A nomogram to predict cognitive function impairment in patients with chronic kidney disease: A national cross-sectional survey.

BACKGROUND: Cognitive function impairment (CFI) is common in patients with chronic kidney disease (CKD) and significantly impacts treatment adherence and quality of life. This study aims to create a simplified nomogram for early CFI risk detection.

METHODS: Data were obtained from the National Health and Nutrition Examination Survey cycles spanning from 1999 to 2002 and again from 2011 to 2014. Stepwise logistic regression was used to select variables and construct a CFI risk prediction model. Furthermore, C-statistic and Brier Score (BS) assessed model performance. Additionally, Kaplan-Meier survival curves were utilised to assess risk group-death prognosis relationships.

RESULTS: Of the 545 participants in the CKD model development cohort, a total of 146 (26.8 %) had CFI. The final model included the variables of age, race, education, annual family income, body mass index, estimated glomerular filtration rate, serum albumin and uric acid. The model had a C-statistic of 0.808 (95 % confidence interval (CI): 0.769-0.847) and a BS of 0.149. Furthermore, the 5-fold cross-validation internal C-statistic was 0.764 (interquartile range: 0.763-0.807) and BS was 0.154. Upon external validation, the model's C-statistic decreased to 0.752 (95 % CI: 0.654-0.850) and its BS increased to 0.182. The Kaplan-Meier survival curves demonstrated that intermediate-to-high-risk participants had shorter overall survival time than low-risk participants (log-rank test: p = 0.00042).

CONCLUSIONS: This study established an effective nomogram for predicting CFI in patients with CKD, which can be used for the early detection of CFI and guide the treatment of patients with CKD.