Conroy Andrew, Bilker Warren B, Miller Christopher J, Argoff Charles E, Bell Russell, Haythornthwaite Jennifer, Gewandter Jennifer S, Gilron Ian, Theken Katherine N, Farrar John T
Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA.
Department of Neurology, Albany Medical Center, Albany, NY, USA.
Pain Rep. 2025 Jun 5;10(4):e1281. doi: 10.1097/PR9.0000000000001281. eCollection 2025 Aug.
No prediction models exist for the success for buprenorphine initiation in opioid-naïve patients or in transition from other opioids in patients treated for chronic pain.
To create a prediction model for the successful use of buprenorphine to treat chronic pain.
Stepwise Akaike information criterion prediction modeling procedures were applied to a harmonized participant-level data set of 10 enriched enrollment randomized withdrawal clinical trials of buprenorphine submitted to the Food and Drug Administration. Available baseline factors and nine patient-reported outcomes were considered to predict success with the titration (10 studies) and maintenance of benefit after randomization (5 studies). Patient-reported outcomes were modeled separately given inconsistent use across studies.
No prediction model reached an area under the receiver operator curve ≥0.70, the threshold for clinical usefulness. Successful initiation or transition of buprenorphine was accomplished in 3541 of 6052 (58.7%) participants, and 614 of 877 (70.0%) completed the 12-week maintenance phase with no difference between opioid-experienced and opioid-naïve participants. Only a medical history of obesity and baseline pain were retained in the overall titration model and only baseline pain in the maintenance model. Only brief pain inventory and subject opioid withdrawal scores were retained in the titration subsets containing those measures.
No clinically useful prediction models of clinical benefit were identified, but a few covariates may be of interest in future studies of the initiation of buprenorphine in opioid-naïve patients or of transition from other opioids to buprenorphine. The lack of a predictor supports considering a trial of buprenorphine in clinically relevant scenarios for patients without known opioid use disorder, including careful monitoring and an a priori plan to deal with any problems that may occur.
对于阿片类药物初治患者使用丁丙诺啡起始治疗的成功情况，或慢性疼痛患者从其他阿片类药物转换使用丁丙诺啡的成功情况，目前尚无预测模型。
创建一个预测丁丙诺啡成功用于治疗慢性疼痛的模型。
将逐步Akaike信息准则预测建模程序应用于一个经过协调的参与者水平数据集，该数据集来自提交给美国食品药品监督管理局的10项丁丙诺啡强化入组随机撤药临床试验。考虑可用的基线因素和9项患者报告的结局，以预测滴定阶段（10项研究）的成功情况以及随机分组后获益的维持情况（5项研究）。由于各研究中使用情况不一致，对患者报告的结局进行了单独建模。
没有预测模型达到接受者操作特征曲线下面积≥0.70这一临床有用性阈值。6052名参与者中有3541名（58.7%）成功起始或转换使用丁丙诺啡，877名参与者中有614名（70.0%）完成了12周的维持阶段，有阿片类药物使用经验的参与者和阿片类药物初治参与者之间无差异。在总体滴定模型中仅保留了肥胖病史和基线疼痛，在维持模型中仅保留了基线疼痛。在包含这些测量指标的滴定亚组中，仅保留了简短疼痛量表和受试者阿片类药物戒断评分。
未识别出具有临床有用性的临床获益预测模型，但在未来针对阿片类药物初治患者起始使用丁丙诺啡或从其他阿片类药物转换为丁丙诺啡的研究中，一些协变量可能值得关注。缺乏预测指标支持在无已知阿片类药物使用障碍的患者的临床相关场景中考虑进行丁丙诺啡试验，包括仔细监测以及处理可能出现的任何问题的先验计划。
