Predictors of Abstinence From Heavy Drinking During Follow-Up in COMBINE

Predicting prognosis outcomes of primary central nervous system lymphoma with high-dose methotrexate-based chemotherapeutic treatment using lipidomics

Background

Primary central nervous system lymphoma (PCNSL) is a rare extranodal lymphomatous malignancy which is commonly treated with high-dose methotrexate (HD-MTX)-based chemotherapy. However, the prognosis outcome of HD-MTX-based treatment cannot be accurately predicted using the current prognostic scoring systems, such as the Memorial Sloan-Kettering Cancer Center (MSKCC) score.

Methods

We studied 2 cohorts of patients with PCNSL and applied lipidomic analysis to their cerebrospinal fluid (CSF) samples. After removing the batch effects and features engineering, we applied and compared several classic machine-learning models based on lipidomic data of CSF to predict the relapse of PCNSL in patients who were treated with HD-MTX-based chemotherapy.

Results

We managed to remove the batch effects and get the optimum features of each model. Finally, we found that Cox regression had the best prediction performance (AUC = 0.711) on prognosis outcomes.

Conclusions

We developed a Cox regression model based on lipidomic data, which could effectively predict PCNSL patient prognosis before the HD-MTX-based chemotherapy treatments.

Adjunctive Medication Management and Contingency Management to enhance adherence to acamprosate for alcohol dependence: the ADAM trial RCT

Background

Acamprosate is an effective and cost-effective medication for alcohol relapse prevention but poor adherence can limit its full benefit. Effective interventions to support adherence to acamprosate are therefore needed.

Objectives

To determine the effectiveness of Medication Management, with and without Contingency Management, compared to Standard Support alone in enhancing adherence to acamprosate and the impact of adherence to acamprosate on abstinence and reduced alcohol consumption.

Design

Multicentre, three-arm, parallel-group, randomised controlled clinical trial.

Setting

Specialist alcohol treatment services in five regions of England (South East London, Central and North West London, Wessex, Yorkshire and Humber and West Midlands).

Participants

Adults (aged 18 years or more), an International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, diagnosis of alcohol dependence, abstinent from alcohol at baseline assessment, in receipt of a prescription for acamprosate.

Interventions

(1) Standard Support, (2) Standard Support with adjunctive Medication Management provided by pharmacists via a clinical contact centre (12 sessions over 6 months), (3) Standard Support with adjunctive Medication Management plus Contingency Management that consisted of vouchers (up to £120) to reinforce participation in Medication Management. Consenting participants were randomised in a 2 : 1 : 1 ratio to one of the three groups using a stratified random permuted block method using a remote system. Participants and researchers were not blind to treatment allocation.

Main outcome measures

Primary outcome: self-reported percentage of medication taken in the previous 28 days at 6 months post randomisation. Economic outcome: EuroQol-5 Dimensions, a five-level version, used to calculate quality-adjusted life-years, with costs estimated using the Adult Service Use Schedule.

Results

Of the 1459 potential participants approached, 1019 (70%) were assessed and 739 (73 consented to participate in the study, 372 (50%) were allocated to Standard Support, 182 (25%) to Standard Support with Medication Management and 185 (25%) to Standard Support and Medication Management with Contingency Management. Data were available for 518 (70%) of participants at 6-month follow-up, 255 (68.5%) allocated to Standard Support, 122 (67.0%) to Standard Support and Medication Management and 141 (76.2%) to Standard Support and Medication Management with Contingency Management. The mean difference of per cent adherence to acamprosate was higher for those who received Standard Support and Medication Management with Contingency Management (10.6%, 95% confidence interval 19.6% to 1.6%) compared to Standard Support alone, at the primary end point (6-month follow-up). There was no significant difference in per cent days adherent when comparing Standard Support and Medication Management with Standard Support alone 3.1% (95% confidence interval 12.8% to -6.5%) or comparing Standard Support and Medication Management with Standard Support and Medication Management with Contingency Management 7.9% (95% confidence interval 18.7% to -2.8%). The primary economic analysis at 6 months found that Standard Support and Medication Management with Contingency Management was cost-effective compared to Standard Support alone, achieving small gains in quality-adjusted life-years at a lower cost per participant. Cost-effectiveness was not observed for adjunctive Medication Management compared to Standard Support alone. There were no serious adverse events related to the trial interventions reported.

