P1584FRAILTY IN MAINTENANCE HEMODIALYSIS PATIENTS
Notice bibliographique
Résumé
Abstract Background and Aims The population in Kazakhstan is rapidly aging, as a result the number of geriatric patients on maintenance hemodialysis (MHD) has been increasing. Frailty is prevalent in dialysis patients and is one of the common factors that can lead to increased morbidity and mortality. The primary objectives of this study were to evaluate the prevalence of frailty in elderly patients on MHD by using Edmonton Frailty Scale and assess their association with clinical and laboratory measurements. A secondary objective was to investigate the relationship between nutritional status and frailty. Method From July to September 2018, a total of 65 elderly patients undergoing HD in 7 dialysis facilities in Almaty, Kazakhstan were enrolled in this cross-sectional study. All participants were evaluated for the cognitive status through Mini-Mental State Examination (MMSE), nutritional status by using Mini Nutritional Assessment (MNA), Malnutrition-Inflammation Score (MIS), and anthropometric measurements (body mass index (BMI), triceps skinfold (TSF), mid-arm muscle circumference (MAMC)), functionality (Handgrip strength), as well biochemical data were collected from medical records. Frailty was defined in accordance with the Edmonton Frail scale (EFS). Results The study participants’ median age was 69 (range: 65–88) years old, and median dialysis vintage was 36 (IQR 15–60) months, 53.8% were female. The main comorbidities were hypertension (69.2%) and diabetes (35.4%). The prevalence of frailty assessed by the EFS was 23.1% (men: 13.3%; women: 86.7), 43.1% patients were non-frail (men: 64.3%; women: 35.7%), 33.8% patients were vulnerable (men: 45.5%; women: 54.5%). Based on MIS the prevalence of PEW was 73.8% and, according to MNA, the risk of malnutrition was detected in 47.7%, and 9.2% had malnutrition. No significant difference was observed between genders in the frequency of PEW. Mean body weight was 69.1±11.3kg, the mean BMI was slightly overweight 25.6±4.29kg/m2, while hand-grip strength was 21.33±3.36 in men and 15.5±5.51 in women, p=0.008, and it is lower than the normal population standard values. The frail patients group had a higher proportion of women 86.7% (p=0.001), worse nutritional status (93.3% and 86,7% had PEW evaluated by MIS (p=0.018) and MNA (p=0.035), respectively), more frequency of falls (p=0.01), anemia (p=0.038) when compared to group of non-frail and vulnerable patients. 66.7% of frail patients were widowed (p=0.005). The mean MMSE in this group of patients was 26.7±1.9. Conclusion The prevalence of frailty among elderly hemodialysis patients in this study was 23.1%, and we detected that 86.7% of them were female, as well PEW increased in frail patients. Also the study showed that protein-energy wasting is common among elderly hemodialysis patients. Its prevalence varies between 73.8% and 56.9% depending on the measurement tool used to evaluate the nutritional status. In our country with limited resources, EFS, MIS and MNA could help to follow elderly hemodialysis patients.
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 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 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) | 0,005 | 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 ».