Search

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. The severity of outcomes was derived from the NPDS Coding Manual. Our database was divided into training (70%) and test (30%) sets. We used a light gradient boosting machine (LGBM), extreme gradient boosting (XGBoost), random forest (RF), and logistic regression (LR) to predict the outcomes of methadone poisoning. A total of 3847 patients with methadone exposures were included. Our results demonstrated that machine learning models conferred high accuracy and reliability in determining the outcomes of methadone poisoning cases. The performance evaluation showed all models had high accuracy, precision, specificity, recall, and F 1-score values. All models could reach high specificity (more than 96%) and high precision (80% or more) for predicting major outcomes. The models could also achieve a high sensitivity to predict minor outcomes. Finally, the accuracy of all models was about 75%. However, XGBoost and LGBM models achieved the best performance among all models. This study showcased the accuracy and reliability of machine learning models in the outcome prediction of methadone poisoning.

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., Methods: The National Poison Data System was used for this six-year retrospective cohort study.The decision tree model, common machine learning models multi layers perceptron, stochastic gradient descent (SGD), Adaboosting classiefier, linear support vector machine and ensembling methods including bagging, voting and stacking methods were used. The confusion matrix, precision, recall, specificity, f1-score, and accuracy were reported to evaluate the model's performance., Results: Of 6183 participants, 3336 patients (54.0%) were identified as biguanides exposures, and the remaining were those with sulfonylureas exposures. The decision tree model showed that the most important clinical findings defining biguanide and sulfonylurea exposures were hypoglycemia, abdominal pain, acidosis, diaphoresis, tremor, vomiting, diarrhea, age, and reasons for exposure. The specificity, precision, recall, f1-score, and accuracy of all models were greater than 86%, 89%, 88%, and 88%, respectively. The lowest values belong to SGD model. The decision tree model has a sensitivity (recall) of 93.3%, specificity of 92.8%, precision of 93.4%, f1_score of 93.3%, and accuracy of 93.3%., Conclusion: Our results indicated that machine learning methods including decision tree and ensembling methods provide a precise prediction model to diagnose biguanides and sulfonylureas exposure., (© 2023. The Author(s).)

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)., Methods: This study is a retrospective cohort study of National Poison Data System (NPDS) data, the largest data repository of poisoning cases in the United States. The SVM and DT algorithms were developed using training and test datasets. We also used precision-recall and ROC curves and Area Under the Curve value (AUC) for model evaluation., Results: Our model showed that acidosis, hypoglycemia, electrolyte abnormality, hypotension, elevated anion gap, elevated creatinine, tachycardia, and renal failure are the most important determinants in terms of outcome prediction of metformin poisoning. The average negative predictive value for the decision tree and SVM models was 92.30 and 93.30. The AUC of the ROC curve of the decision tree for major, minor, and moderate outcomes was 0.92, 0.92, and 0.89, respectively. While this figure of SVM model for major, minor, and moderate outcomes was 0.98, 0.90, and 0.82, respectively., Conclusions: In order to predict the prognosis of metformin poisoning, machine learning algorithms might help clinicians in the management and follow-up of metformin poisoning cases., (© 2022. The Author(s).)

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., Methods: This study is a six-year retrospective cohort analysis based on the National Poison Data System. Clinical effects of 6183 biguanide and sulfonylurea exposures were identified using binary logistic regression., Results: The mean age of patients with biguanide and sulfonylurea exposure was 39.27 ± 28.91 and 28.91 ± 30.41 years, respectively. Sulfonylurea exposure is most commonly seen via unintentional exposure, while biguanide exposure frequently occurs as a result of intentional ingestion. Minor and moderate outcomes commonly developed following biguanide and sulfonylurea exposure, respectively. Sulfonylurea exposure was less likely to develop clinical effects abdominal pain, metabolic acidosis, diarrhea, nausea, vomiting, and elevated creatinine than patients ingesting biguanides. However, sulfonylurea exposure was more likely to cause dizziness or vertigo, tremor, drowsiness or lethargy, agitation, diaphoresis, and hypoglycemia., Conclusions: Our study is the first to use a wide range of national data to describe the clinical characteristics that differentiate the toxicologic exposure to biguanides and sulfonylureas. Sulfonylurea exposure is commonly seen via unintentional exposure, while metformin exposure is frequently seen via intentional exposure. Sulfonylurea toxicity is more likely to cause agitation, dizziness or vertigo, tremor, diaphoresis, and hypoglycemia, while metformin exposure induces abdominal pain, acidosis, diarrhea, nausea, vomiting, and elevated creatinine., Competing Interests: Declaration of Competing Interest Authors declare no conflict of interest., (Copyright © 2022. Published by Elsevier Inc.)

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., (© 2021 Nordic Association for the Publication of BCPT (former Nordic Pharmacological Society). Published by John Wiley & Sons Ltd.)

