An allopurinol adherence tool using plasma oxypurinol concentrations

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.

Determinants of Achieving Serum Urate Goal with Treat‐to‐Target Urate‐Lowering Therapy in Gout

“…Direct measurement of medication or metabolites (ie, febuxostat or oxypurinol in allopurinol users) was not done for this study but would be relevant because such measures could be used to more precisely define adherence. 22 As anticipated in a clinical trial, reported adherence was high in the STOP Gout study, with rates approaching those defined using oxypurinol measurement among participants undergoing allopurinol escalation in a previous clinical trial. 22 Collectively, the factors comprising our predictive models demonstrated good discriminative capacity with C-statistics of 0.76 (models excluding adherence) to 0.79 (models including adherence).…”

“…22 As anticipated in a clinical trial, reported adherence was high in the STOP Gout study, with rates approaching those defined using oxypurinol measurement among participants undergoing allopurinol escalation in a previous clinical trial. 22 Collectively, the factors comprising our predictive models demonstrated good discriminative capacity with C-statistics of 0.76 (models excluding adherence) to 0.79 (models including adherence). Accurate prediction could facilitate a more personalized approach in gout management, including the addition of adjunctive therapies or tailoring medications used to treat comorbid disease that might impact SU concentration.…”

“…We measured adherence based on participant diaries, an approach that could be prone to misclassification. Direct measurement of medication or metabolites (ie, febuxostat or oxypurinol in allopurinol users) was not done for this study but would be relevant because such measures could be used to more precisely define adherence 22 . As anticipated in a clinical trial, reported adherence was high in the STOP Gout study, with rates approaching those defined using oxypurinol measurement among participants undergoing allopurinol escalation in a previous clinical trial 22 …”

The development and evaluation of dose‐prediction tools for allopurinol therapy (Easy‐Allo tools)

“…All participants on dialysis were excluded from the analysis. Data obtained from study visits where the criteria for suboptimal adherence to allopurinol therapy were based on a published allopurinol adherence screening tool 17 were excluded as well as visits with documented low adherence in the study records. The data associated with study visits were excluded if the following data were missing: the dates or times of oxypurinol or urate measurements, dosing information (dose or date), or plasma oxypurinol or serum urate concentrations.…”

Medication adherence research comes of age

“…the urine screen. The study highlights the challenges of detecting longitudinal medication-taking behaviour from scant cross-sectional clinical data.The topic of adherence screening was also presented by Smith-Diaz et al14 who developed and evaluated a screening tool to detect allopurinol suboptimal adherence in gout trials. The authors used stochastic simulations from a pharmacometric model for oxypurinol pharmacokinetics (the active metabolite of allopurinol) to determine the threshold plasma concentration below which suboptimal uratelowering taking can be concluded.…”