Applying methods for personalized medicine to the treatment of alcohol use disorder

Beyond mediators: A critical review and methodological path forward for studying mechanisms in alcohol use treatment research

Understanding how treatments for alcohol use disorder (AUD) facilitate behavior change has long been recognized as an important area of research for advancing clinical care. However, despite decades of research, the specific mechanisms of change for most AUD treatments remain largely unknown because most prior work in the field has focused only on statistical mediation. Statistical mediation is a necessary but not sufficient condition to establish evidence for a mechanism of change. Mediators are intermediate variables that account statistically for the relationship between independent and dependent variables, whereas mechanisms provide more detailed explanations of how an intervention leads to a desired outcome. Thus, mediators and mechanisms are not equivalent. To advance mechanisms of behavior change research, in this critical review we provide an overview of methodological shortfalls of existing AUD treatment mechanism research and introduce an etiologically informed precision medicine approach that facilitates the testing of mechanisms of behavior change rather than treatment mediators. We propose a framework for studying mechanisms in alcohol treatment research that promises to facilitate our understanding of behavior change and precision medicine (i.e., for whom a given mechanism of behavior change operates and under what conditions). The framework presented in this review has several overarching goals, one of which is to provide a methodological roadmap for testing AUD recovery mechanisms. We provide two examples of our framework, one pharmacological and one behavioral, to facilitate future efforts to implement this methodological approach to mechanism research. The framework proposed in this critical review facilitates the alignment of AUD treatment mechanism research with current theories of etiologic mechanisms, precision medicine efforts, and cross-disciplinary approaches to testing mechanisms. Although no framework can address all the challenges related to mechanisms research, our goal is to help facilitate a shift toward more rigorous and falsifiable behavior change research.

Motivational interviewing for substance use reduction

BACKGROUND

Substance use is a global issue, with around 30 to 35 million individuals estimated to have a substance-use disorder. Motivational interviewing (MI) is a client-centred method that aims to strengthen a person's motivation and commitment to a specific goal by exploring their reasons for change and resolving ambivalence, in an atmosphere of acceptance and compassion. This review updates the 2011 version by Smedslund and colleagues.

OBJECTIVES

To assess the effectiveness of motivational interviewing for substance use on the extent of substance use, readiness to change, and retention in treatment.

SEARCH METHODS

We searched 18 electronic databases, six websites, four mailing lists, and the reference lists of included studies and reviews. The last search dates were in February 2021 and November 2022.

SELECTION CRITERIA

We included randomised controlled trials with individuals using drugs, alcohol, or both. Interventions were MI or motivational enhancement therapy (MET), delivered individually and face to face. Eligible control interventions were no intervention, treatment as usual, assessment and feedback, or other active intervention.

DATA COLLECTION AND ANALYSIS

We used standard methodological procedures expected by Cochrane, and assessed the certainty of evidence with GRADE. We conducted meta-analyses for the three outcomes (extent of substance use, readiness to change, retention in treatment) at four time points (post-intervention, short-, medium-, and long-term follow-up).

