An Integrated Risk Prediction Model for Gout Using Clinical Data, Ultrasound Features, and Deep Learning: A Retrospective Multicenter Study

Lishan Xiao,1,* Yizhe Zhao,2,3,* Yuchen Li,1 Mengmeng Yan,1 Yongming Liu,4 Changgui Li,5,6 Manhua Liu,2,3 Chunping Ning1 1Department of Ultrasound, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People’s Republic of China; 2The School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, People’s Republic of China; 3MoE Key Laboratory of Artificial Intelligence, Shanghai Jiao Tong University, Shanghai, People’s Republic of China; 4Department of Ultrasound, Shandong Province Chronic Disease Hospital, Qingdao, Shandong, People’s Republic of China; 5Shandong Provincial Key Laboratory of Metabolic Diseases and Qingdao Key Laboratory of Gout, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People’s Republic of China; 6Department of Endocrinology and Metabolism, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People’s Republic of China*These authors contributed equally to this workCorrespondence: Chunping Ning, Department of Ultrasound, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People’s Republic of China, Email [email protected]: To develop and validate a combined model for predicting gout risk by integrating ultrasound (US) features as novel risk factors with clinical data and predictions from deep learning (DL) models.Patients and Methods: This retrospective study included 609 cases who underwent first metatarsophalangeal (MTP1) joint US at two centers. Data from Center 1 were divided into a training group (70%, n = 355) and an internal testing cohort (ITC) (30%, n = 162). Data from Center 2 served as an external testing cohort (ETC) (n = 92). A DL diagnostic model based on MTP1 US images was developed to obtain diagnostic predictions. Clinical data, US features, and DL predictions were integrated, and logistic regression analysis was performed to identify independent risk factors. Various models were constructed (clinical, US, clinical-US, clinical-DL, and combined), and the best model was interpreted with a nomogram. Multicollinearity was assessed using the variance inflation factor. Model performance was evaluated using the receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA).Results: The combined model, incorporating clinical data (gender, serum uric acid [SUA]), US features (tophus, double contour sign (DCs), bone erosion), and DL predictions, exhibited the best performance. For the ITC, the area under the curve (AUC) and Brier scores were 0.904 (95% CI: 0.843~0.965) and 0.100 (0.066~0.140), respectively. For the ETC, they were 0.881 (95% CI: 0.815~0.947) and 0.160 (0.107~0.221). DCA confirmed the clinical utility of the combined nomogram.Conclusion: A nomogram was constructed based on seven risk predictors (gender, SUA, estimated glomerular filtration rate (eGFR), tophus, bone erosion, DCs, and DL prediction) to predict and quantify gout risk.Plain Language Summary: We developed a new method to predict gout risk by combining clinical data, ultrasound features, and deep learning technology.We created a simple-to-use scoring tool that doctors can use to assess the likelihood of patients developing gout.Three key ultrasound features (tophus, double contour sign, and bone erosion) can help predict gout risk. Keywords: gout, first metatarsophalangeal joint, ultrasonography, risk assessment, nomogram