Limitations

The trial's primary outcome measure changed substantially due to data collection difficulties and therefore relied on a measure of self-reported adherence. A lower than anticipated follow-up rate at 12 months may have lowered the statistical power to detect differences in the secondary analyses, although the primary analysis was not impacted.

Conclusions

Medication Management enhanced with Contingency Management is beneficial to patients for supporting them to take acamprosate.

Future work

Given our findings in relation to Contingency Management enhancing Medication Management adherence, future trials should be developed to explore its effectiveness and cost-effectiveness with other alcohol interventions where there is evidence of poor adherence.

Trial registration

This trial is registered as ISRCTN17083622 https://doi.org/10.1186/ISRCTN17083622.

Funding

This project was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 27, No. 22. See the NIHR Journals Library website for further project information.

Predictors of abstinence, no heavy drinking days, and a 2-level reduction in World Health Organization drinking levels during treatment for alcohol use disorder in the COMBINE study

BACKGROUND

Data from trials of medications for alcohol use disorder (AUD) can be used to identify predictors of drinking outcomes regardless of treatment, which can inform the design of future trials with heterogeneous populations. Here, we identified predictors of abstinence, no heavy drinking days, and a 2-level reduction in World Health Organization (WHO) drinking levels during treatment for AUD in the Combined Pharmacotherapies and Behavioral Interventions (COMBINE) Study.

METHODS

We utilized data from the COMBINE Study, a randomized placebo-controlled trial evaluating the efficacy of naltrexone and acamprosate, both alone and in combination, for AUD (n = 1168). A tree-based machine learning algorithm was used to construct classification trees predicting abstinence, no heavy drinking days, and a 2-level reduction in WHO drinking levels in the last 4 weeks of treatment, based on 89 baseline variables.

RESULTS

The final tree for predicting abstinence had one split based on consecutive days abstinent prior to randomization, with a higher proportion of subjects achieving abstinence among those classified as abstinent for >2 versus ≤2 consecutive weeks prior to randomization (66% vs. 29%). The final tree for predicting no heavy drinking days in the last 4 weeks of treatment had three splits based on consecutive days abstinent, age, and total Alcohol Dependence Scale score at baseline. Seventy-three percent of the subjects classified as abstinent for >2 consecutive weeks prior to randomization had no heavy drinking days in the last 4 weeks of treatment. Among those classified as abstinent ≤2 consecutive weeks prior, three additional splits showed that younger subjects (age ≤44 years; 37%), and older subjects (age >44) with a total Alcohol Dependence Scale score >13 and complete abstinence (56%) or other drinking goals (35%), were less likely to have no heavy drinking days than older subjects with a total Alcohol Dependence Scale score ≤13 (67%). The final tree for predicting a 2-level reduction in WHO levels had no splits.

CONCLUSIONS

Consecutive days abstinent prior to randomization may predict abstinence and no heavy drinking days and total Alcohol Dependence Scale score and age may predict no heavy drinking days. The 2-level reduction in WHO levels outcome may be less likely to discriminate based on multiple patient characteristics.

Differences in Sociodemographic and Alcohol-Related Clinical Characteristics Between Treatment Seekers and Nontreatment Seekers and Their Role in Predicting Outcomes in the COMBINE Study for Alcohol Use Disorder

BACKGROUND

One of the challenges in early-stage clinical research aimed at developing novel treatments for alcohol use disorder (AUD) is that the enrolled participants are heavy drinkers, but do not seek treatment for AUD.

