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Abstract: 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. This is a secondary analysis of a multicenter randomized trial (CTN-0051, X:BOT) 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. 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. Related protocols: CTN-0051

Abstract:

Treatments for cannabis use disorder (CUD) have limited efficacy and little is known about who responds to existing treatments. Accurately predicting who will respond to treatment can improve clinical decision-making by allowing clinicians to offer the most appropriate level and type of care. This study aimed to determine whether multivariable/machine learning models can be used to classify CUD treatment responders vs. non-responders.

This secondary analysis used data from National Drug Abuse Treatment Clinical Trials Network (NIDA CTN) multi-site outpatient clinical trial (CTN-0053, Achieving Cannabis Cessation – Evaluating N-Acetylcysteine Treatment (ACCENT)). Adults with CUD (N=302) received 12 weeks of contingency management, brief cessation counseling, and were randomized to receive additionally either 1) N-Acetylcysteine or 2) placebo. Multivariable/machine learning models were used to classify treatment responders (i.e., two consecutive negative urine cannabinoid tests or a 50% reduction in days of use) versus non-responders using baseline demographic, medical, psychiatric, and substance use information.

Prediction performance for various machine learning and regression prediction models yielded area under the curves (AUCs) greater than 0.70 for four models (0.72-0.77), with support vector machine models having the highest overall accuracy (73%; 95% confidence interval [CI]: 68-78%) and AUC (0.77; 95% CI: 0.72, 0.83). Fourteen variables were retained in at least 3 of 4 top models, including demographic (ethnicity, education), medical (diastolic/systolic blood pressure, overall health, neurological diagnosis), psychiatric (depressive symptoms, generalized anxiety disorder, antisocial personality disorder), and substance use (tobacco smoker, baseline cannabinoid level, amphetamine use, age of experimentation with other substances, cannabis withdrawal intensity) characteristics.

Conclusions: Multivariable/machine learning models can improve upon chance prediction of treatment response to outpatient cannabis use disorder treatment, though further improvements in prediction performance are likely necessary for decisions about clinical care.

Related protocols: CTN-0053

Abstract:

This is the primary outcomes paper for CTN-0111.

The COVID-19 pandemic has exacerbated health inequities in the United States. People with unhealthy opioid use (UOU) may face disproportionate challenges with COVID-19 precautions, and the pandemic has disrupted access to opioids and UOU treatments. Unhealthy opioid use impairs the immunological, cardiovascular, pulmonary, renal, and neurological systems and may increase severity of outcomes for COVID-19. This study, NIDA-CTN-0111 (COVID-19 and Substance Misuse Case Identification Using Data Science: A Retrospective Cohort Study), aimed to apply machine learning techniques in order to explore clinical presentations of hospitalized patients with UOU and COVID-19 and to test the association between UOU and COVID-19 disease severity.

This retrospective, cross-sectional cohort study was conducted based on data from 4,110 electronic health record patient encounters at an academic health center in Chicago between January 1, 2020, and December 31, 2020. Inclusion criteria were unplanned admissions for patients =18 years of age; encounters were counted as COVID-19-positive if there was a positive test for COVID-19 or two COVID-19 ICD-10 codes recorded in the encounter. Using a predefined cutoff with optimal sensitivity and specificity to identify UOU, we ran a machine learning UOU classifier on the data for patients with COVID-19 to estimate the subcohort of patients with UOU. Topic modeling was used to explore and compare the clinical presentations documented for two subgroups: encounters with UOU and COVID-19 and those with no-UOU and COVID-19. Mixed effects logistic regression accounted for multiple encounters for some patients and tested the association between UOU and COVID-19 outcome severity. Severity was measured with three utilization metrics: low – unplanned admission, medium – unplanned admission and receiving mechanical ventilation, and high – unplanned admission with in-hospital death. All models controlled for age, sex, race/ethnicity, insurance status, and body mass index (BMI).

Topic modeling yielded ten topics per subgroup and highlighted unique comorbidities associated with UOU and COVID-19 (e.g., HIV) and no-UOU and COVID-19 (e.g., diabetes). In regression analysis, each incremental increase in the classifier’s predicted probability of UOU was associated with 1.16 higher odds of COVID-19 outcome severity (odds ratio 1.16, 95% CI 1.04-1.29, P=.009).

Conclusions: Among patients hospitalized with COVID-19, UOU is an independent risk factor associated with greater outcome severity, including in-hospital death. Social determinants of health and opioid-related overdose are unique comorbidities in the clinical presentation of the UOU patient subgroup. Additional research is needed on the role of COVID-19 therapeutics and inpatient management of acute COVID-19 pneumonia for patients with UOU. Further research is needed to test associations between expanded evidence-based harm reduction strategies for UOU and vaccination rates, hospitalizations, and risks for overdose and death among people with UOU and COVID-19. Machine learning techniques may offer more exhaustive means for cohort discovery and a novel mixed methods approach to population health.

Related protocols: CTN-0111