Outcome in Relation to Drinking Goals in Alcohol-Dependent Individuals: A Follow-up Study 2.5 and 5 Years After Treatment Entry

Berglund, Kristina; Rauwolf, Kerstin K.; Berggren, Ulf; Balldin, Jan; Fahlke, Claudia

doi:10.1093/alcalc/agz042

Cited by 19 publications

(14 citation statements)

References 21 publications

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“…Psychologists were assigned to patients based on case‐load, and their fractional time appointment with the treatment service. Alcohol abstinence is considered the clinically optimal goal for severely dependent patients [1,45,46]. Some patients were prescribed adjunctive medication (Table 2) after consultation with a physician at treatment entry (acamprosate, naltrexone, both or CBT only).…”

Section: Methodsmentioning

confidence: 99%

Predicting alcohol dependence treatment outcomes: a prospective comparative study of clinical psychologists versus ‘trained’ machine learning models

et al. 2020

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Background and aims Clinical staff are typically poor at predicting alcohol dependence treatment outcomes. Machine learning (ML) offers the potential to model complex clinical data more effectively. This study tested the predictive accuracy of ML algorithms demonstrated to be effective in predicting alcohol dependence outcomes, compared with clinical judgement and traditional linear regression. Design Prospective study. ML models were trained on 1016 previously treated patients (training-set) who attended a hospital-based alcohol and drug clinic. ML models (n = 27), clinical psychologists (n = 10) and a 'traditional' logistic regression model (n = 1) predicted treatment outcome during the initial treatment session of an alcohol dependence programme. Setting A 12-week cognitive behavioural therapy (CBT)-based abstinence programme for alcohol dependence in a hospital-based alcohol and drug clinic in Australia. Participants Prospective predictions were made for 220 new patients (test-set; 70.91% male, mean age = 35.78 years, standard deviation = 9.19). Sixty-nine (31.36%) patients successfully completed treatment. Measurements Treatment success was the primary outcome variable. The cross-validated training-set accuracy of ML models was used to determine optimal parameters for selecting models for prospective prediction. Accuracy, sensitivity, specificity, area under the receiver operator curve (AUC), Brier score and calibration curves were calculated and compared across predictions. Findings The mean aggregate accuracy of the ML models (63.06%) was higher than the mean accuracy of psychologist predictions (56.36%). The most accurate ML model achieved 70% accuracy, as did logistic regression. Both were more accurate than psychologists (P < 0.05) and had superior calibration. The high specificity for the selected ML (79%) and logistic regression (90%) meant they were significantly (P < 0.001) more effective than psychologists (50%) at correctly identifying patients whose treatment was unsuccessful. For ML and logistic regression, high specificity came at the expense of sensitivity (26 and 31%, respectively), resulting in poor prediction of successful patients. Conclusions Machine learning models and logistic regression appear to be more accurate than psychologists at predicting treatment outcomes in an abstinence programme for alcohol dependence, but sensitivity is low.

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Section: Methodsmentioning

confidence: 99%

Predicting alcohol dependence treatment outcomes: a prospective comparative study of clinical psychologists versus ‘trained’ machine learning models

et al. 2020

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“…We hypothesized that one-and two-level reductions in WHO risk levels would be achieved, associated with improved functional outcomes, and maintained over time in both the COMBINE and UKATT samples. Based on prior research showing that low risk drinking is more likely among individuals with lower alcohol dependence severity [12,[26][27][28], we hypothesized that reductions in WHO risk levels would be more likely to be achieved, associated with improved functional outcomes, and maintained over time among those with lower initial alcohol dependence severity.…”

Section: Who Risk Levels As a Drinking Reduction Targetmentioning

confidence: 99%

“…The inclusion of harm reduction end-points is important, because most individuals who receive treatment for AUD have difficulty sustaining abstinence [4][5][6] and many individuals with AUD prefer drinking reduction goals [7][8][9][10][11]. However, questions remain regarding whether non-abstinent drinking reductions can be achieved and maintained, particularly among individuals with more severe AUD [12,13].…”

Section: Introductionmentioning

confidence: 99%

World Health Organization risk drinking level reductions are associated with improved functioning and are sustained among patients with mild, moderate and severe alcohol dependence in clinical trials in the United States and United Kingdom

