P1501KIDNEY FAILURE AND BRAIN FUNCTION DECLINE
Notice bibliographique
Résumé
Abstract Background and Aims: Chronic kidney disease (CKD) and cognitive impairment (CI) are two major health problems in an aging population, and both carry out negative prognostic implications. Prevalence in general population appears to be around 22.2% and recent analysis indicates that CI and frailty can be more prevalent in individuals undergoing hemodialysis (HD). Many causes can contribute to this higher prevalence, from vascular calcification to cerebral hypoperfusion, oxidative damage and uremic toxins. Both frailty and CI can lead to an increase morbimortality. Montreal Cognitive Assessment (MoCA) and Mini Mental State Examination (MMSE) are two screening instruments with a good application profile for cognitive evaluation, as well as the Frailty Clinical Scale (FCS). However, there are few studies using these scales on HD patients and demonstrating association between frailty, cognitive impairment and their clinical characteristics. The aim of this study is to investigate the prevalence of coexisting cognitive impairment and frailty in our center hemodialysis patients and its association with clinical characteristics and outcomes. Method: Thirty-two patients undergoing hospital hemodialysis program were assessed. The MoCA scale, MMSE and FCS were applied. Data were analyzed using appropriate statistical methods, using SPSS ® version 22.0. The significance level considered was 5%. Results: Thirty-two patients aged between 30 and 90 years were evaluated, with a mean of 61.63 years (SD ± 18.26), without gender predominance. The prevalence of deficits was 78.1% and 37,5% in MoCA and MMSE, respectively, without differences between gender. The prevalence of frailty (≥3) was 43.8%. Patients with deficit assessed by MoCA and MMSE were on average 15 years and 20 years older, respectively, than patients without deficit (p = 0.002). We found a statistically significant association between deficit measured by MMSE and frailty (p <.001), with higher prevalence of frailty (83.3%) in individuals with deficit compared to individuals without deficit, where the prevalence of frailty was 20.0%. The deficit assessed by MMSE was also associated with time on dialysis (p = .029). No statistically significant associations were detected between MoCA and frailty over time on dialysis or between deficit measured by MoCA and frailty. Regarding patients’ comorbidities, there were no statistically significant differences between deficit assessed by MoCA and MMSE and presence of diabetes mellitus, hypertension and dyslipidemia. Deficits assessed by MoCA, MMSE, and Frailty were not associated with phosphoremia and also there was no association between presence of significative hypotension episodes during HD and these scales. Dialysis efficacy (kt/v) was not statistically associated with MoCA and MMSE deficits. Similarly, no association was found between Kt / v and frailty. Conclusion: In our study, prevalence of CI and frailty in hemodialysis patients was high. Time on dialysis program was related in a statistically significant way with CI and there was higher prevalence of frailty in individuals with deficit measured by MMSE. However, we did not find a correlation between dialysis efficacy, comorbidities and vascular risk factors and cognitive deficit or frailty score. The epidemiology and natural history of cognitive impairment and its association with frailty are important to understand among patients on HD for early intervention and management.
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 | 0,001 | 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) | 0,004 | 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 ».