AIMS

To compare nontreatment seekers with alcohol dependence (AD) from 4 human laboratory studies conducted at Brown University (N = 240; 65.4% male) to treatment seekers with AD from the multisite COMBINE study (N = 1,383; 69.1% male) across sociodemographic and alcohol-related clinical variables and to evaluate whether the variables that significantly differentiate the 2 samples predict the 3 main COMBINE clinical outcomes: time to relapse, percent days abstinent (PDA), and good clinical outcome.

METHODS

Sample characteristics were assessed by parametric and nonparametric testing. Three regression models measured the association between the differing variables and the 3 main COMBINE clinical outcomes.

RESULTS

The nontreatment seekers, compared to the treatment seekers, were more ethnically diverse, less educated, single, and working part-time or unemployed (p's < 0.05); they met fewer DSM-IV AD criteria and had significantly lower scores on alcohol-related scales (p's < 0.05); they were less likely to have a father with alcohol problems (p < 0.0001) and had a significantly earlier age of onset and longer duration of AD (p's < 0.05); they also had significantly more total drinks, drinks per drinking day, heavy drinking days (HDD), and lower PDA in the 30 days prior to baseline (p's < 0.0001 to <0.05). Having more HDD in the 30 days prior to baseline predicted all of the 3 COMBINE clinical outcomes. All the other characteristics mentioned above that differed significantly between the 2 groups predicted at least 1 of the 3 COMBINE clinical outcomes, except for level of education, age of onset, and duration of AD.

CONCLUSIONS

The observed differences between groups should be considered in efforts across participant recruitment at different stages of the development of new treatments for AUD.

Implementing Precision Psychiatry: A Systematic Review of Individualized Prediction Models for Clinical Practice

BACKGROUND

The impact of precision psychiatry for clinical practice has not been systematically appraised. This study aims to provide a comprehensive review of validated prediction models to estimate the individual risk of being affected with a condition (diagnostic), developing outcomes (prognostic), or responding to treatments (predictive) in mental disorders.

METHODS

PRISMA/RIGHT/CHARMS-compliant systematic review of the Web of Science, Cochrane Central Register of Reviews, and Ovid/PsycINFO databases from inception until July 21, 2019 (PROSPERO CRD42019155713) to identify diagnostic/prognostic/predictive prediction studies that reported individualized estimates in psychiatry and that were internally or externally validated or implemented. Random effect meta-regression analyses addressed the impact of several factors on the accuracy of prediction models.

FINDINGS

Literature search identified 584 prediction modeling studies, of which 89 were included. 10.4% of the total studies included prediction models internally validated (n = 61), 4.6% models externally validated (n = 27), and 0.2% (n = 1) models considered for implementation. Across validated prediction modeling studies (n = 88), 18.2% were diagnostic, 68.2% prognostic, and 13.6% predictive. The most frequently investigated condition was psychosis (36.4%), and the most frequently employed predictors clinical (69.5%). Unimodal compared to multimodal models (β = .29, P = .03) and diagnostic compared to prognostic (β = .84, p < .0001) and predictive (β = .87, P = .002) models were associated with increased accuracy.

INTERPRETATION

To date, several validated prediction models are available to support the diagnosis and prognosis of psychiatric conditions, in particular, psychosis, or to predict treatment response. Advancements of knowledge are limited by the lack of implementation research in real-world clinical practice. A new generation of implementation research is required to address this translational gap.

Objective continuous monitoring of alcohol consumption for three months among alcohol use disorder treatment outpatients

BACKGROUND

Transdermal alcohol sensing technology allows for objective continuous monitoring of alcohol use. The purpose of this study was to characterize alcohol consumption measured with this technology among alcohol use disorder treatment outpatients in two clinical trials.

METHODS

Participants were community-based alcohol treatment outpatients in usual care (N = 63) during the first three years of studies that monitored drinking with the secure continuous remote alcohol monitor (SCRAMx®) ankle bracelet. Research visits for uploading SCRAMx data occurred every other week in Study 1 (n = 43) and once weekly in Study 2 (n = 20), for 3 months. Staff used timeline follow-back procedures to collect self-reports of drinking frequency and magnitude at each research visit.