et al. 2020

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Aims To examine whether World Health Organization (WHO) risk‐level reductions in drinking were achievable, associated with improved functioning and maintained over time among patients at varying initial alcohol dependence severity levels. Design and setting Secondary data analysis of multi‐site randomized clinical trials: the US Combined Pharmacotherapies and Behavioral Interventions for Alcohol Dependence (COMBINE) study and the UK Alcohol Treatment Trial (UKATT). Participants Individuals with alcohol dependence enrolled in COMBINE (n = 1383; 68.8% male) and seeking treatment for alcohol problems in UKATT (n = 742; 74.1% male). Interventions Naltrexone, acamprosate or placebo, and combined behavioral intervention or medication management in COMBINE. Social behavior network therapy or motivational enhancement therapy in UKATT. Measurements WHO risk‐level reductions were assessed via the calendar method. Alcohol dependence was measured by the Alcohol Dependence Scale, the Leeds Dependence Questionnaire and the Diagnostic and Statistical Manual of Mental Disorders. Measures of functioning included alcohol‐related consequences (Drinker Inventory of Consequences and Alcohol Problems Questionnaire), mental health (Short Form Health Survey) and liver enzyme tests. Findings One‐ and two‐level reductions in WHO risk levels in the last month of treatment were maintained at the 1‐year follow‐up [adjusted odds ratio (OR), 95% confidence interval (CI) = one‐level reduction in COMBINE: 3.51 (2.73, 4.29) and UKATT: 2.65 (2.32, 2.98)] and associated with fewer alcohol‐related consequences [e.g. B, 95% CI = one‐level reduction COMBINE: –26.22 (–30.62, –21.82)], better mental health [e.g. B, 95% CI = one‐level reduction UKATT: 9.53 (7.36, 11.73)] and improvements in γ‐glutamyltransferase [e.g. B, 95% CI = one‐level reduction UKATT: –89.77 (–122.50, –57.04)] at the end of treatment, even among patients with severe alcohol dependence. Results were similar when abstainers were excluded. Conclusions Reductions in World Health Organization risk levels for alcohol consumption appear to be achievable, associated with better functioning and maintained over time in both the United States and the United Kingdom.

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“…4,7,9 Current Study Abstinence has long been considered a stable outcome for individuals with AUD, 14,15 and numerous studies have questioned whether "controlled" drinking can be maintained. [15][16][17] The debate over whether controlled drinking is achievable has been considered in the academic literature for nearly 50 years, 18 but concrete guidance has not emerged on the magnitude of the reduction that is necessary for sustained health benefits. More recent work has shown that drinking reductions, as assessed by the WHO RDLs, are often seen over the first post-treatment year, 7 but whether these reductions can be maintained for longer periods and whether WHO RDL reductions, short of abstinence, are associated with sustained improvements in functioning remain unclear.…”

Section: Defining "Success" In Aud Treatmentmentioning

confidence: 99%

Stability of Drinking Reductions and Long-term Functioning Among Patients with Alcohol Use Disorder

et al. 2020

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Background The World Health Organization (WHO) categorizes alcohol consumption according to grams consumed into low-, medium-, high-, and very-high-risk drinking levels (RDLs). Although abstinence has been considered the ideal outcome of alcohol treatment, reductions in WHO RDLs have been proposed as primary outcomes for alcohol use disorder (AUD) trials. Objective The current study examines the stability of WHO RDL reductions and the association between RDL reductions and long-term functioning for up to 3 years following treatment. Design and Participants Secondary data analysis of patients with AUD enrolled in the COMBINE Study and Project MATCH, two multi-site, randomized AUD clinical trials, who were followed for up to 3 years post-treatment (COMBINE: n = 694; MATCH: n = 806). Measures Alcohol use was measured via calendar-based methods. We estimated all models in the total sample and among participants who did not achieve abstinence during treatment. Key Results One-level RDL reductions were achieved by 84% of patients at the end of treatment, with 84.9% of those individuals maintaining that reduction at a 3-year follow-up. Two-level RDL reductions were achieved by 68% of patients at the end of treatment, with 77.7% of those individuals maintaining that reduction at a 3-year follow-up. One- and two-level RDL reductions at the end of treatment were associated with significantly better mental health, quality of life (including physical quality of life), and fewer drinking consequences 3 years after treatment (p < 0.05), as compared to no change or increased drinking. Conclusion AUD patients can maintain WHO RDL reductions for up to 3 years after treatment. Patients who had WHO RDL reductions functioned significantly better than those who did not reduce their drinking. These findings are consistent with prior reports suggesting that drinking reductions, short of abstinence, yield meaningful improvements in patient health, well-being, and functioning.

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Outcome in Relation to Drinking Goals in Alcohol-Dependent Individuals: A Follow-up Study 2.5 and 5 Years After Treatment Entry

Cited by 19 publications

References 21 publications

Predicting alcohol dependence treatment outcomes: a prospective comparative study of clinical psychologists versus ‘trained’ machine learning models

Predicting alcohol dependence treatment outcomes: a prospective comparative study of clinical psychologists versus ‘trained’ machine learning models

World Health Organization risk drinking level reductions are associated with improved functioning and are sustained among patients with mild, moderate and severe alcohol dependence in clinical trials in the United States and United Kingdom

Stability of Drinking Reductions and Long-term Functioning Among Patients with Alcohol Use Disorder

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