Common Laboratory Parameters Are Useful for Screening for Alcohol Use Disorder: Designing a Predictive Model Using Machine Learning.

The diagnosis of alcohol use disorder (AUD) remains a difficult challenge, and some patients may not be adequately diagnosed. This study aims to identify an optimum combination of laboratory markers to detect alcohol consumption, using data science. An analytical observational study was conducted with 337 subjects (253 men and 83 women, with a mean age of 44 years (10.61 Standard Deviation (SD)). The first group included 204 participants being treated in the Addictive Behaviors Unit (ABU) from Albacete (Spain). They met the diagnostic criteria for AUD specified in the Diagnostic and Statistical Manual of mental disorders fifth edition (DSM-5). The second group included 133 blood donors (people with no risk of AUD), recruited by cross-section. All participants were also divided in two groups according to the WHO classification for risk of alcohol consumption in Spain, that is, males drinking more than 28 standard drink units (SDUs) or women drinking more than 17 SDUs. Medical history and laboratory markers were selected from our hospital's database. A correlation between alterations in laboratory markers and the amount of alcohol consumed was established. We then created three predicted models (with logistic regression, classification tree, and Bayesian network) to detect risk of alcohol consumption by using laboratory markers as predictive features. For the execution of the selection of variables and the creation and validation of predictive models, two tools were used: the scikit-learn library for Python, and the Weka application. The logistic regression model provided a maximum AUD prediction accuracy of 85.07%. Secondly, the classification tree provided a lower accuracy of 79.4%, but easier interpretation. Finally, the Naive Bayes network had an accuracy of 87.46%. The combination of several common biochemical markers and the use of data science can enhance detection of AUD, helping to prevent future medical complications derived from AUD.

[Seroepidemiology of Coxiella burnetii infection among blood donors in Albacete]. / Seroepidemiología de la infección por Coxiella burnetii en donantes de sangre en Albacete.

INTRODUCTION: The objectives of this study were to determine the prevalence of antibodies to Coxiella burnetii among blood donors and to examine the epidemiological characteristics of C. burnetii infection in Albacete, Spain. METHODS: A total of 863 serum samples were collected from blood donors aged 18-65 years. Donor samples were stratified by age, sex, and residence (rural or urban). IgG and IgM titers to the C. burnetii phase II antigen were determined by an indirect immunofluorescence assay. RESULTS: The prevalence of anti-phase II IgG was 23.1%, and three (0.3%) donors had positive IgM titers. Men were more frequently seropositive than women (29% vs. 18%; OR: 1.85; 95% CI: 1.34-2.56), and this difference was not related to differential occupational exposure to animals. Pet ownership had no impact on seroprevalence. In contrast, occupations involving contact with domestic ungulates were associated with a higher seroprevalence (OR: 2.39; 95% CI: 1.04-5.48). Nevertheless, 90% of seropositive donors reported no contact with farm animals. CONCLUSION: Our results show that C. burnetii infection is highly endemic in Albacete and that most infections are not linked to specific occupational exposure in this area. The high prevalence of antibodies to C. burnetii among blood donors indicates the advisability of studies to determine the risk of transfusion-transmitted Q fever in endemic areas.