RESULTS

In the 90 days before intake, 85.7% (n = 54) of participants reported consuming alcohol, and consumption occurred on a median (interquartile range) 35.7% (29.0%) of days. During the treatment period, per SCRAMx versus self-report, the percentage who drank was 92.1% (n = 58) versus 46.6% (n = 30), p = .03, and consumption occurred on 16.5% (36.5%) versus 0.0% (21.4%) of days, respectively, p < .001. Median longest duration of abstinence was 26.0 (25.0) versus 39.0 (58.0) days, respectively, p < .001. Breath alcohol concentration, estimated from SCRAMx data, ranged from 0.02 to 0.91 g/dL.

CONCLUSIONS

This is the first study to quantify alcohol consumption among alcohol treatment outpatients using transdermal sensor technology. Results indicate that most patients drank while in outpatient care. Issues to consider for future applications of this technology and implications for alcohol treatment are discussed.

Mindfulness-based relapse prevention for alcohol dependence: Findings from a randomized controlled trial

OBJECTIVES

To assess the effects of mindfulness-based relapse prevention for alcohol dependence (MBRP-A) intervention on drinking and related consequences.

METHODS

123 alcohol-dependent adults in early recovery, recruited from outpatient treatment programs, were randomly assigned to MBRP-A (intervention plus usual-care; N = 64) or Control (usual-care-alone; N = 59) group. MBRP-A consisted of eight-weekly sessions and home practice. Outcomes were assessed at baseline, 8 weeks and 26 weeks (18 weeks post-intervention), and compared between groups using repeated measures analysis.

RESULTS

Outcome analysis included 112 participants (57 MBRP-A; 55 Control) who provided follow-up data. Participants were 41.0 ± 12.2 years old, 56.2% male, and 91% white. Prior to "quit date," they reported drinking on 59.4 ± 34.8% (averaging 6.1 ± 5.0 drinks/day) and heavy drinking (HD) on 50.4 ± 35.5% of days. Their drinking reduced after the "quit date" (before enrollment) to 0.4 ± 1.7% (HD: 0.1 ± 0.7%) of days. At 26 weeks, the MBRP-A and control groups reported any drinking on 11.5 ± 22.5% and 5.9 ± 11.6% of days and HD on 4.5 ± 9.3% and 3.2 ± 8.7% of days, respectively, without between-group differences (ps ≥ 0.05) in drinking or related consequences during the follow-up period. Three MBRP-A participants reported "relapse," defined as three-consecutive HD days, during the study. Subgroup analysis indicated that greater adherence to session attendance and weekly home practice minutes were associated with improved outcomes.

CONCLUSIONS

MBRP-A as an adjunct to usual-care did not show to improve outcomes in alcohol-dependent adults in early recovery compared to usual-care-alone; a return to drinking and relapse to HD were rare in both groups. However, greater adherence to MBRP-A intervention may improve long-term drinking-related outcomes.

Who achieves low risk drinking during alcohol treatment? An analysis of patients in three alcohol clinical trials

BACKGROUND AND AIMS

There is evidence that low-risk drinking is possible during the course of alcohol treatment and can be maintained following treatment. Our aim was to identify characteristics associated with low-risk drinking during treatment in a large sample of individuals as they received treatment for alcohol dependence.

DESIGN

Integrated analysis of data from the Combined Pharmacotherapies and Behavioral Intervention (COMBINE) study, Project MATCH (Matching Alcoholism Treatments to Client Heterogeneity) and the United Kingdom Alcohol Treatment Trial (UKATT) using repeated-measures latent class analysis to identify patterns of drinking and predictors of low-risk drinking patterns during treatment.

SETTING

United States and United Kingdom.

