Citation

Mehrpour O, Saeedi F, Hoyte C. Basic Clin. Pharmacol. Toxicol. 2021; ePub(ePub): ePub.

Copyright

(Copyright © 2021, Nordic Pharmacological Society, Publisher John Wiley and Sons)

DOI

10.1111/bcpt.13674

PMID

34649297

Abstract

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 six-year retrospective cohort study using National Poison Data System data (NPDS). 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.

Language: en

Keywords

Poisoning; Machine learning; Acetaminophen; Decision tree