Acetaminophen is a frequently used over-the-counter or prescribed medication in the United States. Exposure to acetaminophen can lead to acute liver cytolysis, acute liver failure, acute kidney injury, encephalopathy, and coagulopathy. This retrospective cohort study (1/1/2012 to 12/31/2017) investigated the clinical outcomes of intentional and unintentional acetaminophen exposure using the National Poison Data System data. The frequency of outcomes, chronicity, gender, route of exposure, the reasons for exposure, and treatments as described. Binary logistic regression was used to estimate the prognostic factors and odds ratios (OR) with 95% confidence intervals (CI) for outcomes. This study included 39,022 patients with acetaminophen exposure. Our study demonstrated that the likelihood of developing severe outcomes increased by aging (OR = 1.12, 95% CI: 1.08-1.015) and was lower in females (OR = 0.88, 95% CI: 0.78-0.99). Drowsiness/lethargy (OR = 1.48, 95% CI: 1.22-1.82), agitation (OR = 1.66, 95% CI: 1.11-2.50), coma (OR = 23.95, 95% CI: 17.05-33.64), bradycardia (OR = 2.29, 95% CI: 1.22-4.32), rhabdomyolysis (OR = 8.84, 95% CI: 3.71-21.03), hypothermia (OR = 4.1, 95% CI: 1.77-9.51), and hyperthermia 2.10 (OR = 2.10, 95% CI: 1.04-4.22) were likely associated with major outcomes or death. Treatments included intravenous N-acetylcysteine (61%), oral N-acetylcysteine (10%), vasopressor (1%), hemodialysis (0.7%), fomepizole (0.1%), hemoperfusion (0.03%), and liver transplant (0.1%). In conclusion, it is important to consider clinical presentations of patients with acetaminophen toxicity that result in major outcomes and mortality to treat them effectively.

Background: Toxic alcohol exposures are an ongoing concern in the United States. In the US, few studies characterize the local epidemiology of toxic alcohols over time., Objectives: The objective was to examine the incidence of toxic alcohol ingestions and changes in management over time., Methods: This retrospective cohort study evaluates toxic alcohol ingestion phone calls to a regional poison center in the United States covering four states. Data were queried for this poison center from the National Poison Data System (NPDS) using generic codes for each toxic alcohol. Inclusion criteria were ingestion of toxic alcohol, age ≥ 13 years, from January 1, 2000 to Dec 31, 2017. Exclusion criteria were unrelated effects coded in the medical outcome, duplicate data, or incomplete demographic data., Results: Of 926 subjects (adults and teenagers), 71.5% were male, and the mean age was 34.5 years. Toxic alcohol ingestion was more common in individuals younger than 40 years, with a significant relationship between age and intentional abuse or misuse (p = 0.001). There was also a significant relationship between age and reason for ingestion, with younger patients more likely to be suicidal (p < 0.001). Ethyleneglycol was the most common toxic alcohol. There was no change in the incidence of toxic alcohol ingestions over the study period. The mortality rate was 1.7%, and 31.2%of patients were hospitalized in a critical care unit. Major effects and death were more common in younger patients (p < 0.001). There was a significant difference in medical outcomes based on the type of toxic alcohol(p = 0.03). Fomepizole was the most common treatment. A Poisson regression model found no change in fomepizole use during the study period (p = 0.1). Ethanol administration over the study period increased (p = 0.02), while hemodialysis decreased (p = 0.02)., Conclusion: Data obtained from a single regional United States poison center showed low mortality related to toxic alcohol ingestions. The most prevalent toxic alcohol was Ethylene glycol. In all cases, toxic alcohol ingestion was higher in the 20-29-year-old age group. Reasons for ingestion, in most cases, were suicidal. Fomepizole was the most common treatment, ethanol administration as an antidote is rising, and hemodialysis utilization is decreasing. Data may not be nationally representative., (© 2021. Springer Nature Switzerland AG.)

Background: Methadone is well known for its long duration of action and propensity for mortality after an overdose. The present research was aimed at evaluating the clinical manifestations and time trends of methadone exposure in patients in US hospitals., Methods: We queried the American College of Medical Toxicology's Toxicology Investigators Consortium case registry for all cases of methadone exposure between January 1, 2010, and December 31, 2017. The collected information included demographic features, clinical presentations, therapeutic interventions, poisoning type (acute, chronic, or acute on chronic), and the reason(s) for exposure. Descriptive data and relative frequencies were used to investigate the participants' characteristics. Our data analysis was performed using SPSS version 19 and Prism software. The trends and clinical manifestations of methadone poisoning over the time period of the study were specifically investigated., Results: Nine hundred and seventy-three patients who met our inclusion criteria, with a mean age of 41.9 ± 16.6 years (range: 11 months-78 years) were analyzed. Five hundred eighty-two (60.2%) were male. The highest rate of methadone poisoning was observed in 2013. There was an increasing rate of methadone exposures in 2010-2013, followed by a decline in 2014-2017. The most common clinical manifestations in methadone-poisoned patients were coma (48.6%) and respiratory depression (33.6%). The in-hospital mortality rate of methadone poisoning was 1.4%., Conclusion: ToxIC Registry data showed that inpatient methadone exposures enhanced from 2010 to 2013, after which a reduction occurred in the years 2014 to 2017.