A nomogram for predicting poor sleep quality in patients with systemic lupus erythematosus.

Objective: To construct a nomogram for poor sleep quality in patients with systemic lupus erythematosus (SLE). Methods: Clinical data from 218 SLE patients who visited a tertiary hospital's rheumatology and immunology department in Chengdu, Sichuan Province, China, between 2021 and 2022 were analyzed. LASSO analysis and multivariate logistic regression were used to identify independent risk factors, and a nomogram was used to integrate and model the various risk factors. The model was evaluated using receiver operating characteristic curves, calibration curves, and decision curve analysis (DCA). Internal validation was conducted using the bootstrap method, and the clinical impact curve (CIC) was used to assess the clinical effectiveness of the predictive model. Results: In total, 104 patients (47.7%) had poor sleep quality, while 114 patients did not have poor sleep quality (52.3%). The nomogram for predicting poor sleep quality in patients had an area under the curve of 0.789, a sensitivity of 51.92%, and a specificity of 93.86%. The calibration curve closely approximated the ideal curve; DCA showed a threshold probability of 35%; the C-index was 0.789; and the CIC showed a threshold probability of 60%. These results indicate that the nomogram has good predictive accuracy and clinical utility. Conclusion: We constructed and validated a nomogram for poor sleep quality in patients with SLE, providing a convenient and reliable tool for the clinical prediction of poor sleep quality in these patients. Further multicenter studies are warranted to validate these findings and further elucidate the underlying mechanisms of sleep disturbances in SLE.