Cognitive Impairment in Patients on Maintenance Hemodialysis and Its Influencing Factors: a Multicenter Cross-sectional Study
Notice bibliographique
Résumé
Background Understanding the condition and influencing factors of cognitive impairment in maintenance hemodialysis (MHD) patients could signficantly enhance their quality of life while alleviating the burden on their families and society. Objective TO investigate the status of cognitive impairment in MHD patients and explore the possible influencing factors. Methods Using convenience sampling, we selected MHD patients from three hemodialysis centers (including the Department of Nephrology at the First Affiliated Hospital of Shihezi University, the Department of Nephrology at Shihezi People's Hospital, and the Langshen Hemodialysis Center) in Shihezi City between April 2023 and April 2024. We collected data on demographic characteristics, cognitive impairment levels, sleep quality, independent living abilities, serum levels of α-Klotho, β-Klotho, FGF-23, and other common laboratory indicators. Cognitive function was assessed with the Montreal Cognitive Assessment (MoCA), sleep quality was evaluated with the Athens Insomnia Scale (AIS-8), and independent living ability was assessed using the Functional Activities Questionnaire (FAQ). Serum levels of α-Klotho, β-Klotho, and FGF-23 were measured by the ELISA method. Univariate and multivariate Logistic regression analyses were performed to identify influencing factors, which were validated for their predictive value on cognitive impairment using the receiver operating characteristic (ROC) curve. A nomogram was subsequently plotted. Results A total of 276 MHD patients were surveyed, revealing a cognitive impairment incidence rate of 76.4% (211/276). Among these, 145 patients had mild cognitive impairment and 66 patients had moderate cognitive impairment. Nearly half of the patients exhibited suspected insomnia (21.4%) or confirmed insomnia (25.4%). Among the patients studied, 14.9% (41 out of 276) lacked the ability to live independently. The multivariate Logistic regression analysis indicated that age (OR=1.038, 95%CI=1.004-1.072) and sleep disorders (OR=1.179, 95%CI=1.051-1.322) were risk factors for cognitive impairment in MHD patients (P<0.05). High serum α-Klotho levels (OR=0.996, 95%CI=0.994-0.998), high serum β-Klotho levels (OR=0.750, 95%CI=0.661-0.852), and higher years of education (OR=0.800, 95%CI=0.699-0.915) were protective factors (P<0.05). The area under the ROC curve (AUC) showed that age (AUC=0.732, 95%CI=0.667-0.797), sleep disorder (AUC=0.710, 95%CI=0.638-0.783), α-Klotho (AUC=0.774, 95%CI=0.709-0.839), β-Klotho (AUC=0.741, 95%CI=0.663-0.819) and years of education (AUC=0.718, 95%CI=0.647-0.789) had predictive value for cognitive impairment in MHD patients. The combination of age, sleep disorder, serum α-Klotho, serum β-Klotho and years of education (P=-0.004×α-Klotho-0.287×β-Klotho+0.370×age-0.223×years of education +0.165×AIS-8 score+6.658) predicted the occurrence of MHD. The AUC of cognitive impairment was 0.894 (95%CI=0.851-0.937, P<0.001), the sensitivity was 82.9%, and the specificity was 78.5%. Conclusion The prevalence of cognitive impairment among MHD patients is substantially high, estimated at approximately 76%. Age, sleep disorders, years of education, and levels of α-Klotho and β-Klotho are important influencing factors. Medical staff and patients' families should raise awareness of cognitive impairment, actively screen and intervene in key patients to improve their quality of life and reduce the burden on their families and society.
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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 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».