Department of Pharmaceutical Outcomes & Policy, University of Florida, Gainesville, FL, USA.
Department of Mechanical and Industrial Engineering, Northeastern University, Boston, MA, USA.
Comput Biol Med. 2024 Jul;177:108493. doi: 10.1016/j.compbiomed.2024.108493. Epub 2024 Apr 22.
Buprenorphine is an effective evidence-based medication for opioid use disorder (OUD). Yet premature discontinuation undermines treatment effectiveness, increasing the risk of mortality and overdose. We developed and evaluated a machine learning (ML) framework for predicting buprenorphine care discontinuity within 12 months following treatment initiation.
This retrospective study used United States (US) 2018-2021 MarketScan commercial claims data of insured individuals aged 18-64 who initiated buprenorphine between July 2018 and December 2020 with no buprenorphine prescriptions in the previous six months. We measured buprenorphine prescription discontinuation gaps of ≥30 days within 12 months of initiating treatment. We developed predictive models employing logistic regression, decision tree classifier, random forest, extreme gradient boosting, Adaboost, and random forest-extreme gradient boosting ensemble. We applied recursive feature elimination with cross-validation to reduce dimensionality and identify the most predictive features while maintaining model robustness. For model validation, we used several statistics to evaluate performance, such as C-statistics and precision-recall curves. We focused on two distinct treatment stages: at the time of treatment initiation and one and three months after treatment initiation. We employed SHapley Additive exPlanations (SHAP) analysis that helped us explain the contributions of different features in predicting buprenorphine discontinuation. We stratified patients into risk subgroups based on their predicted likelihood of treatment discontinuation, dividing them into decile subgroups. Additionally, we used a calibration plot to analyze the reliability of the models.
A total of 30,373 patients initiated buprenorphine and 14.98% (4551) discontinued treatment. C-statistic varied between 0.56 and 0.76 for the first-stage models including patient-level demographic and clinical variables. Inclusion of proportion of days covered (PDC) measured after one month and three months following treatment initiation significantly increased the models' discriminative power (C-statistics: 0.60 to 0.82). Random forest (C-statistics: 0.76, 0.79 and 0.82 with baseline predictors, one-month PDC and three-months PDC, respectively) outperformed other ML models in discriminative performance in all stages (C-statistics: 0.56 to 0.77). Most influential risk factors of discontinuation included early stage medication adherence, age, and initial days of supply.
ML algorithms demonstrated a good discriminative power in identifying patients at higher risk of buprenorphine care discontinuity. The proposed framework may help healthcare providers optimize treatment strategies and deliver targeted interventions to improve buprenorphine care continuity.
丁丙诺啡是一种有效的阿片类药物使用障碍（OUD）循证药物。然而，过早停药会降低治疗效果，增加死亡率和过量用药的风险。我们开发并评估了一种机器学习（ML）框架，用于预测治疗开始后 12 个月内丁丙诺啡治疗的中断情况。
这项回顾性研究使用了美国（US）2018-2021 年市场扫描商业索赔数据，该数据来自于 2018 年 7 月至 2020 年 12 月期间接受丁丙诺啡治疗且在过去六个月内没有丁丙诺啡处方的 18-64 岁有保险的个体。我们测量了治疗开始后 12 个月内≥30 天的丁丙诺啡处方停药间隙。我们采用逻辑回归、决策树分类器、随机森林、极端梯度提升、自适应增强和随机森林-极端梯度提升集成来开发预测模型。我们应用递归特征消除和交叉验证来降低维度并识别最具预测性的特征，同时保持模型的稳健性。对于模型验证，我们使用了几种统计方法来评估性能，如 C 统计量和精度-召回曲线。我们专注于两个不同的治疗阶段：治疗开始时和治疗开始后一个月和三个月。我们使用了 SHapley Additive exPlanations（SHAP）分析，这有助于我们解释不同特征在预测丁丙诺啡停药方面的贡献。我们根据他们停药的可能性将患者分为风险亚组，将他们分为十分位亚组。此外，我们还使用校准图来分析模型的可靠性。
共有 30373 名患者开始接受丁丙诺啡治疗，其中 14.98%（4551 人）停止治疗。包括患者水平的人口统计学和临床变量在内的第一阶段模型的 C 统计量在 0.56 到 0.76 之间。在治疗开始后一个月和三个月测量的覆盖天数（PDC）的比例纳入后，模型的判别能力显著提高（C 统计量：0.60 到 0.82）。随机森林（C 统计量：0.76、0.79 和 0.82，分别为具有基线预测因子、一个月 PDC 和三个月 PDC）在所有阶段的判别性能（C 统计量：0.56 至 0.77）均优于其他 ML 模型。停药的最主要危险因素包括早期药物依从性、年龄和初始供应天数。
ML 算法在识别丁丙诺啡治疗中断风险较高的患者方面表现出良好的判别能力。所提出的框架可以帮助医疗保健提供者优化治疗策略，并提供针对性的干预措施，以提高丁丙诺啡治疗的连续性。
