ORIGINAL RESEARCH article
Front. Med.
Sec. Rheumatology
Volume 12 - 2025 | doi: 10.3389/fmed.2025.1556387
This article is part of the Research TopicCardiovascular Comorbidities in Inflammatory Rheumatic DiseasesView all 10 articles
Integrating Ultrasound and Clinical Risk Factors to Predict Carotid Plaque Vulnerability in Gout Patients: A Machine Learning Approach
Provisionally accepted
Yabin Fang1Kaiyi Yang1Xinyu Gao1Yiran Gong1Yaxin Deng2Xiang Xu1Jing Xu1
Lei Yan1
Jinshu Zeng1
Shuqiang CHEN2*- 1First Affiliated Hospital of Fujian Medical University, Fuzhou, China
- 2Fujian Provincial Hospital, Fuzhou, Fujian Province, China
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Objectives: This study aimed to identify independent risk factors for carotid plaque (CP) vulnerability in patients with gout and to develop a predictive model incorporating both goutspecific and cardiovascular factors.Method: This study was designed as a retrospective cohort analysis that enrolled patients with newly diagnosed gout. These patients were retrospectively followed for a period of 1 to 2 years to evaluate the incidence of CP vulnerability. CP vulnerability was assessed using standardized ultrasound examinations and graded according to the Plaque Reporting and Data System (Plaque-RADS). Multivariate ordinal logistic regression analysis was employed to identify independent risk factors associated with CP vulnerability, with a particular focus on the impact of gout-related variables. Based on these results, a random forest prediction model was developed by integrating ultrasound imaging features and clinical variables to predict CP vulnerability.Results: Tophi (OR = 1.760, p = 0.009), power Doppler (PD) signal grades (Grade 2: OR = 1.540, p = 0.002; Grade 3: OR = 1.890, p = 0.001), and the frequency of gout flares in the past year (OR = 1.524, p = 0.001) were identified as independent risk factors for CP vulnerability. The random forest model showed excellent predictive performance (C-index = 0.997) and highlighted tophi, PD signal grades, and gout flare frequency as key gout-specific contributors to CP risk.The presence of tophi, elevated PD signals, and increased frequency of gout flares are significantly associated with CP vulnerability in patients with gout. The proposed machine learning model, integrating gout-specific and cardiovascular factors, provides a novel and effective approach for personalized risk stratification and management in gout patients, bridging the gap between rheumatic inflammation and cardiovascular risk assessment.
Keywords: Gout, risk stratification, ultrasound, carotid plaque, Inflammation, Prediction model, diagnosis
Received: 06 Jan 2025; Accepted: 26 May 2025.
Copyright: © 2025 Fang, Yang, Gao, Gong, Deng, Xu, Xu, Yan, Zeng and CHEN. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Shuqiang CHEN, Fujian Provincial Hospital, Fuzhou, 350001, Fujian Province, China
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