Bridging the Medication Adherence Gap from Therapeutic Drug Monitoring: A Bayesian approach for Anti-Seizure Medications

Background: Adherence to antiseizure medications (ASMs) is crucial for the success of treatment. However, current recommendations for assessing medication adherence through therapeutic drug monitoring (TDM) may overlook individual patient characteristics, potentially leading to misjudgments. This study aims to evaluate the capability of a Bayesian approach in assessing adherence for 14 ASMs using TDM. Method: A Bayesian framework incorporating population pharmacokinetics was used to assess adherence using TDM data. Additionally, the impact of patient characteristics, concomitant medications, sampling times, and prior adherence probability was examined. Results: With essential patient information, such as age, weight, and scheduled dosing regimen, the Bayesian approach effectively assessed recent adherence for all investigated ASMs. The concentration thresholds varied by ASM and were influenced by patients' characteristics. To facilitate individual adherence evaluations, a web-based dashboard was developed. Conclusion: The integration of Bayesian methods with pharmacokinetics significantly enhances the reliability of TDM in assessing adherence to ASMs.