부산대학교 도서관

Aims Design Methods Results Conclusions Impact Patient or Public Contribution Reporting Method This study aimed to construct a nomogram for predicting the risk of cognitive frailty in patients on maintenance haemodialysis.An explorative cross‐sectional design was adopted.From April 2022 to July 2022, 496 participants were recruited from five haemodialysis centres in Qingdao, Shandong Province, China. Participants with cognitive frailty were screened by Frailty Phenotype scale and Mini‐Mental State Examination. Least Absolute Shrinkage and Selection Operator (LASSO) regression and multivariate logistic regression were utilized to determine predictors. The predictive performance of the nomogram was validated by calibration and discrimination. Decision curve analysis was used to assess clinical utility. Internal validation was implemented using 1000 bootstrap samples to mitigate overfitting.The prevalence of cognitive frailty was 17.5% (n = 87). Six risk predictors, namely health empowerment, alexithymia, age, educational level, marital status and dialysis vintage, were screened and used to develop a nomogram model. The nomogram had satisfactory discrimination and calibration, and decision curve analysis revealed considerable clinical utility.A nomogram incorporated with the six risk predictors was developed, and it exhibited excellent prediction performance. The nomogram may strengthen the effective screening of patients at high risk of cognitive frailty.This study established a tool for healthcare staff to predict cognitive frailty probability and identify risk factors in patients on maintenance haemodialysis. The nomogram can meet the needs of personalized care and precision medicine simultaneously.Data were collected from patients on maintenance haemodialysis by using questionnaire survey.STROBE checklist was used. [ABSTRACT FROM AUTHOR]