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.