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An In-Hospital Mortality Prediction Model for Acute Pesticide Poisoning in the Emergency Department.

Yoonseo Jeon1, Da-Eun Kim1, Inyong Jeong2

  • 1College of Medicine, Korea University, Seoul 02841, Republic of Korea.

Toxics
|October 28, 2025
PubMed
Summary

A new model predicts 14-day in-hospital mortality for acute pesticide poisoning patients using early data. Key predictors include paraquat, Glasgow Coma Scale, and bicarbonate levels, aiding clinical decisions.

Keywords:
acute pesticide poisoningemergency departmentin-hospital mortalitylogistic regressionmachine learning

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Area of Science:

  • Emergency Medicine
  • Toxicology
  • Public Health

Background:

  • Pesticide poisoning is a major cause of death and illness, especially in emergency departments.
  • Early identification of high-risk patients is crucial for effective management.

Purpose of the Study:

  • To develop and validate a predictive model for 14-day in-hospital mortality in acute pesticide poisoning patients.
  • To identify early clinical and laboratory predictors of mortality.

Main Methods:

  • Retrospective cohort study of 1056 patients with acute pesticide poisoning.
  • Development of logistic regression models using LASSO regularization and clinical curation.
  • Model performance evaluated using AUC, balanced accuracy, sensitivity, and specificity.

Main Results:

  • The best model achieved an AUC of 0.926, outperforming the APACHE II score (AUC 0.835).
  • Key predictors identified by SHAP analysis include paraquat ingestion, Glasgow Coma Scale, bicarbonate level, base excess, and alcohol history.
  • The final interpretable model showed an AUC of 0.923 with 0.835 balanced accuracy.

Conclusions:

  • A novel, accurate prediction model for 14-day in-hospital mortality in pesticide poisoning is developed.
  • The model utilizes readily available early clinical and laboratory data for risk stratification.
  • This tool can support clinical decision-making and improve patient outcomes in emergency settings.