Functional Predictors of Chemotherapy Toxicity in Elderly Lymphoma Patients: A Prospective Pilot Study
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».