Abstract
Aims: This study aimed to develop and evaluate an allopurinol adherence tool based on steady‐state oxypurinol plasma concentrations, allopurinol's active metabolite.
Methods: Plasma oxypurinol concentrations were simulated stochastically from an oxypurinol pharmacokinetic model for allopurinol doses of 100‐800 mg daily, accounting for differences in renal function, diuretic use and ethnicity. For each scenario, the 20th percentile for peak and trough concentrations defined the adherence threshold, below which imperfect adherence was assumed. Predictive performance was evaluated using both simulated low adherence and against data from 146 individuals with paired oxypurinol plasma concentrations and adherence measures. Sensitivity and specificity (S&S), negative and positive predictive values (NPV, PPV) and receiver operating characteristic (ROC) area under the curve (AUC) were determined. The predictive performance of the tool was evaluated using adherence data from an external study (CKD‐FIX).
Results: The allopurinol adherence tool produced S&S values for trough thresholds of 89‐98% and 76‐84%, respectively, and 90%‐98% and 76‐83% for peak thresholds. PPV and NPV were 79‐84% and 88‐94%, respectively, for trough and 80‐85% and 89‐98%, respectively, for peak concentrations. The ROC AUC values ranged from 0.84 to 0.88 and from 0.86 to 0.89 for trough and peak concentrations, respectively. S&S values for the external evaluation were found to be 75.8% and 86.5%, respectively, producing an ROC AUC of 0.8113.
Conclusion: A tool to identify people with gout who require additional support to maintain adherence using plasma oxypurinol concentrations was developed and evaluated. The predictive performance of the tool is suitable for adherence screening in clinical trials and may have utility in some clinical practice settings.