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

Abstract

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 26.75 ± 16.3 years (1–89). 3160 patients (70%) were females. Most patients had intentional exposure to acetaminophen. Almost all the patients had acetaminophen exposure via ingestion. In addition, 400 (8.8%) experienced major outcomes, 1500 (33.2%) experienced moderate outcomes, and 2622 (58%) of the patients experienced mild ones. The decision tree model performed well in the training and test groups. In the test group, the accuracy was 0.813, precision of 0.827, recall being 0.798, specificity 0.898, and an F1 score 0.80. In the training group, accuracy was 0.831, recall was 0.825, precision was 0.837, specificity was 0.90, and F1 score was 0.829. Our results showed that serum liver enzymes being present at elevated levels (Alanine aminotransferase (ALT), Aspartate aminotransferase (AST) greater than 1000 U/L followed by ALT, AST between 100 and 1000 U/L) , prothrombin time (PT) prolongation, bilirubin increase, renal failure, confusion, age, hypotension, other coagulopathy (such as partial thromboplastin time (PTT) prolongation), acidosis, and electrolyte abnormality were the effective factors in determining the outcomes in these patients. The decision tree algorithm is a dependable method for establishing the prognosis of patients who have been exposed to RSTI acetaminophen and can be used throughout the patients’ hospitalization period.