RESUMO
AIMS: To investigate the current situation of mental psychology and quality of life (QoL) in patients with inflammatory bowel disease (IBD) in China, and analyze the influencing factors. METHODS: A unified questionnaire was developed to collect clinical data on IBD patients from 42 hospitals in 22 provinces from September 2021 to May 2022. Multivariate Logistic regression analysis was conducted, and independent influencing factors were screened out to construct nomogram. The consistency index (C-index), receiver operating characteristic (ROC) curve, area under the ROC curve (AUC), calibration curve, and decision curve analysis (DCA) were used to evaluate the discrimination, accuracy, and clinical utility of the nomogram model. RESULTS: A total of 2478 IBD patients were surveyed, including 1371 patients with ulcerative colitis (UC) and 1107 patients with Crohn's disease (CD). Among them, 25.5%, 29.7%, 60.2%, and 37.7% of IBD patients had anxiety, depression, sleep disturbance and poor QoL, respectively. The proportion of anxiety, depression, and poor QoL in UC patients was significantly higher than that in CD patients (all p < 0.05), but there was no difference in sleep disturbance between them (p = 0.737). Female, higher disease activity and the first visit were independent risk factors for anxiety, depression and sleep disturbance in IBD patients (all p < 0.05). The first visit, higher disease activity, abdominal pain and diarrhea symptoms, anxiety, depression and sleep disturbance were independent risk factors for the poor QoL of patients (all p < 0.05). The AUC value of the nomogram prediction model for predicting poor QoL was 0.773 (95% CI: 0.754-0.792). The calibration diagram of the model showed that the calibration curve fit well with the ideal curve, and DCA showed that the nomogram model could bring clinical benefits. CONCLUSION: IBD patients have higher anxiety, depression, and sleep disturbance, which affect their QoL. The nomogram prediction model we constructed has high accuracy and performance when predicting QoL.