Optimizing methadone dose adjustment in patients with opioid use disorder - Liu, Po-Shen; Kuo, Teng-Yao; Chen, I-Chun; Lee, Shu-Wua; Chang, Ting-Gang; Chen, Hou-Liang; Chen, Jun-Peng (Frontiers in Psychiatry, 2024)
Introduction: Opioid use disorder is a cause for concern globally. This study aimed to optimize methadone dose adjustments using mixed modeling and machine learning. Methods: This retrospective study was conducted at Taichung Veterans General Hospital between January 1, 2019, and December 31, 2020. Overall, 40,530 daily dosing records and 1,508 urine opiate test results were collected from 96 patients with opioid use disorder. A two-stage approach was used to create a model of the optimized methadone dose. In Stage 1, mixed modeling was performed to analyze the association between methadone dose, age, sex, treatment duration, HIV positivity, referral source, urine opiate level, last methadone dose taken, treatment adherence, and likelihood of treatment discontinuation. In Stage 2, machine learning was performed to build a model for optimized methadone dose. Results: Likelihood of discontinuation was associated with reduced methadone doses (β=0.002, 95% CI=0.000-0.081). Correlation analysis between the methadone dose determined by physicians and the optimized methadone dose showed a mean correlation coefficient of 0.995±0.003, indicating that the difference between the methadone dose determined by physicians and that determined by the model was within the allowable range (P<0.001). Conclusions: We developed a model for methadone dose adjustment in patients with opioid use disorders. By integrating urine opiate levels, treatment adherence, and likelihood of treatment discontinuation, the model could suggest automatic adjustment of the methadone dose, particularly when face-to-face encounters are impractical.