Frontiers in endocrinology · 2025
Prediction model for cognitive impairment in maintenance hemodialysis patients: the role of diabetes.
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Xu Jia, Li Hao, Qi Hualin, Zhao Xinhui
Аннотация
OBJECTIVE: The aim of this study was to identify risk factors for cognitive impairment (CI) in maintenance hemodialysis (MHD) patients and to develop a predictive model.
METHODS: A total of 151 MHD patients from our hospital were recruited between July 2020 and April 2021. Sociodemographic, clinical, and laboratory data were collected. Cognitive function was assessed using the Mini-Mental State Examination (MMSE), whereas physical performance was evaluated using handgrip strength, the Timed Up and Go Test (TUGT), and 4-m gait speed. Univariate and multivariate logistic regression analyses identified risk factors, which were used to develop original and simplified predictive models.
RESULTS: CI was present in 43 patients (28.5%). The simplified model demonstrated discriminatory ability comparable to that of the original model (AUC: 0.737; 95% CI: 0.648-0.818) and was easier to use. A robust nomogram was developed on the basis of the simplified model. Decision curve analysis (DCA) confirmed the clinical utility of both models. Diabetes was identified as an independent risk factor, whereas dialysis duration was not associated with CI.
CONCLUSIONS: This study provides a simple predictive model for CI in MHD patients, which could aid in clinical decision-making.
METHODS: A total of 151 MHD patients from our hospital were recruited between July 2020 and April 2021. Sociodemographic, clinical, and laboratory data were collected. Cognitive function was assessed using the Mini-Mental State Examination (MMSE), whereas physical performance was evaluated using handgrip strength, the Timed Up and Go Test (TUGT), and 4-m gait speed. Univariate and multivariate logistic regression analyses identified risk factors, which were used to develop original and simplified predictive models.
RESULTS: CI was present in 43 patients (28.5%). The simplified model demonstrated discriminatory ability comparable to that of the original model (AUC: 0.737; 95% CI: 0.648-0.818) and was easier to use. A robust nomogram was developed on the basis of the simplified model. Decision curve analysis (DCA) confirmed the clinical utility of both models. Diabetes was identified as an independent risk factor, whereas dialysis duration was not associated with CI.
CONCLUSIONS: This study provides a simple predictive model for CI in MHD patients, which could aid in clinical decision-making.
Предметные рубрики MeSH
Risk FactorsCognitive DysfunctionRenal DialysisArea Under CurveLogistic ModelsMental Status and Dementia TestsHand StrengthExercise TestNomogramsDecision Support TechniquesClinical Decision-MakingPredictive Learning ModelsCross-Sectional StudiesHumansMaleFemaleMiddle AgedAgedDiabetic NephropathiesDiabetes MellitusPredictive Value of Tests