AUTHOR=Fang Yabin , Yang Kaiyi , Gao Xinyu , Gong Yiran , Deng Yaxin , Xu Xiang , Xu Jing , Yan Lei , Zeng Jinshu , Chen Shuqiang 

TITLE=Integrating ultrasound and clinical risk factors to predict carotid plaque vulnerability in gout patients: a machine learning approach

JOURNAL=Frontiers in Medicine

VOLUME=Volume 12 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1556387

DOI=10.3389/fmed.2025.1556387

ISSN=2296-858X

ABSTRACT=ObjectivesThis study aimed to identify independent risk factors for carotid plaque (CP) vulnerability in patients with gout and to develop a predictive model incorporating both gout-specific and cardiovascular factors.MethodThis 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.ResultsTophi (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 number of gout flares in the last 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.ConclusionThe presence of tophi, positive PD signals, and increased number 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.