated with PFS, as well as with OS (CFS: HR 1.68, 95%CI 1.26-2.22, p<0.001; Mini-Cog: HR 0.67, 95%CI 0.51-0.89, p=0.005). Conclusions Conducting serial frailty assessments in patients undergoing CAR-T therapy is feasible, but their use and interpretation is complex. Significant longitudinal changes were seen in CFS and gait speed, suggesting an element of reversible functional impairment related to patients' lymphoma. Within the limits of our sample size, baseline measures of frailty were not predictive of CRS or ICANS; however the combination of the simple measures CFS and Mini-Cog was predictive of overall survival, which could help clinical decisions; these observations need to be validated in larger number of patients.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.034
GPT teacher head0.317
Teacher spread0.283 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Published2025
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