Characterization of glucagon-like peptide-1 (GLP-1) agonist exposures reported to a single United States poison center.

Introduction: Glucagon-like peptide-1 agonists have gained attention in recent years due to their efficacy in managing type II diabetes mellitus and their emerging role in weight management. The purpose of this study was to characterize glucagon-like peptide-1 agonist exposures reported to a single United States regional poison center over nine years, including causes of exposure, associated clinical effects, and potential areas for improving patient education and safety.

Methods: This retrospective cohort study analyzed all poison center calls involving glucagon-like peptide-1 agonists submitted to a single United States regional poison center from 14 January 2014 to 1 May 2023.

Clinical Effects of Psychedelic Substances Reported to United States Poison Centers: 2012 to 2022.

Study Objective: Psychedelic substances use is increasing in the United States (US). The approval of new psychedelic drugs and legalization of natural psychedelic substances will likely further increase exposures and subsequent adverse events. The study objective is to describe the clinical effects, therapies, and medical outcomes of patients with psychedelic exposures reported to US poison centers.

Use of large language models to optimize poison center charting.

Introduction: Efficient and complete medical charting is essential for patient care and research purposes. In this study, we sought to determine if Chat Generative Pre-Trained Transformer could generate cogent, suitable charts from recorded, real-world poison center calls and abstract and tabulate data.

Methods: De-identified transcripts of real-world hospital-initiated poison center consults were summarized by Chat Generative Pre-Trained Transformer 4.

Acute exposure to hydrazine reported to four United States regional poison centers: reconsidering a paradigm.

Introduction: Exposures to hydrazines occur during aeronautic and space operations and pose a potential risk to personnel. Historically, extensive preparatory countermeasures have been taken due to concern for severe toxicity. This study seeks to better understand manifestations of acute occupational exposures to hydrazine to guide recommendations for management.

The Clinical Toxicology Recommendations Collaborative: purpose, organization, and methodology.

Introduction: Scientific societies aim to provide a collective voice and unified stance on important issues. The Clinical Toxicology Recommendations Collaborative was formed in 2016 to develop evidence- and consensus-based recommendations for the management of patients exposed to common and/or serious poisonings for which the management is unclear or controversial.

Organization: The Clinical Toxicology Recommendations Collaborative is led jointly by the American Academy of Clinical Toxicology, the Asia Pacific Association of Medical Toxicology, and the European Association of Poison Centres and Clinical Toxicologists.

Patients with Altered Mental Status May Benefit from Medical Toxicology Consultation.

Background: Presentation of intoxicated patients to hospitals is frequent, varied, and increasing. Medical toxicology expertise could lead to important changes in diagnosis and treatment, especially in patients presenting with altered mental status.

Objectives: To describe and analyze clinical scenarios during a 1-year period after the establishment of a medical toxicology consultation service (MTCS).

The role of QRS complex prolongation in predicting severe toxicity in single-xenobiotic overdose.

Objective: The QRS complex duration is commonly used to prognosticate severity, predict outcomes, and indicate treatment in overdose. However, literature to support this practice is mixed in tricyclic antidepressant overdoses and absent in non-tricyclic antidepressant overdoses. Our objective was to assess the validity of QRS complex duration as a prognostic marker in overdose.

Outcome prediction of methadone poisoning in the United States: implications of machine learning in the National Poison Data System (NPDS).

Methadone is an opioid receptor agonist with a high potential for abuse. The current study aimed to compare different machine learning models to predict the outcomes following methadone poisoning. This six-year retrospective longitudinal study utilizes National Poison Data System (NPDS) data.

Antivenom supply and demand: An analysis of antivenom availability and utilization in South Africa.

Introduction: Snakebites are a neglected tropical disease. In many areas, envenoming incidence and antivenom administration rates are unknown. This study compared antivenom (AV) availability to rates of envenoming and recommendations to treat (RTT) in South Africa.

2023 American Heart Association Focused Update on the Management of Patients With Cardiac Arrest or Life-Threatening Toxicity Due to Poisoning: An Update to the American Heart Association Guidelines for Cardiopulmonary Resuscitation and Emergency Cardiovascular Care.

In this focused update, the American Heart Association provides updated guidance for resuscitation of patients with cardiac arrest, respiratory arrest, and refractory shock due to poisoning. Based on structured evidence reviews, guidelines are provided for the treatment of critical poisoning from benzodiazepines, β-adrenergic receptor antagonists (also known as β-blockers), L-type calcium channel antagonists (commonly called calcium channel blockers), cocaine, cyanide, digoxin and related cardiac glycosides, local anesthetics, methemoglobinemia, opioids, organophosphates and carbamates, sodium channel antagonists (also called sodium channel blockers), and sympathomimetics. Recommendations are also provided for the use of venoarterial extracorporeal membrane oxygenation.

Toxic Tetrahydrocannabinol (THC) Dose in Pediatric Cannabis Edible Ingestions.

Objective: The study characterizes cannabis toxicity in relation to tetrahydrocannabinol (THC) dose in pediatric edible cannabis ingestions.

Methods: This is a retrospective review of children aged <6 years presenting with edible cannabis ingestions of known THC dose within a pediatric hospital network (January 1, 2015-October 25, 2022). Cannabis toxicity was characterized as severe if patients exhibited severe cardiovascular (bradycardia, tachycardia/hypotension requiring vasopressors or intravenous fluids, other dysrhythmias), respiratory (respiratory failure, apnea, requiring oxygen supplementation), or neurologic (seizure, myoclonus, unresponsiveness, responsiveness to painful stimulation only, requiring intubation or sedation) effects.