PARTICIPANTS

Patients (n = 3589) with alcohol dependence receiving treatment in an alcohol clinical trial were primarily male (73.0%), white (82.0%) and non-married (41.7%), with an average age of 42.0 (standard deviation = 10.7).

MEASUREMENTS

Self-reported weekly alcohol consumption during treatment was assessed using the Form-90 and validated with biological verification or collateral informants.

FINDINGS

Seven patterns of drinking during treatment were identified: persistent heavy drinking (18.7% of the sample), increasing heavy drinking (9.6%), heavy and low-risk drinking (6.7%), heavy drinking alternating with abstinence (7.9%), low-risk drinking (6.8%), increasing low-risk drinking (10.5%) and abstinence (39.8%). Lower alcohol dependence severity and fewer drinks per day at baseline significantly predicted low-risk drinking patterns [e.g. each additional drink prior to baseline predicted a 27% increase in the odds of expected classification in heavy drinking versus low-risk drinking patterns; odds ratio = 1.27 (95% confidence interval (CI) = 1.10, 1.47, P = 0.002]. Greater negative mood and more heavy drinkers in the social network were significant predictors of expected membership in heavier drinking patterns.

CONCLUSIONS

Low-risk drinking is achievable for some individuals as they undergo treatment for alcohol dependence. Individuals with lower dependence severity, less baseline drinking, fewer negative mood symptoms and fewer heavy drinkers in their social networks have a higher probability of achieving low-risk drinking during treatment.

Predicting drinking outcomes: Evidence from the United Kingdom Alcohol Treatment Trial (UKATT)

AIMS

To explore client characteristics that predict drinking outcomes using data from the UK Alcohol Treatment Trial (UKATT).

METHODS

Multiple linear regression was used to determine if there were any characteristics, measured before the start of treatment, that could predict drinking outcomes at three and 12months, as measured by percent day abstinent (PDA) and drinks per drinking day (DDD) over the preceding 90days.

RESULTS

Lower baseline DDD score and greater confidence to resist drinking predicted lower DDD at both three and twelve months following entry to treatment. In addition to baseline PDA and having greater confidence to resist heavy drinking, female gender, aiming for abstinence, more satisfaction with family life and a social network that included less support for drinking were predictors of percent days abstinent.

CONCLUSIONS

Overall the strongest and most consistent predictors of outcome were confidence to avoid heavy drinking and social support for drinking. More predictors were identified for percent of days abstinent than for drinks per drinking day. For percent of days abstinent, a number of client characteristics at baseline consistently predicted outcome at both month three and month twelve.

Continuous Abstinence During Early Alcohol Treatment is Significantly Associated with Positive Treatment Outcomes, Independent of Duration of Abstinence

AIMS

Neither the predictive value of early continuous abstinence in alcohol use disorder (AUD) or the point at which this effect may emerge has been evaluated. This analysis of the Combined Pharmacotherapies and Behavioral Interventions (COMBINE) clinical trial evaluated whether abstinence early in treatment was a predictor of longer term abstinence.

METHODS

Participants who stated a goal of total abstinence (N = 954) were dichotomized into Early Abstainer vs. Nonabstainers and were compared on a variety of drinking outcome measures that are frequently used in clinical trial evaluations of alcohol treatment strategies, as a function of duration of early continuous abstinence.

RESULTS

Significant differences existed for every outcome. Early Abstinence was significantly associated with fewer drinks per drinking day, number of drinking and number of heavy drinking days, and longer time to first drinking and first heavy drinking day. Effects were evident within the first week. The magnitude of all effects increased as the duration of early abstinence (1-4 weeks) increased, though the size of increase varied across the outcomes.

CONCLUSIONS

These data provide evidence that drinking at the beginning of alcohol treatment is significantly and robustly associated with drinking throughout and at the end of a clinical trial treatment for AUD. Early drinking may be a useful early index to identify whether patients are responding positively to a treatment strategy, and provides a useful method for tailoring treatment to patients that is consistent with a personalized medicine approach.