Construction of a nomogram prediction model for individualized prediction of the risk of left ventricular diastolic dysfunction in maintenance hemodialysis patients

Objective: To explore the influencing factors of left ventricular diastolic dysfunction (LVDD) in maintenance hemodialysis (MHD) patients and construct a nomogram prediction model. Methods: Data was collected from 357 patients who received MHD treatment in our hospital from April 2022 to December 2024. According to a 7:3 ratio, the patients were grouped into a modeling group of 250 cases and a validation group of 107 cases. The modeling group was grouped into LVDD group of 61 cases and non LVDD group of 189 cases based on whether LVDD occurred. Multivariate logistic regression was used to analyze the risk predictive factors of LVDD in MHD patients in the modeling group. R software was used to draw nomograms. The calibration curve and Hosmer-Lemeshow goodness of fit test were used to evaluate the calibration of the nomogram. The receiver operating characteristic (ROC) curve was used to evaluate the discriminative power of the nomogram. Clinical decision curve analysis (DCA) was used to evaluate the clinical utility of nomograms. Results: = 0.317 and 0.320, and the AUC of the ROC curve was 0.922 and 0.896, indicating good calibration and discrimination ability of the model. When the high-risk threshold probabilities of the modeling group and validation group were within the range of 0.02-0.98 and 0.04-0.86, the net benefit of using a nomogram prediction model to make treatment decisions by clinical physicians was high. Conclusion: The nomogram prediction model constructed in this study can help clinicians identify LVDD high-risk patients in MHD and improve management strategies.