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Background Internet-based interventions are seen as attractive for harmful users of alcohol and lead to desirable clinical outcomes. Some participants will however not achieve the desired results. In this study, harmful users of alcohol have been partitioned in subgroups with low, intermediate or high probability of positive treatment outcome, using recursive partitioning classification tree analysis. Methods Data were obtained from a randomized controlled trial assessing the effectiveness of two Internet-based alcohol interventions. The main outcome variable was treatment response, a dichotomous outcome measure for treatment success. Candidate predictors for the classification analysis were first selected using univariate regression. Next, a tree decision model to classify participants in categories with a low, medium and high probability of treatment response was constructed using recursive partitioning software. Results Based on literature review, 46 potentially relevant baseline predictors were identified. Five variables were selected using univariate regression as candidate predictors for the classification analysis. Two variables were found most relevant for classification and selected for the decision tree model: ‘living alone’, and ‘interpersonal sensitivity’. Using sensitivity analysis, the robustness of the decision tree model was supported. Conclusions Harmful alcohol users in a shared living situation, with high interpersonal sensitivity, have a significantly higher probability of positive treatment outcome. The resulting decision tree model may be used as part of a decision support system but is on its own insufficient as a screening algorithm with satisfactory clinical utility. Trial registration Netherlands Trial Register (Cochrane Collaboration): NTR-TC1155 .

There is a lack of standardization regarding how to assess and categorize alcohol intake in older adults. The aim of this study was to systematically review methods used in epidemiological studies to define drinking patterns and measure alcohol consumption among older adults. A systematic search was conducted in the MEDLINE, PubMed, PsycINFO, EMBASE, and CINAHL databases for studies published from January 2009 to April 2021. Studies were included if they were observational studies with a quantitative design; the mean age of the participants was ≥ 65 years; questionnaires, screening tools, or diagnostic tools were used to define alcohol consumption; and alcohol consumption was self-reported. Of 492 studies considered, 105 were included. Among the 105 studies, we detected 19 different drinking patterns, and each drinking pattern had a wide range of definitions. The drinking patterns abstaining from alcohol, current drinking, and risk drinking had seven, 12 and 21 diverse definitions, respectively. The most used questionnaire and screening tools were the quantity-frequency questionnaire, with a recall period of 12 months, and the full and short versions of the Alcohol Use Disorders Identification Test, respectively. No consensus was found regarding methods used to assess, define, and measure alcohol consumption in older adults. Identical assessments and definitions must be developed to make valid comparisons of alcohol consumption in older adults. We recommend that alcohol surveys for older adults define the following drinking patterns: lifetime abstainers, former drinkers, current drinkers, risk drinking, and heavy episodic drinking. Standardized and valid definitions of risk drinking, and heavy episodic drinking should be developed. The expanded quantity-frequency questionnaire including three questions focused on drinking frequency, drinking volume, and heavy episodic drinking, with a recall period of 12 months, could be used.

With progress in magnetic resonance imaging technology and a broader dissemination of state-of-the-art imaging facilities, the acquisition of multiple neuroimaging modalities is becoming increasingly feasible. One particular hope associated with multimodal neuroimaging is the development of reliable data-driven diagnostic classifiers for psychiatric disorders, yet previous studies have often failed to find a benefit of combining multiple modalities. As a psychiatric disorder with established neurobiological effects at several levels of description, alcohol dependence is particularly well-suited for multimodal classification. To this aim, we developed a multimodal classification scheme and applied it to a rich neuroimaging battery (structural, functional task-based and functional resting-state data) collected in a matched sample of alcohol-dependent patients (N = 119) and controls (N = 97). We found that our classification scheme yielded 79.3% diagnostic accuracy, which outperformed the strongest individual modality – grey-matter density – by 2.7%. We found that this moderate benefit of multimodal classification depended on a number of critical design choices: a procedure to select optimal modality-specific classifiers, a fine-grained ensemble prediction based on cross-modal weight matrices and continuous classifier decision values. We conclude that the combination of multiple neuroimaging modalities is able to moderately improve the accuracy of machine-learning-based diagnostic classification in alcohol dependence.

Item response theory (IRT) analyses of alcohol use disorder (AUD) and other psychological disorders are a predominant method for assessing overall and individual criterion severity for psychiatric diagnosis. However, no investigation has established the consistency of the relative criteria severities across different samples. PubMed/Medline, PsycINFO, Web of Science and ProQuest databases were queried for entries relating to alcohol use and IRT. Study data were extracted using a standardized data entry sheet. Consistency of reported criteria severities across studies was analysed using generalizability theory to estimate generalized intraclass correlations (ICCs). A total of 451 citations were screened and 34 papers (30 unique samples) included in the research synthesis. The AUD criteria set exhibited low consistency in the ordering of criteria using both traditional [ICC = 0.16, 95% confidence interval (CI) 0.06-0.56] and generalized (ICC = 0.18, 95% CI 0.15-0.21) approaches. These results were partially accounted for by previously studied factors such as age and type of sample (e.g. clinical v. community), but the largest source of unreliability was the diagnostic instrument employed. Despite the robust finding of unidimensional structure of AUDs, inconsistency in the relative severities across studies suggests low replicability, challenging the generalizability of findings from any given study. Explicit modeling of well-studied factors like age and sample type is essential and increases the generalizability of findings. Moreover, while the development of structured diagnostic interviews is considered a landmark contribution toward improving psychiatric research, variability across instruments has not been fully appreciated and is substantial.

