Routine alcohol screening in the ED: unscreened patients have an increased risk for hazardous alcohol use

The value of toxicological analysis in acute poisoning patients with uncertain exposure histories: a retrospective and descriptive study from an institute of poisoning

BACKGROUND

In clinical practice, some patients might not be able or unwilling to provide a thorough history of medication and poison exposure. The aim of this study was to use toxicological analysis to examine the clinical characteristics of patients with acute poisoning whose exposure history was uncertain from a toxicological analysis perspective.

METHODS

This was a retrospective and descriptive study from an institute of poisoning. Patient registration information and test reports spanning the period from April 1, 2020 to March 31, 2022, were obtained. Patients with uncertain exposure histories and who underwent toxicological analysis were included. Clinical manifestations and categories of toxics were analyzed.

RESULTS

Among the 195 patients with positive toxicological analysis results, the main causes of uncertain exposure history was disturbance of consciousness (62.6%), unawareness (23.6%) and unwillingness or lack of cooperation (13.8%). The predominant clinical manifestations were disturbed consciousness (62.6%), followed by vomiting and nausea (14.4%) and liver function abnormalities (8.7%). A comparison of clinical manifestations between patients with positive and negative (n=99) toxicological analyses results revealed significantly different proportions of disturbances in consciousness (63% vs. 21%), dizziness (1.5% vs. 5.1%), multi-organ failure (1.5% vs. 7.1%), and local pain (0 vs 4%). The main categories of substances involved were psychiatric medications (23.1%), sedatives (20.5%), insecticides (13.8%), and herbicides (12.8%).

CONCLUSION

The clinical manifestations of acute poisoning in patients with an uncertain exposure history are diverse and nonspecific, and toxicological analysis plays a pivotal role in the diagnosis and differential diagnosis of such patients.

Acceptability Among Frontline Staff Toward Distributing an Anonymous Alcohol Survey in Emergency Departments: A Mixed Methods Study

ABSTRACT

Emergency departments (EDs) serve as the front line when patients encounter the hospital system. Limited data are available of patients' alcohol habits collected during Danish ED visits, and no studies have, to our knowledge, examined frontline staffs' (registered nurses and medical secretaries) acceptability to deliver anonymous alcohol surveys to patients. We aimed at examining the proportion of survey respondents and the prevalence of patients' alcohol habits and also exploring frontline staff acceptability of the distribution of an anonymous survey regarding patients' alcohol habits in EDs. Intendedly, all eligible patients ≥18 years old entering two EDs in March 2019 should receive a survey based on the Alcohol Use Disorder Identification Test. The study was an explanatory, sequential, mixed methods design, and results were analyzed with descriptive statistics and a deductive content analysis based on the theoretical framework of acceptability. In total, 15% (n = 1,305) of the total 8,679 patients in the EDs returned the survey. Qualitative analysis of interviews (n = 31) with staff showed that they had been reluctant to distribute the survey primarily because of ethical concerns of anonymity, freedom of choice, and being nonjudgmental toward patients. Hence, patients with no obvious alcohol problems were more likely to receive the survey. Still, we found that 23% of the respondents had an Alcohol Use Disorder Identification Test score ≥ 8. Results indicate that frontline staffs' recognition of patients' alcohol use is inadequate, and findings show a low degree of acceptability among staff to deliver an anonymous survey, which is in line with earlier described barriers toward screening activities in EDs.

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.

Identifying the Assumptions and Bias That Affect Screening and Brief Interventions for Harmful Alcohol Use

BACKGROUND

Screening for harmful alcohol use (HAU) is standard in clinical settings; however, self-reports of the quantity/frequency of use are often inconsistent with population survey data and objective assessment. Inaccuracies of self-reported alcohol use prompt exploration of patients' reluctance to share this information with their health care providers.

OBJECTIVES

The purpose of this article is to identify the assumptions and biases that affect screening and brief interventions for HAU.

METHOD

A total of 22 research articles were selected for a systematic review of the literature. The studies included prospective and retrospective studies, randomized controlled trials, case studies, and quantitative and qualitative research from 2015 to 2021.

RESULTS

This systematic review identified alcohol health literacy, the inherent bias of surveys and screening tools, stigma, avoidance bias, and the normalization/villainization of alcohol use as contributors to the assumptions and biases that affect screening and brief interventions for HAU.

CONCLUSIONS

Exploring these assumptions and biases presents opportunities to develop strategies that promote positive change.

A Deep Learning Algorithm to Predict Hazardous Drinkers and the Severity of Alcohol-Related Problems Using K-NHANES

Purpose: The number of patients with alcohol-related problems is steadily increasing. A large-scale survey of alcohol-related problems has been conducted. However, studies that predict hazardous drinkers and identify which factors contribute to the prediction are limited. Thus, the purpose of this study was to predict hazardous drinkers and the severity of alcohol-related problems of patients using a deep learning algorithm based on a large-scale survey data. Materials and Methods: Datasets of National Health and Nutrition Examination Survey of South Korea (K-NHANES), a nationally representative survey for the entire South Korean population, were used to train deep learning and conventional machine learning algorithms. Datasets from 69,187 and 45,672 participants were used to predict hazardous drinkers and the severity of alcohol-related problems, respectively. Based on the degree of contribution of each variable to deep learning, it was possible to determine which variable contributed significantly to the prediction of hazardous drinkers. Results: Deep learning showed the higher performance than conventional machine learning algorithms. It predicted hazardous drinkers with an AUC (Area under the receiver operating characteristic curve) of 0.870 (Logistic regression: 0.858, Linear SVM: 0.849, Random forest classifier: 0.810, K-nearest neighbors: 0.740). Among 325 variables for predicting hazardous drinkers, energy intake was a factor showing the greatest contribution to the prediction, followed by carbohydrate intake. Participants were classified into Zone I, Zone II, Zone III, and Zone IV based on the degree of alcohol-related problems, showing AUCs of 0.881, 0.774, 0.853, and 0.879, respectively. Conclusion: Hazardous drinking groups could be effectively predicted and individuals could be classified according to the degree of alcohol-related problems using a deep learning algorithm. This algorithm could be used to screen people who need treatment for alcohol-related problems among the general population or hospital visitors.