Prediction Model of Hypoglycemia in Hemodialysis for Elderly Patients with Diabetic Kidney Disease:Construction and Validation
Objective To construct and validate a prediction model of hypoglycemia in hemodialysis for elderly patients with diabetic kidney disease(DKD),and to provide reference for the clinical formulation of individualized hypoglycemia prevention and control measures.Methods From June 2022 to June 2023,273 elderly DKD patients who underwent hemodialysis in the blood purification room of A Branch of a tertiary A hospital in Nanning City were selected for modeling by the convenience sampling method,and 65 elderly DKD patients in B Branch from July to December 2023 were selected for model validation by the convenience sampling method.Lasso regression was used to select characteristic variables,and Logistic regression was used to construct the model.The receiver operating characteristic curve,calibration curve and clinical decision curve were used to evaluate the prediction performance of the model.Results The incidence of hypoglycemia in 338 subjects was 43.5％.Body mass index,educational level,self-management,social support,duration of DKD,eating during dialysis,history of hypoglycemia in the past year,and blood glucose before dialysis were the influencing factors for dialysis hypoglycemia in elderly DKD patients(all P＜0.05).The area under the receiver operating characteristic curve of the constructed prediction model was 0.828,and the calibration curve showed that the prediction results were in good agreement with the reality(P＞0.05).It was verified that the overall accuracy of the prediction model was 80.0％,the threshold probability of the clinical decision curve was 7％-95％,and the model had a high clinical net benefit.Conclusion The nomogram model based on Lasso-logistic regression has good predictive value in clinical practice.
diabetic kidney diseasehemodialysishemodialysisprediction model
万方数据
维普
