Abstract: Objective  To construct a prediction model of sleep disorder (SD) in patients with maintenance hemodialysis (MHD) based on Lasso-Nomogram model, and to verify the efficacy of the prediction model.  Methods   A total of 198 patients with chronic renal failure (CRF) who underwent MHD in our hospital were selected and categorized into SD and non-SD groups according to whether SD occurred 6 months after MHD. We compared the clinical data of the two groups, analyzed the influencing factors for SD, and constructed a nomogram prediction model of SD according to the predictive factors.  Results   In the sixth month after MHD, 92 CRF patients developed SD, with the SD incidence of 46.46% (92/198). Logistic analysis showed that age (OR=2.152, 95% CI：1.246～3.718), skin itching (OR=6.209, 95% CI：2.051～18.796), depression (OR=3.715, 95% CI：1.531～9.013), urea clearance index (Kt/V) (OR=0.302, 95% CI：0.154～0.592), blood phosphorus (OR=2.274, 95% CI：1.236～4.185), calcium and phosphorus product (OR=3.210, 95% CI：1.517～6.792), serum copeptin (OR=6.816, 95% CI：2.317～20.048), α-amylase (OR=5.277, 95% CI：1.953～14.257), and 25-(OH)D3 (OR=0.381, 95% CI：0.186～0.780) were the influencing factors for SD (P<0.001). A nomogram prediction model of SD was constructed based on the nine indicators screened by Lasso and logistic analyses. Using this model, the area under the curve (AUC) for the occurrence of SD in CRF patients with MHD was 0.928 (95% CI：0.892～0.963), with the prediction sensitivity and specificity of 81.13% and 90.11% respectively.  Conclusion  This nomogram prediction model of SD in CRF patients with MHD based on the influencing factors for SD has higher predictive efficacy and better clinical effect in predicting SD risk.

Key words: Chronic renal failure, Maintenance hemodialysis, Sleep disorders, Influencing factor, Prediction model