The COVID-19 pandemics caused both physical and mental health problems raising global social tension, anxiety and discomfort which toghether lead to the increase in the consumption of psychoactive substances, among which alcohol was the most common, as a way of self-help. The hypothesis of this paper is the rising number of type II (anxious model) and type III (depressive model) alcohol dependent patients (as identified by the Lesch Typology) in the post-COVID-19 pandemic period compared to the pre-pandemic period, as a likely consequence of the stress, fear, problems and adversities that were caused by the pandemic. The research was conducted as a retrospective cross-sectional study. It included 218 patients who were diagnosed with alcohol dependence. To classify the patients by the Lesch Typology, the MS Windows softer package for data processing available in public domain was used. In relation to the Lesch Typology, 111 (50.9%) patients belonged to type III, 45 (20.6%) to type I, 37 (17.0%) to type II and 25 (11.5%) to type IV. Compared to the pre-pandemic findings of alcohol dependents classification according to the Lesch Typology, there was no increase in types II and III after the COVID-19 pandemic.

Background Alcohol use disorder (AUD) is conceptualized as a dimensional phenomenon in the DSM-5, but electronic health records (EHRs) rely on binary AUD definitions according to the ICD-10. The present study classifies AUD severity levels using EHR data and tests whether increasing AUD severity levels are linked with increased comorbidity. Methods Billing data from two German statutory health insurance companies in Hamburg included n  = 21,954 adults diagnosed with alcohol-specific conditions between 2017 and 2021. Based on ICD-10 alcohol-specific diagnoses, patients were classified into five AUD severity levels: 1 (F10.0, T51.0 or T51.9); 2 (F10.1); 3 (F10.2); 4 (F10.3/4); 5 (K70 + or one of the following diagnoses: K70.0-4, K70.9, K85.2, K85.20, K86.0, 10.5-9, E24.4, G31.2, G62.1, G72.1, I42.6, K29.2). Generalized estimating equation regression models for count data (Poisson distribution) were used to assess associations with the Elixhauser Comorbidity Score (ECS). Results Across the study period, the annual prevalence of any AUD diagnosis varied between 2.7% and 2.9%. A dose-response relationship was observed between AUD severity and ECS, indicating that individuals with higher AUD severity experience more comorbid conditions, particularly cardiovascular and liver diseases. Conclusions The proposal to define AUD severity levels based on ICD-10 diagnoses allows for a more nuanced analysis of AUD in EHR data.

Background Research suggests depression and alcohol misuse are highly prevalent among chronic hepatitis C (CHC) patients, which is of clinical concern. Aims To compare ICD-9 codes for depression and alcohol misuse to validated survey instruments. Methods Among CHC patients, we assessed how well electronic ICD-9 codes for depression and alcohol misuse predicted these disorders using validated instruments. Results Of 4874 patients surveyed, 56% were male and 52% had a history of injection drug use. Based on the PHQ-8, the prevalence of depression was 30% compared to 14% based on ICD-9 codes within 12 months of survey, 37% from ICD-9 codes any time before or within 12 months after survey, and 48% from ICD-9 codes any time before or within 24 months after survey. ICD-9 codes predicting PHQ-8 depression had a sensitivity ranging from 59 to 88% and a specificity ranging from 33 to 65%. Based on the AUDIT-C, the prevalence of alcohol misuse was 21% compared to 3–23% using ICD-9 codes. The sensitivity of ICD-9 codes to predict AUDIT-C score ranged from 9 to 35% and specificity from 80 to 98%. Overall 39% of patients reported ever binge drinking, with a sensitivity of ICD-9 to predict binge drinking ranging from 7 to 33% and a specificity from 84 to 98%. More than half of patients had either an ICD-9 code for depression, a survey score indicating depression, or both (59%); more than one-third had the same patterns for alcohol misuse (36%). Conclusions ICD-9 codes were limited in predicting current depression and alcohol misuse, suggesting that caution should be exercised when using ICD-9 codes to assess depression or alcohol misuse among CHC patients.

