Predictors of Inadequate Serum Urate Response to Low-Dose Febuxostat in Male Patients with Gout

Abstract

Objective: This study aimed to understand predictors of inadequate response (IR) to low-dose febuxostat treatment based on clinical variables.

Methods: We pooled data from 340 patients of an observational cohort and two clinical trials who received febuxostat 20 mg/day for at least 3 months. IR was defined as failure to reach the target serum urate level (sUA<6 mg/dL) at any time point during 3 months treatment. The potential predictors associated with short- or mid-term febuxostat IR after pooling the three cohorts were explored using mixed-effect logistic analysis. Machine learning models were performed to evaluate the predictors for IR using the pooled data as the discovery set and validated in an external test set.

Results: Of the 340 patients, 68.9% and 51.8% were non-responders to low-dose febuxostat during short- and mid-term follow-up, respectively. Serum urate and triglyceride (TG) levels were significantly associated with febuxostat IR, but were also selected as significant features by LASSO analysis combined with age, BMI, and C-reactive protein (CRP). These five features in combination, using the best-performing stochastic gradient descent classifier, achieved an area under the receiver operating characteristic curve of 0.873 (95% CI [0.763, 0.942]) and 0.706 (95% CI [0.636, 0.727]) in the internal and external test sets, respectively, to predict febuxostat IR.

Conclusion: Response to low-dose febuxostat is associated with early sUA improvement in individual patients, as well as patient age, BMI, and levels of TG and CRP.

Keywords: febuxostat; gout; machine learning model; urate-lowering therapy.

Figure 1

Study flow diagram. CA-724 cohort, cohort evaluating the predictive ability of CA-724 on gout flares (#ChiCTR2100043573); chitosan cohort, cohort evaluating the efficacy and safety of chitosan oligosaccharide treatment (#ChiCTR2100042424); febuxostat cohort, cohort comparing the efficacy and safety of low-dose febuxostat and low-dose benzbromarone in patients with gout (#ChiCTR1800019352).

Figure 2

Variable selection and model performance. The area under the receiver operating characteristic curve (AUC) score of the 29 variables was determined using the LASSO model in the training set (A). Red dots represent the AUC score, gray lines represent the standard error, and vertical dotted lines represent the optimal values based on the minimum criteria. The upper abscissa is the number of nonzero coefficients in the model at this time, and the lower abscissa is log lambda, which is the tuning parameter used for the 10-fold cross-validation in the LASSO model. LASSO coefficient profiles of the 29 variables (B). The performance of each model and comparisons of the external test dataset (C). AUC: the area under the receiver operating characteristic curve.

Figure 3

Model performance and evaluation. The area under the receiver-operator characteristic curve on the internal validation and external test sets of stochastic gradient descent (SGD) classifier (A), the calibration plot of the SGD model in the test set (B), the decision curve (C), and the correlation analysis between the features (D).

Figure 4

Febuxostat inadequate response interpreted by the trained stochastic gradient descent model with Shapley additive explanations (SHAP). The global importance of five features based on the average SHAP value magnitude (A). A set of bee-swarm plots corresponding to feature summary (B). The vertical axis shows the sorted five features while the horizontal axis displays the impact on the model output. Each data point represents a predicted output and the color indicates the feature values.