Risk prediction model for hypoglycemia in diabetic nephropathy patients on hemodialysis:A systematic review.

AIM This systematic review comprehensively examines predictive models for hypoglycemia risk during hemodialysis in patients with diabetic nephropathy. It aims to evaluate the accuracy and limitations of existing models and to provide a basis for optimizing and improving future research.

METHODS A search was conducted from the earliest available record to April 25, 2025, among the following databases Chinese National Knowledge Infrastructure (CNKI), Wan fang Database, VIP Database, SinoMed, PubMed, Web of Science, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Embase, and Cochrane Central Register of Controlled. Two researchers independently performed literature screening and data extraction and conducted quality assessments of the included predictive models.

RESULTS A total of 6 papers involving 6 models were included, with outcome event rates ranging from 8% to 21.6%. All models included reported the area under the curve (AUC) and model calibration. The predictors that appeared more frequently in the models were mainly body mass index (BMI), disease duration, and coefficient of variation of blood glucose (CVBG). The results of the risk of bias assessment tool for prediction model showed that the overall applicability of the included risk prediction models was good, but the risk of bias was high.

CONCLUSION Research on the risk prediction model for hypoglycemia in diabetic nephropathy patients on hemodialysis are still in early stages, and a all existing models have methodological limitations. Future research is recommended to validate, refine, and analyze the impact of existing models. The validity and feasibility of the model in clinical practice will be further evaluated in terms of predictor selection, model evaluation, and model validation.