Public Service Department, The First Hospital of China Medical University, Shenyang, China.
Nursing Department, Peking Union Medical College Hospital, Peking, China.
Front Public Health. 2024 Jul 19;12:1356814. doi: 10.3389/fpubh.2024.1356814. eCollection 2024.
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.
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.
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 ( < 0.05). The calibration (χ = 11.348, = 0.183, > 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 for predicting the occurrence of depressive symptoms in gout patients.
本研究旨在探讨痛风患者抑郁症状的危险因素，并构建和验证列线图模型。
采用便利抽样法，于 2022 年 10 月至 2023 年 7 月选取中国东北地区一家三级甲等医院的 469 例痛风患者作为研究对象。采用一般资料问卷、抑郁自评量表、痛风知识问卷、慢性病自我效能量表（SEMCD）和社会支持评定量表进行调查。采用单因素和多因素逻辑回归分析建立抑郁风险预测模型，并构建列线图。采用自举法验证模型的性能。
痛风患者抑郁症状检出率为 25.16%。二元逻辑回归分析显示，男性、痛风石数量、急性发作期、缺乏痛风相关知识、过去 1 年发作次数和上次发作持续时间是痛风后抑郁的独立危险因素；女性、间歇期、慢性关节炎期、痛风知识和社会支持是痛风后抑郁的保护因素（<0.05）。列线图模型预测痛风患者抑郁症状的校准（χ²=11.348，=0.183，>0.05）和区分度（AUC=0.858，95%CI：0.818-0.897）良好。
痛风患者抑郁症状的发生率较高，受性别、当前疾病阶段、痛风石数量、痛风知识水平、过去 1 年发作次数和上次发作持续时间的影响。该列线图模型可科学实用地预测痛风患者抑郁症状的发生。