A Case Report of Venezuelan Suntiger Tarantula (Psalmopoeus Irminia) Envenomation and Review of Tarantula Exposures.

Background: Tarantula envenomations are encountered infrequently but may increase with increased exotic animal ownership. This case report presents the first documented toxicity from a Venezuelan suntiger tarantula (VST), Psalmopoeus irminia, and provides a general framework for approaching patients with tarantula exposures.

Case Report: A 35-year-old man presented to an emergency department 4 h after experiencing a bite from his pet VST.

Deep learning neural network derivation and testing to distinguish acute poisonings.

Introduction: Acute poisoning is a significant global health burden, and the causative agent is often unclear. The primary aim of this pilot study was to develop a deep learning algorithm that predicts the most probable agent a poisoned patient was exposed to from a pre-specified list of drugs.

Research Design & Methods: Data were queried from the National Poison Data System (NPDS) from 2014 through 2018 for eight single-agent poisonings (acetaminophen, diphenhydramine, aspirin, calcium channel blockers, sulfonylureas, benzodiazepines, bupropion, and lithium).

Using a decision tree algorithm to distinguish between repeated supra-therapeutic and acute acetaminophen exposures.

Background: This study aimed to compare clinical and laboratory characteristics of supra-therapeutic (RSTI) and acute acetaminophen exposures using a predictive decision tree (DT) algorithm.

Methods: We conducted a retrospective cohort study using the National Poison Data System (NPDS). All patients with RSTI acetaminophen exposure (n = 4,522) between January 2012 and December 2017 were included.

Comparison of decision tree with common machine learning models for prediction of biguanide and sulfonylurea poisoning in the United States: an analysis of the National Poison Data System.

Background: Biguanides and sulfonylurea are two classes of anti-diabetic medications that have commonly been prescribed all around the world. Diagnosis of biguanide and sulfonylurea exposures is based on history taking and physical examination; thus, physicians might misdiagnose these two different clinical settings. We aimed to conduct a study to develop a model based on decision tree analysis to help physicians better diagnose these poisoning cases.

Classification of acute poisoning exposures with machine learning models derived from the National Poison Data System.

The primary aim of this pilot study was to develop a machine learning algorithm to predict and distinguish eight poisoning agents based on clinical symptoms. Data were used from the National Poison Data System from 2014 to 2018, for patients 0-89 years old with single-agent exposure to eight drugs or drug classes (acetaminophen, aspirin, benzodiazepines, bupropion, calcium channel blockers, diphenhydramine, lithium and sulfonylureas). Four classifier prediction models were applied to the data: logistic regression, LightGBM, XGBoost, and CatBoost.

Adolescent US Poison Center Exposure Calls During the COVID-19 Pandemic.

Purpose: The objective of this study was to evaluate trends and characteristics in adolescent poison center (PC) exposure calls before and during the COVID-19 pandemic.

Methods: A retrospective review of PC calls for adolescents aged 13-17 years from January 1, 2018 through June 30, 2021.

Results: During the pandemic, US PCs had a higher proportion of adolescent exposure calls managed in a healthcare facility (71.

Utility of support vector machine and decision tree to identify the prognosis of metformin poisoning in the United States: analysis of National Poisoning Data System.

Background: With diabetes incidence growing globally and metformin still being the first-line for its treatment, metformin's toxicity and overdose have been increasing. Hence, its mortality rate is increasing. For the first time, we aimed to study the efficacy of machine learning algorithms in predicting the outcome of metformin poisoning using two well-known classification methods, including support vector machine (SVM) and decision tree (DT).

Decision tree algorithm can determine the outcome of repeated supratherapeutic ingestion (RSTI) exposure to acetaminophen: review of 4500 national poison data system cases.

This study is aimed at establishing the outcome of RSTI exposure to acetaminophen based on a decision tree algorithm for the first time. This study used the National Poison Data System (NPDS) to conduct a six-year retrospective cohort analysis, which included 4522 individuals. The patients had a mean age of 26.

Comparison of Two-Bag Versus Three-Bag N-Acetylcysteine Regimens for Pediatric Acetaminophen Toxicity.

Background: Acetaminophen overdose is a leading cause of liver failure, and a leading cause of pediatric poisoning requiring hospital admission. The antidote, N-acetylcysteine (NAC), is traditionally administered as a three-bag intravenous infusion. Despite its efficacy, NAC is associated with high incidence of nonallergic anaphylactoid reactions (NAARs).

Distinguishing characteristics of exposure to biguanide and sulfonylurea anti-diabetic medications in the United States.

Objectives: Biguanides and sulfonylureas are anti-hyperglycemic drugs commonly used in the United States. Poisoning with these drugs may lead to serious consequences. The diagnosis of biguanide and sulfonylurea poisoning is based on history, clinical manifestations, and laboratory studies.

Predictors of severe outcome following opioid intoxication in children.

Introduction: While the opioid crisis has claimed the lives of nearly 500,000 in the U.S. over the past two decades, and pediatric cases of opioid intoxications are increasing, only sparse data exist regarding risk factors for severe outcome in children following an opioid intoxication.