MAIN RESULTS

We included 93 studies with 22,776 participants. MI was delivered in one to nine sessions. Session durations varied, from as little as 10 minutes to as long as 148 minutes per session, across included studies. Study settings included inpatient and outpatient clinics, universities, army recruitment centres, veterans' health centres, and prisons. We judged 69 studies to be at high risk of bias in at least one domain and 24 studies to be at low or unclear risk. Comparing MI to no intervention revealed a small to moderate effect of MI in substance use post-intervention (standardised mean difference (SMD) 0.48, 95% confidence interval (CI) 0.07 to 0.89; I2 = 75%; 6 studies, 471 participants; low-certainty evidence). The effect was weaker at short-term follow-up (SMD 0.20, 95% CI 0.12 to 0.28; 19 studies, 3351 participants; very low-certainty evidence). This comparison revealed a difference in favour of MI at medium-term follow-up (SMD 0.12, 95% CI 0.05 to 0.20; 16 studies, 3137 participants; low-certainty evidence) and no difference at long-term follow-up (SMD 0.12, 95% CI -0.00 to 0.25; 9 studies, 1525 participants; very low-certainty evidence). There was no difference in readiness to change (SMD 0.05, 95% CI -0.11 to 0.22; 5 studies, 1495 participants; very low-certainty evidence). Retention in treatment was slightly higher with MI (SMD 0.26, 95% CI -0.00 to 0.52; 2 studies, 427 participants; very low-certainty evidence). Comparing MI to treatment as usual revealed a very small negative effect in substance use post-intervention (SMD -0.14, 95% CI -0.27 to -0.02; 5 studies, 976 participants; very low-certainty evidence). There was no difference at short-term follow-up (SMD 0.07, 95% CI -0.03 to 0.17; 14 studies, 3066 participants), a very small benefit of MI at medium-term follow-up (SMD 0.12, 95% CI 0.02 to 0.22; 9 studies, 1624 participants), and no difference at long-term follow-up (SMD 0.06, 95% CI -0.05 to 0.17; 8 studies, 1449 participants), all with low-certainty evidence. There was no difference in readiness to change (SMD 0.06, 95% CI -0.27 to 0.39; 2 studies, 150 participants) and retention in treatment (SMD -0.09, 95% CI -0.34 to 0.16; 5 studies, 1295 participants), both with very low-certainty evidence. Comparing MI to assessment and feedback revealed no difference in substance use at short-term follow-up (SMD 0.09, 95% CI -0.05 to 0.23; 7 studies, 854 participants; low-certainty evidence). A small benefit for MI was shown at medium-term (SMD 0.24, 95% CI 0.08 to 0.40; 6 studies, 688 participants) and long-term follow-up (SMD 0.24, 95% CI 0.07 to 0.41; 3 studies, 448 participants), both with moderate-certainty evidence. None of the studies in this comparison measured substance use at the post-intervention time point, readiness to change, and retention in treatment. Comparing MI to another active intervention revealed no difference in substance use at any follow-up time point, all with low-certainty evidence: post-intervention (SMD 0.07, 95% CI -0.15 to 0.29; 3 studies, 338 participants); short-term (SMD 0.05, 95% CI -0.03 to 0.13; 18 studies, 2795 participants); medium-term (SMD 0.08, 95% CI -0.01 to 0.17; 15 studies, 2352 participants); and long-term follow-up (SMD 0.03, 95% CI -0.07 to 0.13; 10 studies, 1908 participants). There was no difference in readiness to change (SMD 0.15, 95% CI -0.00 to 0.30; 5 studies, 988 participants; low-certainty evidence) and retention in treatment (SMD -0.04, 95% CI -0.23 to 0.14; 12 studies, 1945 participants; moderate-certainty evidence). We downgraded the certainty of evidence due to inconsistency, study limitations, publication bias, and imprecision.

AUTHORS' CONCLUSIONS

Motivational interviewing may reduce substance use compared with no intervention up to a short follow-up period. MI probably reduces substance use slightly compared with assessment and feedback over medium- and long-term periods. MI may make little to no difference to substance use compared to treatment as usual and another active intervention. It is unclear if MI has an effect on readiness to change and retention in treatment. The studies included in this review were heterogeneous in many respects, including the characteristics of participants, substance(s) used, and interventions. Given the widespread use of MI and the many studies examining MI, it is very important that counsellors adhere to and report quality conditions so that only studies in which the intervention implemented was actually MI are included in evidence syntheses and systematic reviews. Overall, we have moderate to no confidence in the evidence, which forces us to be careful about our conclusions. Consequently, future studies are likely to change the findings and conclusions of this review.

Who responds to a multi-component treatment for cannabis use disorder? Using multivariable and machine learning models to classify treatment responders and non-responders

BACKGROUND AND AIMS

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 versus non-responders.

METHODS

This secondary analysis used data from a National Drug Abuse Treatment Clinical Trials Network multi-site outpatient clinical trial in the United States. 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.

RESULTS

Prediction performance for various machine learning and regression prediction models yielded area under the curves (AUCs) >0.70 for four models (0.72-0.77), with support vector machine models having the highest overall accuracy (73%; 95% CI = 68-78%) and AUC (0.77; 95% CI = 0.72, 0.83). Fourteen variables were retained in at least three of four 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 on chance prediction of treatment response to outpatient cannabis use disorder treatment, although further improvements in prediction performance are likely necessary for decisions about clinical care.

New approaches to deep phenotyping in addictions

OBJECTIVE

The causes of substance use disorders (SUDs) are largely unknown and the effectiveness of their treatments is limited. One crucial impediment to research and treatment progress surrounds how SUDs are classified and diagnosed. Given the substantial heterogeneity among individuals diagnosed with a given SUD (e.g., alcohol use disorder [AUD]), identifying novel research and treatment targets and developing new study designs is daunting.

