Poison center exposures due to therapeutic misuse of nonprescription acetaminophen-containing combination products in the United States 2007-2016

Comparison of machine learning algorithms to predict intentional and unintentional poisoning risk factors

Introduction

A major share of poisoning cases are perpetrated intentionally, but this varies depending on different geographical regions, age spectrums, and gender distribution. The present study was conducted to determine the most important factors affecting intentional and unintentional poisonings using machine learning algorithms.

Materials and methods

The current cross-sectional study was conducted on 658 people hospitalized due to poisoning. The enrollment and follow-up of patients were carried out during 2020-2021. The data obtained from patients' files and during follow-up were recorded by a physician and entered into SPSS software by the registration expert. Different machine learning algorithms were used to analyze the data. Fit models of the training data were assessed by determining accuracy, sensitivity, specificity, F-measure, and the area under the rock curve (AUC). Finally, after analyzing the models, the data of the Gradient boosted trees (GBT) model were finalized.

Results

The GBT model rendered the highest accuracy (91.5 ± 3.4) among other models tested. Also, the GBT model had significantly higher sensitivity (94.7 ± 1.7) and specificity (93.2 ± 4.1) compared to other models (P < 0.001). The most prominent predictors based on the GBT model were the route of poison entry (weight = 0.583), place of residence (weight = 0.137), history of psychiatric diseases (weight = 0.087), and age (weight = 0.085).

Conclusion

The present study suggests the GBT model as a reliable predictor model for identifying the factors affecting intentional and unintentional poisoning. According to our results, the determinants of intentional poisoning included the route of poison entry into the body, place of residence, and the heart rate. The most important predictors of unintentional poisoning were age, exposure to benzodiazepine, creatinine levels, and occupation.

Decision tree outcome prediction of acute acetaminophen exposure in the United States: A study of 30,000 cases from the National Poison Data System

Acetaminophen is one of the most commonly used analgesic drugs in the United States. However, the outcomes of acute acetaminophen overdose might be very serious in some cases. Therefore, prediction of the outcomes of acute acetaminophen exposure is crucial. This study is a 6-year retrospective cohort study using National Poison Data System (NPDS) data. A decision tree algorithm was used to determine the risk predictors of acetaminophen exposure. The decision tree model had an accuracy of 0.839, an accuracy of 0.836, a recall of 0.72, a specificity of 0.86 and an F1_score of 0.76 for the test group and an accuracy of 0.848, a recall of 0.85, a recall of 0.74, a specificity of 0.87 and an F1_score of 0.78 for the training group. Our results showed that elevated serum levels of liver enzymes, other liver function test abnormality, anorexia, acidosis, electrolyte abnormality, increased bilirubin, coagulopathy, abdominal pain, coma, increased anion gap, tachycardia and hypotension were the most important factors in determining the outcome of acute acetaminophen exposure. Therefore, the decision tree model is a reliable approach in determining the prognosis of acetaminophen exposure cases and can be used in an emergency room or during hospitalization.

A mixed-methods systematic review of the prevalence, reasons, associated harms and risk-reduction interventions of over-the-counter (OTC) medicines misuse, abuse and dependence in adults

Background

Over-the-counter (OTC) medicines are typically safe. However, there is evidence that OTC medicines can sometimes cause harm as a result of their misuse, abuse and dependence.

Aim of the review

To review the literature on OTC medicines misuse, abuse and dependence in adults and identify the implicated medicines, contributing factors, associated harms and risk-mitigating interventions.

Methods

Following PRISMA guidelines, electronic databases including Cumulative Index to Nursing and Allied Health Literature (CINAHL), EMBASE, MEDLINE, PsycINFO Web of Science and Google Scholar were searched for peer-reviewed journal articles published in English between January 2011 and March 2019. Quantitative, qualitative and mixed-methods studies assessing aspects of misuse, abuse and dependence of OTC medicines in individuals aged 18 years or more were included. Studies that solely focused on adolescents only, doping in sports or abuse of OTC medicines in people who are substance abusers were excluded. The random effect meta-analysis model was used to pool the prevalence among the population-based studies.

Results

Of 2355 peer-reviewed studies initially identified, 53 were included in this review. According to the study design, the prevalence varied, but the overall pooled prevalence in the population-based studies was: 16.2% for misuse, 2.0% for abuse, and 7.2% for dependence. The common OTC medicines groups involved in the problematic use were analgesics (with or without codeine), sedative antihistamines, cough mixtures containing dextromethorphan. Physical, psychological, social and financial harms were associated with problematic use of OTC medicines in addition to hospitalisation and death. Interventions for the affected individuals were provided mainly through the community pharmacies, general practices and specialised addiction centres.

Conclusion

The problematic use of OTC medicines is quite prevalent in adults, necessitating raising public awareness about their safe use. In addition, innovative harm minimisation models need to be developed, evaluated and implemented across health care settings.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40545-021-00350-7.