#4781 FRAILTY IN PERITONEAL DIALYSIS: PREVALENCE AND PREDICTION FACTORS
Notice bibliographique
Résumé
Abstract Background and Aims Frailty is a clinical syndrome characterized by a state of increased vulnerability and risk of adverse outcomes following a stress, which exerts a heavy economic and social burden. The identification of this syndrome is done throught validated tools. Although frailty is associated with advanced age, certain conditions that produce age-like changes can lead to a state of frailty at younger ages. The presence of multiple comorbidities increases frailty risk. Also, low levels of albumin, even in the normal range, have been related to greater frailty and its levels have been used to assess frailty. Chronic kidney disease is associated with higher prevalence of frailty. However, little has been reported about frailty prevalence in peritoneal dialysis population. Our aim was to access the prevalence of frail and vulnerable PD patients using the Edmonton Frailty Scale (EFS) and to identify prediction factors. Method In a retrospective cohort study, we assessed frailty in PD patients from 2 center in Portugal by a validated frailty score (Edmonton Frailty Scale-EFS). We also collected information that could contribute to frailty in these patients. Linear and logistic regressions were used to access frailty predictors. Receiver operating characteristics (ROC) curve was used to access accuracy of those predictors. Results We included 74 PD patients, 51,5% male, mean age 53,9 ± 15,1 years, median body max index 25,2 ± 4,3 Kg/m2. Median CCI was 4 [interquartile range (IQR) of 3]. Fifty-three patients (71,6%) were classified as robust (non-frail), 11 as vulnerable, and 10 as frail (8 mildly and 2 moderate). Patients were divided into two groups: A group included robust (non-frail) patients; B group included vulnerable and frail ones. Age, sex distribution, IMC, diabetes mellitus prevalence, variables related to PD (efficacy, vintage, modality and complications), and levels of hemoglobin, phosphorus, potassium and C-reactive protein were similar between the groups. Non frail patients presented significantly higher albumin levels and lower CCI (p-value < 0,05). A linear regression was performed to ascertain the effects of CCI and albumin: CCI was an independent predictor for frailty/vulnerability, accessed by EFS (for each point in CCI, there was an increase of 0,5 points in EFS). Albumin did not show a significant effect defining frailty in our sample. ROC curve showed a high accuracy for CCI to identify frail/ vulnerable patients (AUC 0,817). Conclusion In our sample, CCI was an independent factor for frailty/vulnerability classified through EFS. In fact, it had a high accuracy to identify those patients, with a high sensibility. This means that CCI could be used as an acceptable screening test. However, the prevalence of frail/vulnerable patients was low in our sample. A larger one will allow to determine confirm if certain comorbidities, such as diabetes mellitus, or variables related to dialysis technique, are correlated with frailty. Furthermore, a larger sample would be essential to confirm whether albumin is, after all, a good predictor of frailty in the PD population, which was not confirmed in this work.
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 | 0,002 |
| 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 | 0,000 | 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) | 0,003 | 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 ».