METHOD

In this article, we review and integrate two recently developed frameworks, the National Institute on Drug Abuse's Phenotyping Assessment Battery (NIDA PhAB) and the Hierarchical Taxonomy of Psychopathology (HiTOP), that hope to accelerate progress in understanding the causes and consequences of psychopathology by means of deep phenotyping, or finer-grained analysis of phenotypes.

RESULTS AND CONCLUSIONS

NIDA PhAB focuses on addiction-related processes across multiple units of analysis, whereas HiTOP focuses on clinical phenotypes and covers a broader range of psychopathology. We highlight that NIDA PhAB and HiTOP together provide deep and broad characterizations of people diagnosed with SUDs and complement each other in their efforts to address widely known limitations of traditional classification systems and their diagnostic categories. Next, we show how NIDA PhAB and HiTOP can be integrated to facilitate optimal rich phenotyping of addiction-related phenomena. Finally, we argue that such deep phenotyping promises to advance our understanding of the neurobiology of SUD and addiction, which will guide the development of personalized medicine and interventions. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Classifying risky cannabis involvement in young adults using the Marijuana Consequences Questionnaire (MACQ)

BACKGROUND

Despite the growing prevalence of cannabis use and associated consequences among U.S. young adults, relatively little is known about precisely what level of marijuana involvement may be problematic.

METHOD

With this study we sought to identify empirically-derived cut-scores for the Marijuana Consequences Questionnaire (MACQ) that would distinguish among levels of cannabis risk in a sample of young adult college students (N = 496). We also examined how these levels of cannabis risk corresponded to a variety of indicators of cannabis involvement, including frequency of use, intoxication, other measures of cannabis consequences, and indicators of more severe cannabis involvement (e.g., physiological dependence, loss of control over use, cannabis use disorder).

RESULTS

Receiver operating characteristic analyses yielded cutoffs that distinguished among three distinct levels of risk, "Low", "Moderate", and "High". These empirically derived cut scores showed strong overall differentiation among classifications, with good sensitivity and specificity. MACQ-based risk levels were validated across several indices of cannabis involvement. Cutoffs differed across genders.

CONCLUSIONS

Findings offer a new application for the MACQ, allowing for the identification of those at greatest risk. As such, this measure may be used to facilitate appropriately targeted intervention.

Individual differences in the effects of the ACTION-PAC intervention: an application of personalized medicine in the prevention and treatment of obesity

There is an increased interest in the use of personalized medicine approaches in the prevention or treatment of obesity, however, few studies have used these approaches to identify individual differences in treatment effects. The current study demonstrates the use of the predicted individual treatment effects framework to test for individual differences in the effects of the ACTION-PAC intervention, which targeted the treatment and prevention of obesity in a high school setting. We show how methods for personalized medicine can be used to test for significant individual differences in responses to an intervention and we discuss the potential and limitations of these methods. In our example, 25% of students in the preventive intervention, were predicted to have their BMI z-score reduced by 0.39 or greater, while at other end of the spectrum, 25% were predicted to have their BMI z-score increased by 0.09 or more. In this paper, we demonstrate and discuss the process of using methods for personalized medicine with interventions targeting adiposity and discuss the lessons learned from this application. Ultimately, these methods have the potential to be useful for clinicians and clients in choosing between treatment options, however they are limited in their ability to help researchers understand the mechanisms underlying these predictions.

Mechanisms of Behavior Change in Substance Use Disorder With and Without Formal Treatment

This article provides a narrative review of studies that examined mechanisms of behavior change in substance use disorder. Several mechanisms have some support, including self-efficacy, craving, protective behavioral strategies, and increasing substance-free rewards, whereas others have minimal support (e.g., motivation, identity). The review provides recommendations for expanding the research agenda for studying mechanisms of change, including designs to manipulate putative change mechanisms, measurement approaches that expand the temporal units of analysis during change efforts, more studies of change outside of treatment, and analytic approaches that move beyond mediation tests. The dominant causal inference approach that focuses on treatment and individuals as change agents could be expanded to include a molar behavioral approach that focuses on patterns of behavior in temporally extended environmental contexts. Molar behavioral approaches may advance understanding of how recovery from substance use disorder is influenced by broader contextual features, community-level variables, and social determinants of health. Expected final online publication date for the Annual Review of Clinical Psychology, Volume 18 is May 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.