Development and Testing of a Lymphoma Clinical Trials Specific Frailty Index: A Secondary Analysis of the LY.12 Clinical Trial
Notice bibliographique
Résumé
Abstract Background: Frailty is common in older patients with lymphoma. However, it remains unknown whether frailty is prevalent in patients included in clinical trials of lymphoma, as those with frailty may meet inclusion criteria of a trial which do not include functional information beyond performance status (PS). Understanding the prevalence and impact of frailty in clinical trials is important to direct future stratification criteria, as well as to have robust data to counsel frail patients on their potential outcomes. Methods: We conducted a secondary analysis using data from the phase III LY.12 clinical trial in which patients with relapsed aggressive non-Hodgkin lymphoma were randomized to gemcitabine-dexamethasone-cisplatin or dexamethasone-high dose cytarabine-cisplatin chemotherapy prior to autologous stem cell transplant. The primary objective of our study was to construct a lymphoma clinical trials specific frailty index (FI) using previously described methods (Searle. BMC Geriatr. 2008;8:24). Secondary objectives were to describe the association of frailty (binary variable) with overall survival (OS), event-free survival (EFS), hospitalization, adverse events (AE), serious adverse events (SAE), and proceeding to transplant, and to describe the association of frailty with these outcomes, controlling for important covariates (age, sex, immunophenotype, revised international prognostic index score (rIPI), Eastern Cooperative Oncology Group (ECOG) PS, stage, and response to previous chemotherapy). Results: 619 patients in the LY12 trial were used to construct the frailty index (Table 1). Using a binary cut-off for frailty (<0.2), 15% (N=93) of patients were classified as frail. There were no differences in age or sex between frail and non-frail patients; however they differed in terms of other lymphoma-related characteristics (Table 2). Frailty was strongly associated with OS (HR 2.012, 95% CI 1.57-2.58), EFS (HR 1.94, 95% CI 1.53-2.46), frequency of the worst overall Grade >3 AE (OR 2.65 (15% vs. 6%), p=0.003), and likelihood of proceeding to ASCT (OR 0.26, 95% CI 0.15-0.43), but not hospitalization (OR 1.52, 95% CI 0.97-2.40) or SAE (6% vs. 4%, p=0.3). In multivariable analysis, frailty was not significantly associated with OS, EFS, likelihood of proceeding to ASCT, nor hospitalization (Table 3), though there was a trend to significance for ASCT. However, rIPI remained significantly associated with OS and EFS, ECOG remained significantly associated with OS (Table 3) Conclusion: A potentially broadly applicable lymphoma clinical trials specific FI was constructed through secondary analysis of LY12 data. 15% of patients were classified as frail. Frailty was significantly associated with OS, EFS, frequency of grade >3 AE and likelihood of proceeding to transplant. However, this relationship no longer was significant when controlling for lymphoma-related prognostic variables, suggesting that the impact of poor prognostic features of lymphoma supersede the impact of frailty alone in this younger clinical trial population. Interestingly, rIPI and ECOG demonstrated their value as simple predictors that are highly associated with OS and/or EFS even when controlling for other important covariates including frailty. These findings require further testing in an external data set, and would be particularly valuable to test in an older population. Calibration of the FI against clinical frailty assessment (e.g. Clinical Frailty Scale, Comprehensive Geriatric Assessment) would also be meaningful to confirm its ability to classify frail versus non-frail patients. Figure 1 Figure 1. Disclosures Crump: Epizyme: Research Funding; Roche: Research Funding; Kyte/Gilead: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees. Hay: Merck: Research Funding; Roche: Research Funding; Abbvie: Research Funding; Amgen: Research Funding; Karyopharm: Research Funding; Seattle Genetics: Research Funding. Prica: Astra-Zeneca: Honoraria; Kite Gilead: Honoraria.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,100 | 0,134 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,000 |
| Méta-épidémiologie (sens large) | 0,003 | 0,007 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».