Prognostic Performance of Frailty Measures in MDS Patients Treated with Hypomethylating Agents
Notice bibliographique
Résumé
Background: Hypomethylating agents (HMAs) can confer transfusion independence and prolong overall survival (OS) in patients with myelodysplastic syndromes (MDS), but response rates are < 50% and depend on sustained administration. In Ontario, 33% of higher risk MDS patients receive < 4 cycles AZA and have very short survival. Identifying the patients unsuitable for HMAs and the factors predictive of overall survival (OS)/leukemia free survival (LFS) would be of value. MDS-CAN, the national MDS registry prospectively evaluates patient-related factors in addition to disease factors in MDS, MDS/MPN, and oligoblastic AML patients. Objective: Determine the factors predictive of OS/LFS and the completion of ≥ 4 cycles of HMA, with particular focus on frailty and comorbidity. Methods: All patients who had received HMAs (azacytidine (AZA), decitabine, guadecitabine, ASTX727) were eligible. Frailty was assessed using the Rockwood clinical frailty scale (CFS) and the frailty index (FI) comprised of 42 deficits we previously developed. The MDS-FI was calculated using baseline measurements of comorbidities, laboratory values, Lawton Brody instrumental activities of daily living (LB-IADL), quality of life (EQ-5D), and 3 physical fitness tests. Patients who had ≤ 13 missing variables on the MDS-FI were included. Kaplan-Meier (KM) OS curves were calculated from treatment start date to death or last follow up. Univariable and multivariable analysis was done to identify significant predictors of OS, LFS and the receipt of ≥ 4 cycles of HMA. Results: There were 422 patients treated with an HMA (94% AZA). FI scores could be calculated in 188 patients and CFS in 169 patients. Among the 188 patients, the median age at HMA start was 73 years old (IQR 67, 79), time from diagnosis was 10 months (IQR 2, 28), 66% of patients were high/intermediate-2 risk IPSS, and 72% were high/very high risk IPSS-R. 40% were transfusion dependent, 30% had poor/very poor cytogenetics, and 10% had oligoblastic AML. Median number of HMA cycles was 7 and 76% completed ≥ 4 cycles. The median follow up was 12 months (IQR 7, 25). 19% of patients developed AML. Actuarial median OS was 17 months (95% CI: 13-20) with 50% of deaths due to AML or progressive disease. The MDS-FI score was grouped into categories of 1, 2, and 3 (scores ≤0.2, 0.2-3, and >3, respectively), with a median score of 0.3 (IQR 0.2, 0.3). The median scores for the clinical frailty scale (CFS), Charlson comorbidity index (CCI), and MDS-specific comorbidity index (CI) were 3 (IQR 2, 4), 1 (IQR 0, 2), and 0 (IQR 0, 2) respectively. 21% of patients had cardiac comorbidity(s), 53% had ≥ 1 disability (LB-IADL), and 76% had ≥ 1 impaired symptoms or function on the EQ5D, the most common being usual activities (45%) and pain/discomfort (44%). On physical testing, 56%, 30% and 88% had partial or full deficits in grip strength, 4 meter walk and the 10x chair stand tests compared with age/sex matched reference standards. Those who completed ≥ 4 cycles of AZA compared with those that did not were more likely to be younger (73 vs 78 years old, p=0.002), have lower risk disease (IPSS-R very low/low/intermediate: 29 versus 13%, p=0.044), have lower comorbidity (MDS-CI score: 0 vs 1, p=0.006), lower frailty scores (CFS: 2 versus 3, p=0.008) and performed better on grip strength (31 vs 26 kg, p=0.021) and the 10x chair stand test (28 vs 30s, p=0.045). Predictive factors from univariate analysis are presented on Table 1. There was a trend towards receiving fewer HMA cycles if patients fell within higher FI categories (8 vs 7 vs 5 cycles, p=NS). OS declined with increasing FI categories (p=0.002, Fig 1a). In subgroup analysis by IPSS or IPSS-R score, the FI further stratified the OS of patients with IPSS high/intermediate-2 (p=0.001, Fig 1b) and IPSS-R very high/high risk groups (p=0.002, Fig 1c). The best multivariate model for OS included IPSS (p=0.001), LDH (p=0.001), and MDS-CI (p<0.001). Multivariate predictors of LFS included LDH (p=0.024) and IPSS (p=0.042). IPSS-R (p=0.011), MDS-CI (p=0.007) and grip strength (p=0.007) were independent predictors of receiving ≥4 cycles of HMA. Conclusions: Frailty and comorbidity provide important prognostic information for clinical outcomes in MDS patients receiving hypomethylating agents. The evaluation of patient characteristics in addition to disease parameters should be an integral part of clinical decision-making. Disclosures Wells: Alexion: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; Novartis: Honoraria, Research Funding. Rockwood:Alzheimer Society of Canada: Research Funding; Lundbeck: Membership on an entity's Board of Directors or advisory committees; Canadian consortium on neurodegeneration in aging and nutricia: Membership on an entity's Board of Directors or advisory committees; Foundation Family Fund: Research Funding; Pfizer: Research Funding; Capital Health research support: Research Funding; Sanofi: Research Funding; CIHR: Research Funding; Nova Scotia Health research foundation: Research Funding. Geddes:Celgene: Honoraria, Research Funding; Alexion: Honoraria, Research Funding; Novartis: Honoraria, Research Funding. Sabloff:Actinium Pharmaceuticals, Inc: Membership on an entity's Board of Directors or advisory committees; Sanofi Canada: Research Funding; Astellas Pharma Canada: Honoraria, Membership on an entity's Board of Directors or advisory committees; ASTX: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees; Jazz Pharmaceuticals: Honoraria, Membership on an entity's Board of Directors or advisory committees; Novartis Pharmaceuticals: Honoraria, Membership on an entity's Board of Directors or advisory committees; Pfizer Canada: Honoraria, Membership on an entity's Board of Directors or advisory committees. Keating:Sanofi: Membership on an entity's Board of Directors or advisory committees; Hoffman La Roche: Membership on an entity's Board of Directors or advisory committees; Shire: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees; Seattle Genetics: Consultancy; Novartis: Honoraria; Celgene: Membership on an entity's Board of Directors or advisory committees. Leber:Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Pfizer: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene Corporation: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; AbbVie: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Astellas: Honoraria, Membership on an entity's Board of Directors or advisory committees; Jazz: Honoraria, Membership on an entity's Board of Directors or advisory committees; Alexion: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Leitch:Celgene Corporation: Honoraria, Research Funding; Otsuka: Honoraria; Novartis: Honoraria, Research Funding, Speakers Bureau; Alexion: Research Funding; AbbVie: Research Funding. Yee:Takeda: Membership on an entity's Board of Directors or advisory committees; Merck: Research Funding; Astellas: Membership on an entity's Board of Directors or advisory committees; Pfizer: Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Millennium: Research Funding; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Astex: Research Funding; Hoffman La Roche: Research Funding; MedImmune: Research Funding. St-Hilaire:Teva: Membership on an entity's Board of Directors or advisory committees; Sanofi: Honoraria; BMS: Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees. Finn:Sanofi: Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees; Celgene: Honoraria; Ipsen: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees; Lundbeck: Membership on an entity's Board of Directors or advisory committees; Merck: Research Funding; Astra Zeneca: Membership on an entity's Board of Directors or advisory committees; Roche: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Bristol Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees; Boehringer Ingelheim: Research Funding; Alexion: Membership on an entity's Board of Directors or advisory committees; Pfizer:
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 | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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 ».