To (1) confirm whether the Habit, Reward, and Fear Scale is able to generate a 3-factor solution in a population of obsessive-compulsive disorder and alcohol use disorder (AUD) patients; (2) compare these clinical groups in their habit, reward, and fear motivations; and (3) investigate whether homogenous subgroups can be identified to resolve heterogeneity within and across disorders based on the motivations driving ritualistic and drinking behaviors. One hundred and thirty-four obsessive-compulsive disorder (n = 76) or AUD (n = 58) patients were assessed with a battery of scales including the Habit, Reward, and Fear Scale, the Yale-Brown Obsessive-Compulsive Scale, the Alcohol Dependence Scale, the Behavioral Inhibition/Activation System Scale, and the Urgency, (lack of ) Premeditation, (lack of ) Perseverance, Sensation Seeking, and Positive Urgency Impulsive Behavior Scale. A 3-factor solution reflecting habit, reward, and fear subscores explained 56.6% of the total variance of the Habit, Reward, and Fear Scale. Although the habit and fear subscores were significantly higher in obsessive-compulsive disorder (OCD) and the reward subscores were significantly greater in AUD patients, a cluster analysis identified that the 3 clusters were each characterized by differing proportions of OCD and AUD patients. While affective (reward- and fear-driven) and nonaffective (habitual) motivations for repetitive behaviors seem dissociable from each other, it is possible to identify subgroups in a transdiagnostic manner based on motivations that do not match perfectly motivations that usually described in OCD and AUD patients.

Evidence on alcohol consumption as a risk factor for dementia usually relates to overall consumption. The role of alcohol-induced loss of consciousness is uncertain. To examine the risk of future dementia associated with overall alcohol consumption and alcohol-induced loss of consciousness in a population of current drinkers. Seven cohort studies from the UK, France, Sweden, and Finland (IPD-Work consortium) including 131 415 participants were examined. At baseline (1986-2012), participants were aged 18 to 77 years, reported alcohol consumption, and were free of diagnosed dementia. Dementia was examined during a mean follow-up of 14.4 years (range, 12.3-30.1). Data analysis was conducted from November 17, 2019, to May 23, 2020. Self-reported overall consumption and loss of consciousness due to alcohol consumption were assessed at baseline. Two thresholds were used to define heavy overall consumption: greater than 14 units (U) (UK definition) and greater than 21 U (US definition) per week. Dementia and alcohol-related disorders to 2016 were ascertained from linked electronic health records. Of the 131 415 participants (mean [SD] age, 43.0 [10.4] years; 80 344 [61.1%] women), 1081 individuals (0.8%) developed dementia. After adjustment for potential confounders, the hazard ratio (HR) was 1.16 (95% CI, 0.98-1.37) for consuming greater than 14 vs 1 to 14 U of alcohol per week and 1.22 (95% CI, 1.01-1.48) for greater than 21 vs 1 to 21 U/wk. Of the 96 591 participants with data on loss of consciousness, 10 004 individuals (10.4%) reported having lost consciousness due to alcohol consumption in the past 12 months. The association between loss of consciousness and dementia was observed in men (HR, 2.86; 95% CI, 1.77-4.63) and women (HR, 2.09; 95% CI, 1.34-3.25) during the first 10 years of follow-up (HR, 2.72; 95% CI, 1.78-4.15), after excluding the first 10 years of follow-up (HR, 1.86; 95% CI, 1.16-2.99), and for early-onset (<65 y: HR, 2.21; 95% CI, 1.46-3.34) and late-onset (≥65 y: HR, 2.25; 95% CI, 1.38-3.66) dementia, Alzheimer disease (HR, 1.98; 95% CI, 1.28-3.07), and dementia with features of atherosclerotic cardiovascular disease (HR, 4.18; 95% CI, 1.86-9.37). The association with dementia was not explained by 14 other alcohol-related conditions. With moderate drinkers (1-14 U/wk) who had not lost consciousness as the reference group, the HR for dementia was twice as high in participants who reported having lost consciousness, whether their mean weekly consumption was moderate (HR, 2.19; 95% CI, 1.42-3.37) or heavy (HR, 2.36; 95% CI, 1.57-3.54). The findings of this study suggest that alcohol-induced loss of consciousness, irrespective of overall alcohol consumption, is associated with a subsequent increase in the risk of dementia.

The goal of typology research is to identify subtypes of alcohol dependent (AD) patients sharing fundamental characteristics and try to match each subtype, with the most precise treatment strategy. This review provides a comprehensive history of the literature on alcohol dependent subtypes starting from the earliest attempt made by Jellinek. The binary models identified most closely with Cloninger and Babor as well as the successively more complex classifications are discussed. Typology classification potentially useful in guiding the treatment of AD patients, especially in the case of the serotonergic medications. Contrasting data suggests that other factors could influence the response to a medication and/or that more complex typologies should be identified. In summary, typology models may assist in the ascertainment criteria for clinical trials performed in behavioral and pharmacotherapeutic interventions. Greater emphasis, however, must be made to more clearly delineate this field of research, while moving toward more standardized typologies.