Machine Learning-Driven Analysis of Individualized Treatment Effects Comparing Buprenorphine and Naltrexone in Opioid Use Disorder Relapse Prevention.

IF 4.2 3区 医学 Q1 SUBSTANCE ABUSE Journal of Addiction Medicine Pub Date : 2024-09-01 Epub Date: 2024-05-22 DOI:10.1097/ADM.0000000000001313
Majid Afshar, Emma J Graham Linck, Alexandra B Spicer, John Rotrosen, Elizabeth M Salisbury-Afshar, Pratik Sinha, Matthew W Semler, Matthew M Churpek
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Abstract

Objective: A trial comparing extended-release naltrexone and sublingual buprenorphine-naloxone demonstrated higher relapse rates in individuals randomized to extended-release naltrexone. The effectiveness of treatment might vary based on patient characteristics. We hypothesized that causal machine learning would identify individualized treatment effects for each medication.

Methods: This is a secondary analysis of a multicenter randomized trial that compared the effectiveness of extended-release naltrexone versus buprenorphine-naloxone for preventing relapse of opioid misuse. Three machine learning models were derived using all trial participants with 50% randomly selected for training (n = 285) and the remaining 50% for validation. Individualized treatment effect was measured by the Qini value and c-for-benefit, with the absence of relapse denoting treatment success. Patients were grouped into quartiles by predicted individualized treatment effect to examine differences in characteristics and the observed treatment effects.

Results: The best-performing model had a Qini value of 4.45 (95% confidence interval, 1.02-7.83) and a c-for-benefit of 0.63 (95% confidence interval, 0.53-0.68). The quartile most likely to benefit from buprenorphine-naloxone had a 35% absolute benefit from this treatment, and at study entry, they had a high median opioid withdrawal score ( P < 0.001), used cocaine on more days over the prior 30 days than other quartiles ( P < 0.001), and had highest proportions with alcohol and cocaine use disorder ( P ≤ 0.02). Quartile 4 individuals were predicted to be most likely to benefit from extended-release naltrexone, with the greatest proportion having heroin drug preference ( P = 0.02) and all experiencing homelessness ( P < 0.001).

Conclusions: Causal machine learning identified differing individualized treatment effects between medications based on characteristics associated with preventing relapse.

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机器学习驱动的个性化治疗效果分析,比较丁丙诺啡和纳曲酮在阿片类药物使用障碍复发预防中的作用。
目的:一项比较缓释纳曲酮和舌下含服丁丙诺啡-纳洛酮的试验显示,随机接受缓释纳曲酮治疗的患者复发率更高。治疗效果可能因患者特征而异。我们假设因果机器学习可以识别每种药物的个性化治疗效果:这是一项多中心随机试验的二次分析,该试验比较了缓释纳曲酮与丁丙诺啡-纳洛酮在预防阿片类药物滥用复发方面的效果。利用所有试验参与者得出了三个机器学习模型,其中随机抽取 50%用于训练(n = 285),其余 50%用于验证。个体化治疗效果通过Qini值和c-for-benefit来衡量,无复发表示治疗成功。根据预测的个体化治疗效果将患者分为四等分,以检查特征和观察到的治疗效果之间的差异:表现最好的模型的 Qini 值为 4.45(95% 置信区间为 1.02-7.83),c-效益为 0.63(95% 置信区间为 0.53-0.68)。最有可能从丁丙诺啡-纳洛酮治疗中获益的四分位组的绝对获益率为 35%,在研究开始时,他们的阿片戒断评分中位数较高(P < 0.001),在过去 30 天内使用可卡因的天数多于其他四分位组(P < 0.001),酒精和可卡因使用障碍的比例最高(P ≤ 0.02)。预测第4四分位数的人最有可能从缓释纳曲酮中获益,其中有海洛因毒品偏好(P = 0.02)和无家可归经历(P < 0.001)的比例最高:因果机器学习根据与预防复发相关的特征确定了不同药物的个体化治疗效果。
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来源期刊
Journal of Addiction Medicine
Journal of Addiction Medicine 医学-药物滥用
CiteScore
6.10
自引率
9.10%
发文量
260
审稿时长
>12 weeks
期刊介绍: The mission of Journal of Addiction Medicine, the official peer-reviewed journal of the American Society of Addiction Medicine, is to promote excellence in the practice of addiction medicine and in clinical research as well as to support Addiction Medicine as a mainstream medical sub-specialty. Under the guidance of an esteemed Editorial Board, peer-reviewed articles published in the Journal focus on developments in addiction medicine as well as on treatment innovations and ethical, economic, forensic, and social topics including: •addiction and substance use in pregnancy •adolescent addiction and at-risk use •the drug-exposed neonate •pharmacology •all psychoactive substances relevant to addiction, including alcohol, nicotine, caffeine, marijuana, opioids, stimulants and other prescription and illicit substances •diagnosis •neuroimaging techniques •treatment of special populations •treatment, early intervention and prevention of alcohol and drug use disorders •methodological issues in addiction research •pain and addiction, prescription drug use disorder •co-occurring addiction, medical and psychiatric disorders •pathological gambling disorder, sexual and other behavioral addictions •pathophysiology of addiction •behavioral and pharmacological treatments •issues in graduate medical education •recovery •health services delivery •ethical, legal and liability issues in addiction medicine practice •drug testing •self- and mutual-help.
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