Intramural Research Program, National Institute on Drug Abuse, 251 Bayview Blvd., Suite 200, Baltimore, MD, 21224, USA.
Department of Psychology, Hartwick College, Oneonta, NY, 13820, USA.
Psychopharmacology (Berl). 2020 Nov;237(11):3369-3381. doi: 10.1007/s00213-020-05618-5. Epub 2020 Sep 29.
Many people being treated for opioid use disorder continue to use drugs during treatment. This use occurs in patterns that rarely conform to well-defined cycles of abstinence and relapse. Systematic identification and evaluation of these patterns could enhance analysis of clinical trials and provide insight into drug use.
To evaluate such an approach, we analyzed patterns of opioid and cocaine use from three randomized clinical trials of contingency management in methadone-treated participants.
Sequences of drug test results were analyzed with unsupervised machine-learning techniques, including hierarchical clustering of categorical results (i.e., whether any samples were positive during each week) and K-means longitudinal clustering of quantitative results (i.e., the proportion positive each week). The sensitivity of cluster membership as an experimental outcome was assessed based on the effects of contingency management. External validation of clusters was based on drug craving and other symptoms of substance use disorder.
In each clinical trial, we identified four clusters of use patterns, which can be described as opioid use, cocaine use, dual use (opioid and cocaine), and partial/complete abstinence. Different clustering techniques produced substantially similar classifications of individual participants, with strong above-chance agreement. Contingency management increased membership in clusters with lower levels of drug use and fewer symptoms of substance use disorder.
Cluster analysis provides person-level output that is more interpretable and actionable than traditional outcome measures, providing a concrete answer to the question of what clinicians can tell patients about the success rates of new treatments.
许多正在接受阿片类药物使用障碍治疗的人在治疗过程中仍继续使用药物。这种使用模式很少符合明确的戒断和复发周期。系统地识别和评估这些模式可以增强临床试验的分析，并深入了解药物使用情况。
为了评估这种方法，我们分析了三种接受美沙酮治疗的参与者接受条件管理的随机临床试验中阿片类药物和可卡因使用的模式。
使用无监督机器学习技术分析药物测试结果的序列，包括分类结果的层次聚类（即每周是否有任何样本呈阳性）和定量结果的 K 均值纵向聚类（即每周阳性的比例）。根据条件管理的效果评估集群成员身份作为实验结果的敏感性。基于药物渴求和其他物质使用障碍症状对集群进行外部验证。
在每项临床试验中，我们确定了四种使用模式的集群，可以描述为阿片类药物使用、可卡因使用、双重使用（阿片类药物和可卡因）和部分/完全戒断。不同的聚类技术对个体参与者的分类基本相似，具有很强的超概率一致性。条件管理增加了使用药物水平较低且物质使用障碍症状较少的集群成员资格。
聚类分析提供了比传统结局指标更具可解释性和可操作性的个体水平输出，为临床医生可以向患者提供有关新治疗方法成功率的具体答案。
