Functional Predictors of Chemotherapy Toxicity in Elderly Lymphoma Patients: A Prospective Pilot Study
Bibliographic record
Abstract
Abstract Introduction: Treatment of lymphoma, while subtype specific, often includes curative intent combination chemotherapy. There is little evidence on which to base treatment decisions for older patients, and oncologists often rely on their clinical impression and the patient's chronologic age to make treatment decisions. Objective measures may be more accurate and reproducible in predicting eligibility for combination chemotherapy; however their utility in the clinical setting is still under investigation. Objective: The objectives of this pilot study were to assess the feasibility of applying the Hurria (Hurria et al. JCO 2011) and CRASH (Extermann et al. Cancer 2012) chemotherapy toxicity risk stratification measures, as well as physical performance tests, during busy outpatient clinics, and their ability to potentially predict chemotherapy toxicity. Methods: This is a prospective, single centre pilot feasibility study in patients 70 years of age or older, with lymphoma, planned for definitive systemic chemotherapy. Patients completed geriatric tools (eg. Hurria and CRASH questionnaires, gait speed test and grip strength, CSHA Clinical Frailty Scale and Charlson Comorbidity Index) at baseline. Physical performance tests were repeated with each treatment cycle and data were collected on toxicities, hospital admissions, as well as change in treatment cycles or dosing. Primary outcomes were feasibility and time needed to complete the measures; secondary outcomes were correlation between chemotherapy toxicity and geriatric assessment results, and other patient characteristics (stage and ECOG status). Results: Thirty patients have been enrolled to date (out of a target of 30), and 29 have completed all follow-up assessments, with a median age of 77 yrs (range 69-90) for the whole group, and 59% being male. Diagnosis was diffuse large B cell lymphoma in most (59%), followed by CLL (17%), indolent lymphoma (10%), and other (14%). Chemotherapy treatments most commonly included RCHOP (59%), and bendamustine and rituximab (24%). Aggressive histology pts received G-CSF as 1° prophylaxis. The chemotherapy was dose reduced by the treating physician at cycle 1 in 8 patients (28%), and during the course of treatment in 3 pts (10%). Using the Hurria risk stratification score, 7 pts (24%) were low, 17 (59%) were intermediate and 5 (17%) high risk for chemotherapy toxicity. Similarly, the CRASH scoring identified 2 pts (7%) as low, 11 (38%) as medium-low, 14 (48%) as medium-high and 2 (7%) as high risk. The median amount of time needed to complete the Hurria tool was 2 min (1-5 min) vs. 20 min (5-30min) for the CRASH score. Fourteen pts (48%) experienced CTCAE grade 3-5 toxicity, for a total of 25 severe AEs. The most common gr ≥3 AEs was febrile neutropenia (4 pts), anemia and thrombocytopenia (3pts each); other severe AEs, such as upper GI bleed, PE and DVT, rapid atrial fibrillation, and hyponatremia occurred in one pt each. Dose delays occurred in 9 pts (31%) and 5 pts (17%) required hospitalization due to toxicity. The CSHA frailty score and grip strength worsened throughout treatment and had not recovered by the 1 month visit post-treatment, while the 6 meter walk time did not significantly vary over time during treatment (Figure 1). On univariate analysis, the CSHA frailty score and grip strength changes over time, and the Hurria risk score at baseline were significantly associated with any adverse event, while only the CSHA frailty score and Hurria risk score were associated with Grade 3 or higher events (Tables 1 and 2). On multivariate analysis, the CSHA Frailty score and Hurria risk score retained significance for any AE, however when adjusted for CSHA Frailty, the Hurria score was no longer a significant predictor. Conclusion: When perceived fit older patients are treated with full-dose systemic chemotherapy, the rate of toxicity is high. The Hurria tool, takes a short time to administer, and while not previously tested in lymphoma patients, appears to predict toxicity. The CSHA Frailty was the most robust predictor of chemotherapy toxicity in our patient population, and is a very simple measure to administer. The results of this pilot has led us to recommend routine use of these tools in all older lymphoma patients undergoing systemic chemotherapy at our centre, with the aim of further testing their ability to predict chemotherapy toxicity and treatment outcomes, as well as help plan co-interventions. Disclosures Kuruvilla: Abbvie: Consultancy; Princess Margaret Cancer Foundation: Research Funding; Leukemia and Lymphoma Society Canada: Research Funding; Lundbeck: Honoraria; BMS: Consultancy, Honoraria; Seattle Genetics: Consultancy, Honoraria; Amgen: Honoraria; Roche: Consultancy, Honoraria, Research Funding; Karyopharm: Honoraria; Merck: Consultancy, Honoraria; Celgene: Honoraria; Janssen: Consultancy, Honoraria, Research Funding; Gilead: Consultancy, Honoraria. Crump:Jansen-Ortho: Consultancy; F. Hoffmann-La Roche Ltd: Consultancy; Servier Canada: Consultancy.
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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 | 0.002 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
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".