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Development and validation of a prediction nomogram for depressive symptoms in gout patients
Xinyi Hao1,2, Aiping Wang1
1Public Service Department, The First Hospital of China Medical University, Shenyang, China.
Insights
Depressive symptoms are common in gout patients, influenced by factors like gender and disease severity. A validated nomogram model aids in predicting depression risk for better patient management.
Area of Science:
- Rheumatology
- Psychiatry
- Medical Informatics
Background:
- Gout is a common inflammatory arthritis with significant impact on patients' quality of life.
- Depressive symptoms are frequently observed in patients with chronic conditions, including gout.
- Identifying risk factors and predictive models for depression in gout is crucial for timely intervention.
Purpose of the Study:
- To explore risk factors associated with depressive symptoms in patients diagnosed with gout.
- To develop and validate a nomogram prediction model for depressive symptoms in gout patients.
Main Methods:
- A cross-sectional study involving 469 gout patients from Northeast China.
- Utilized questionnaires for demographics, depression (Self-Rating Depression Scale), gout knowledge, self-efficacy, and social support.
- Employed logistic regression for risk factor analysis and nomogram construction, validated using the bootstrap method.
Main Results:
- The prevalence of depressive symptoms among gout patients was 25.16%.
- Independent risk factors for depression included male gender, presence of tophi, acute attack phase, limited gout knowledge, and higher attack frequency/duration.
- Female gender, interictal/chronic arthritis periods, gout knowledge, and social support acted as protective factors.
Conclusions:
- Depressive symptoms present a significant challenge in gout patient populations.
- The developed nomogram model demonstrates good performance in predicting depression risk in gout patients.
- The model's findings highlight the importance of considering clinical, knowledge-based, and psychosocial factors in managing gout-related depression.
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.
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