The Independent Effects of Frailty and Comorbidity On the Quality of Life in MDS Patients
Notice bibliographique
Résumé
Abstract Abstract 699 Background: MDS is a disease of the elderly. While comorbidity defined by the MDS comorbidity index (MDS-CI) may have independent impact on overall survival in MDS (Della Porta MG., Haematologica 2011), the impact of clinical frailty (an age-related vulnerability state created by loss of physiologic reserve) on clinical outcome and quality of life is not yet known. Rockwood and colleagues have developed a simple 9-point clinical frailty scale (CFS) based on clinical judgment that was highly correlated with the risk of death, institutionalization, worsening health and hospital use (Rockwood K., CMAJ 2005). Since 2008, we have prospectively assessed QOL in all patients registered at our MDS clinic using the instruments EORTC QLQ-C30, FACIT-F, and EQ-5D. Since January 2012, we have also recorded comorbidity and frailty. We present longitudinal QOL data on 240 patients and evaluate the effects of comorbidity (MDS-CI) and frailty (CFS) on QOL in addition to the more traditional covariates. Methods: We considered the following co-variates' potential impact on QOL scores: age, sex, IPSS, time from diagnosis, hemoglobin, transfusion dependence, MDS-CI categorically and frailty. We used univariate and multivariate linear regression analysis to determine their relationship with QOL scores at baseline and over time. For time-dependent covariates, linear mixed modelling was used. P value of <0.05 was considered significant. Spearman correlation was calculated between frailty and comorbidity. Clinically significant (CS) score differences were considered 10 points for the EORTC, 0.08 for EQ5-D and 4 for the FACT-Fatigue. Patients provided informed consent for this REB-approved study. Results: 236 patients (63% males) consented at a median time from diagnosis of 0.8 years (IQR 0.4–2.8). The median time to death or last follow-up was 2 years (95% CI 1.9–2.3). At first QOL assessment, the median age was 72 y. Of the 208 patients with measurable IPSS scores, 83% fell into low/low intermediate risk categories. 40% were transfusion dependent, 46% had a Hgb of <100 g/L and 31% had a ferritin >1000 ug/L. Serial QOLs were measured on 2 (n=187 patients), 3 (140), 4 (114), 5 (86), and 6 (63) occasions with a median lag time between QOLs of 17 weeks (IQR 12–26). The MDS-CI risk categories (scores) were Low (0): 46%, Intermediate (1–2): 41% and High (>2): 12%. The median Rockwood Frailty Score was 3 (range: 1–7). Compared to normative data from the general population, MDS patients had SS and CS differences in the following QLQ-C30 scales: worse physical, role, emotional and cognitive functioning, and worse fatigue and global health status. MDS-CI categories were weakly correlated with frailty (r= 0.25, p=.02). As we have previously shown, at baseline, transfusion dependence had significant negative impact on global health state (p=.0036), EQ-5D (p=.0001) and fatigue (p=.0051) scores and lower hemoglobin had negative impacts on fatigue (p=.01) and dyspnea (p<.0001). Patients with higher frailty scores had significantly worse fatigue (p=.001). Most QOL domains and global QOL scores remained stable over time (figure 1). When examined for significant changes over time, lower frailty scores were independently predictive of improved global health status and health utility (p< .0001) while patients with lower comorbidities had decreased levels of fatigue (p=.04) and dyspnea (p=.02) (Table 1). Conclusions: MDS health-related quality of life scores remain surprisingly stable over time. While transfusion dependence is still highly impactful, frailty and comorbidity are independent variables that should be routinely evaluated for their predictive effects on quality of life, drug toxicity and overall survival. Disclosures: Buckstein: Celgene: Honoraria, Research Funding. Wells:Novartis: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; Janssen Ortho: Honoraria, Research Funding; Alexion: Honoraria, Research Funding.
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 | 0,002 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 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) | 0,003 | 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 ».