Development and validation of a prediction nomogram for depressive symptoms in gout patients.

ABSTRACT

Objective: The objective of the study was to explore the risk factors for 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 the convenience sampling method. The General Information Questionnaire, Self-Rating Depression Scale, Gout Knowledge Questionnaire, Self-Efficacy Scale for Managing Chronic Disease (SEMCD), and Social Support Rating Scale were used to conduct the survey. Univariate and multivariate logistic regression analyses were used to establish a depression risk prediction model and construct a nomogram. The bootstrap method was used to verify the performance of the model. Results: 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, chronic arthritis period, knowledge of gout, and social support were protective factors for post-gout depression (p < 0.05). The calibration (χ2 = 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. Conclusion: 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 for predicting the occurrence of depressive symptoms in gout patients.