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

被引:0
|
作者
Afshar, Majid [1 ]
Linck, Emma J. Graham [1 ]
Spicer, Alexandra B. [1 ]
Rotrosen, John [2 ]
Salisbury-Afshar, Elizabeth M. [1 ]
Sinha, Pratik [3 ]
Semler, Matthew W. [4 ]
Churpek, Matthew M. [1 ]
机构
[1] Univ Wisconsin, Sch Med & Publ Hlth, 1685 Highland Ave, Madison, WI 53705 USA
[2] NYU, Grossman Sch Med, New York, NY USA
[3] Washington Univ, Sch Med, St Louis, MO USA
[4] Vanderbilt Univ, Med Ctr, Nashville, TN USA
关键词
opioid use disorder; opioid treatment; heterogeneity of treatment effect; buprenorphine; naltrexone; EXTENDED-RELEASE NALTREXONE; RATING-SCALES; METHADONE; XBOT;
D O I
10.1097/ADM.0000000000001313
中图分类号
R194 [卫生标准、卫生检查、医药管理];
学科分类号
摘要
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.
引用
收藏
页码:511 / 519
页数:9
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