Objective To analyze risk factors for sleep disorders in patients with chronic heart failure(CHF)and construct a nomogram prediction model.Methods Using simple random sampling,306 hospital-ized CHF patients meeting inclusion criteria were enrolled from four Grade A tertiary hospitals in Guangxi Zhuang Autonomous Region(two in Nanning,one each in Yulin and Guilin)between March 2023 and March 2024.LASSO regression analysis was initially employed for variable screening,followed by logistic regression to identify predictive variables for constructing the nomogram model.Model validation and performance evalua-tion were conducted using receiver operating characteristic(ROC)curves,calibration curves,and clinical decision curves,with internal validation performed through Bootstrap resampling(1 000 iterations).Results The incidence of sleep disorders among the 306 patients was 57.5%(176/306).Logistic regression analysis identified eight independent risk factors for sleep disorders in CHF patients(P＜0.05):age,education level,monthly house-hold income per capita,NYHA cardiac function classification,number of comorbidities,triglyceride levels,ano-rexia,and anxiety.The model demonstrated good discrimination for the AUC of 0.91(95%CI:0.77-0.88)and calibration consistency.Conclusion The prediction model established in this study shows good predictive performance,serving as a valuable reference for healthcare providers to early identify sleep disorders and im-plement preventive care strategies in patients with CHF.