Development and validation of a prediction nomogram for depressive symptoms in gout patients - Hao, Xinyi; Wang, Aiping (Frontiers in Public Health, 2024)
Objective: To explore the risk factors of depressive symptoms in patients with gout, and to construct and validate a nomogram model.Methods: From October 2022 to July 2023, a total of 469 gout patients from a Class ⅲ Grade A hospital in Northeast China were selected as the research objects by convenience sampling method. The general information Questionnaire, Self-Rating Depression Scale, Gout Knowledge Questionnaire, Self-Efficacy Scale for Chronic and Social Support Rated were used Scale was used to conduct the survey. Univariate and multivariate Logistic regression analysis were used to establish a depression risk prediction model and construct a nomogram. Bootstrap method was used to verify the performance of the model.The detection rate of depressive symptoms in gout patients was 25.16%. Binary Logistic regression analysis showed that male, the number of tophi, acute attack period, lack of knowledge about gout, the number of attacks in the past year, and the duration of the last attack were independent risk factors for post-gout depression. Female, interictal period and chronic arthritis period, knowledge of gout and social support were protective factors for post-gout depression (P<0.05). The calibration (²=11.348, P=0.183, P > 0.05) and discrimination (AUC=0.858, 95%CI: 0.818-0.897) of the nomogram model for depressive symptoms in gout patients were good.The prevalence of depressive symptoms in gout patients is high, and it is affected by gender, current disease stage, number of tophi, gout knowledge level, the number of attacks in the past year, and the last attack days. The nomogram model is scientific and practical to predict the occurrence of depressive symptoms in gout patients.