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Parallel modeling of pain and depression in prediction of relapse during buprenorphine and naloxone treatment: A finite mixture model.
Drug and Alcohol Dependence ( IF 3.9 ) Pub Date : 2020-02-26 , DOI: 10.1016/j.drugalcdep.2020.107940
Noel A Vest 1 , Sterling McPherson 2 , G Leonard Burns 1 , Sarah Tragesser 1
Affiliation  

BACKGROUND Relapse is common in treatment for opioid use disorders (OUDs). Pain and depression often co-occur during OUD treatment, yet little is known about how they influence relapse among patients with a primary diagnosis of prescription opioid use disorder (POUD). Advanced statistical analyses that can simultaneously model these two conditions may lead to targeted clinical interventions. METHOD The objective of this study was to utilize a discrete survival analysis with a growth mixture model to test time to prescription opioid relapse, predicted by parallel growth trajectories of depression and pain, in a clinical sample of patients in buprenorphine/naloxone treatment. The latent class analysis characterized heterogeneity with data collected from the National Institute of Drug Abuse Clinical Trials Network project (CTN-0030). RESULTS Results suggested that a 4-class solution was the most parsimonious based on global fit indices and clinical relevance. The 4 classes identified were: 1) low relapse, 2) high depression and moderate pain, 3) high pain, and 4) high relapse. Odds ratios for time-to-first use indicated no statistically significant difference in time to relapse between the high pain and the high depression classes, but all other classes differed significantly. CONCLUSION This is the first longitudinal study to characterize the influence of pain, depression, and relapse in patients receiving buprenorphine and naloxone treatment. These results emphasize the need to monitor the influence of pain and depression during stabilization on buprenorphine and naloxone. Future work may identify appropriate interventions that can be introduced to extend time-to-first prescription opioid use among patients.

中文翻译:

丁丙诺啡和纳洛酮治疗期间预测复发的疼痛和抑郁并行模型:有限混合模型。

背景技术复发在阿片类药物使用障碍(OUD)的治疗中很常见。OUD 治疗期间疼痛和抑郁经常同时发生,但对于初步诊断为处方阿片类药物使用障碍 (POUD) 的患者如何影响复发,人们知之甚少。可以同时模拟这两种情况的先进统计分析可能会导致有针对性的临床干预。方法 本研究的目的是利用生长混合模型的离散生存分析来测试处方阿片类药物复发的时间,通过抑郁和疼痛的平行生长轨迹预测丁丙诺啡/纳洛酮治疗患者的临床样本。潜在类别分析利用从国家药物滥用研究所临床试验网络项目 (CTN-0030) 收集的数据来表征异质性。结果结果表明,根据全局拟合指数和临床相关性,4 类解决方案是最简约的。确定的 4 个类别是:1) 低复发,2) 高度抑郁和中度疼痛,3) 高度疼痛,4) 高复发。首次使用时间的优势比表明,​​高痛和高度抑郁类别之间的复发时间没有统计学上的显着差异,但所有其他类别均存在显着差异。结论 这是第一项描述疼痛、抑郁和复发对接受丁丙诺啡和纳洛酮治疗的患者的影响的纵向研究。这些结果强调需要监测稳定期间疼痛和抑郁对丁丙诺啡和纳洛酮的影响。未来的工作可能会确定适当的干预措施,以延长患者首次处方阿片类药物使用的时间。
更新日期:2020-02-26
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