Predicting Adverse Drug Events in Chinese Pediatric Inpatients With the Associated Risk Factors: A Machine Learning Study.

Ze Yu,Huanhuan Ji,Yuntao Jia,Xin Hao,Tingting Tang,Fei Gao,Lin Song,Jianwen Xiao,Ping Wei,Qiaona Qi,Yuchen Zhou,Jinyuan Zhang

doi:10.3389/fphar.2021.659099

Abstract

The aim of this study was to apply machine learning methods to deeply explore the risk factors associated with adverse drug events (ADEs) and predict the occurrence of ADEs in Chinese pediatric inpatients. Data of 1,746 patients aged between 28 days and 18 years (mean age = 3.84 years) were included in the study from January 1, 2013, to December 31, 2015, in the Children’s Hospital of Chongqing Medical University. There were 247 cases of ADE occurrence, of which the most common drugs inducing ADEs were antibacterials. Seven algorithms, including eXtreme Gradient Boosting (XGBoost), CatBoost, AdaBoost, LightGBM, Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and TPOT, were used to select the important risk factors, and GBDT was chosen to establish the prediction model with the best predicting abilities (precision = 44%, recall = 25%, F1 = 31.88%). The GBDT model has better performance than Global Trigger Tools (GTTs) for ADE prediction (precision 44 vs. 13.3%). In addition, multiple risk factors were identified via GBDT, such as the number of trigger true (TT) (+), number of doses, BMI, number of drugs, number of admission, height, length of hospital stay, weight, age, and number of diagnoses. The influencing directions of the risk factors on ADEs were displayed through Shapley Additive exPlanations (SHAP). This study provides a novel method to accurately predict adverse drug events in Chinese pediatric inpatients with the associated risk factors, which may be applicable in clinical practice in the future.

Highlights

Rising attention has been paid to the early warning of adverse drug events (ADEs) in hospitalized children
In the final dataset of 1,746 cases, children were of the average age of 3.84 years, ranging from 0.08 to 17.75 years, females accounted for 35% (611 cases) and males 65% (1,135 cases), and the average body mass index (BMI) was 16.45 kg/cm2
Prediction based on important risk factors is necessary for the prevention of ADEs in pediatric patients; it is difficult to achieve a precise prediction due to complex body status and dosing regimens of children

Summary

Introduction

Rising attention has been paid to the early warning of adverse drug events (ADEs) in hospitalized children. ADEs are defined as medication-related patient injury caused during any stage of the medication process, some of which are preventable due to errors, whereas some are adverse drug reactions (ADRs) and non-preventable (Desireé et al, 2009; Marcum et al, 2013; Malladi, 2016). The World Health Organization defines an ADR as a response to a noxious and unintended drug (Smyth et al, 2012). Events such as overdose, drug abuse, Predicting adverse drug events treatment failure, and drug administration errors are excluded from ADRs. In this study, we considered ADEs including ADRs and drug administration errors. ADEs can be manifested by signs, symptoms, or laboratory abnormalities, which are important causes of iatrogenic morbidity and mortality (Desireé et al, 2009)

Objectives

Methods

Results

Conclusion