Establishment and Validation of Predictive Model of Tophus in Gout Patients

Abstract

(1) Background: A tophus is a clinical manifestation of advanced gout, and in some patients could lead to joint deformities, fractures, and even serious complications in unusual sites. Therefore, to explore the factors related to the occurrence of tophi and establish a prediction model is clinically significant. (2) Objective: to study the occurrence of tophi in patients with gout and to construct a predictive model to evaluate its predictive efficacy. (3) Methods: The clinical data of 702 gout patients were analyzed by using cross-sectional data of North Sichuan Medical College. The least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression were used to analyze predictors. Multiple machine learning (ML) classification models are integrated to analyze and identify the optimal model, and Shapley Additive exPlanations (SHAP) interpretation was developed for personalized risk assessment. (4) Results: Compliance of urate-lowering therapy (ULT), Body Mass Index (BMI), course of disease, annual attack frequency, polyjoint involvement, history of drinking, family history of gout, estimated glomerular filtration rate (eGFR), and erythrocyte sedimentation rate (ESR) were the predictors of the occurrence of tophi. Logistic classification model was the optimal model, test set area under curve (AUC) (95% confidence interval, CI): 0.888 (0.839–0.937), accuracy: 0.763, sensitivity: 0.852, and specificity: 0.803. (5) Conclusions: We constructed a logistic regression model and explained it with the SHAP method, providing evidence for preventing tophus and guidance for individual treatment of different patients.

Elevated serum IL-2 and Th17/Treg imbalance are associated with gout

“…The tophus demonstrates a multifaceted, well-organized chronic inflammatory tissue response to MSU monohydrate crystals, which involves both innate and adaptive immune cells [ 46 ]. And the formation of tophus can cause joint deformities, joint injury, fracture, and skin rupture or infection [ 47 ]. In our study, gout without tophus had significantly higher number of Th17 cells and Th17/Treg ratio than HCs, while gout with tophus and gout without tophus both exhibited lower percentage of Treg cells than HCs.…”

Country-specific determinants for COVID-19 case fatality rate and response strategies from a global perspective: an interpretable machine learning framework

“…SHAP provides valuable insights into a model's behaviour by overcoming the main drawback of inconsistency in classical global feature importance measures, minimizes the possibility of underestimating the importance of a feature with a certain attribution value, shows consistency and accuracy in its importance ordering, and interpreting the model's global behaviour while retaining local faithfulness. The overall importance of a feature was scored as the mean absolute value of all SHAP values for that feature, and we considered features scoring 0.1 or higher as important [26][27][28]. The association between CFR and each key feature was examined via partial dependence plots, which were adjusted for all other confounding variables.…”

Development of an interpretable machine learning model associated with genetic indicators to identify Yin-deficiency constitution

“…Based on the previous discovery, we used the five genetic indicators and applied four machine learning methods to construct predictive models of YinDC. Four machine learning methods were used including logistic regression, random forest, support vector machine (SVM) [ 23 ], and eXtreme Gradient Boosting (XGBoost) [ 24 ]. Furthermore, we used the Shapley Additive exPlanations (SHAP) interpretation tool [ 25 ] to provide an intuitive interpretation of the predictive models.…”