FP661ASSOCIATION OF HOSPITALIZATION AND MORTALITY WITH FRAILTY AND COMORBIDITY INDEXES IN PATIENTS ON HEMODIALYSIS
Notice bibliographique
Résumé
INTRODUCTION AND AIMS: Frailty has been defined as a syndrome or a state of increased vulnerability resulting from a decline in biological functional reserves, such that the ability to cope with stressors is compromised, and leading to higher risk of poor health outcomes, disability, hospitalization and death. Our objective was to relate frailty - as measured by the Fried Phenotype Frail Index (FPFI) and the Edmonton Frail Scale (EFS) - and comorbidity - as measured by the Charlson comorbidity index (CCI) - with the annual hospitalization rate, number of days at hospital and mortality rate in a cohort of hemodialysis prevalent patients. METHODS: Frailty and comorbidity were measured by using the FPFI, EFS and CCI and admissions to hospital, number of days at hospital and final outcomes (death, kidney transplantation or loss to follow-up) were recorded for the hemodialysis prevalent patients managed in an sanitary area. The study included 275 hemodialysis patients: 61.5% men, 57.5% diabetics, median age 65 years, and the mean time in hemodialysis was 50.4 months. The mean follow-up time was 10.84 months; 215 patients completed the 12-month follow-up; 42 died before completing it, 16 received kidney transplantation and 2 were transfered. RESULTS: The proportion of frail patients was 41.1% as measured with the FPFI and 29.5% as measured with the EFS; 65% of patients presented CCI higher than 5, which is high or very high; 151 hospital admissions were recorded for 103 patients, with a total of 1403 and a mean of 9.29 days at hospital.Hospitalization and frailty according to the FPFI: 70 admissions in 47 patients out of 162 non-frail ones (FPFI) and 81 admissions in 56 patients out of 113 frail ones (FPFI). The annual hospitalization rate and number of days at hospital were significantly different between non-frail and frail patients: 0.55 vs. 0.99 p<0.001 and 4.38 vs. 10.50 p<0.001. Hospitalization and frailty according to the EFS: 91 admissions in 59 patients out of 194 non-frail ones (EFS) and 60 admissions in 44 patients out of 81 frail ones (EFS). The annual hospitalization rate and number of days at hospital were significantly different between non-frail and frail patients: 0.61 vs. 1.02 p<0.001 and 4.58 vs. 12.45 p<0.001. Hospitalization and CCI >5: 45 admissions in 31 patients out of 97 ones with CCI≤5 and 106 admissions in 72 patients out of 178 with CCI>5. The annual hospitalization rate and number of days at hospital were not significantly different between both groups 0.55 vs. 0.83 (n.s.) and 4.09 vs. 8.45 p=0.059. Regarding mortality, 11.1% non-frail patients and 21.2% frail ones died, according to the FPFI p<0.05; 10.3% non-frail patients and 27.2% frail ones according to the EFS p<0.005; and 10.4% patients with CCI ≤5 versus 18% patients with CCI>5 (n.s.). In a multivariate analysis, where other variables were included, such as sex, age, diabetes, time in dialysis, serum albumin and serum phosphorus, only frailty measured by the EFS was associated with mortality. CONCLUSIONS: In conclusion, higher hospitalization rate and number of days at hospital were demonstrated in patients classified as frail by the FPFI and EFS, with stronger association than that of the Charlson comorbidity index. Results also suggest that the frailty indexes could be even better predictors of mortality than the comorbidity index.
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 | 0,000 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| 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 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».