Abstract
Radiocontrast media is a major cause of nephrotoxic acute kidney injury(AKI). Contrast-enhanced CT(CE-CT) is commonly performed in emergency departments(ED). Predicting individualized risks of contrast-associated AKI(CA-AKI) in ED patients is challenging due to a narrow time window and rapid patient turnover. We aimed to develop machine-learning(ML) models to predict CA-AKI in ED patients. Adult ED patients who underwent CE-CT between 2016 and 2020 at an academic, tertiary, referral hospital were included. Demographic, clinical, and laboratory data were collected from electronic medical records. Five ML models based on logistic regression; random forest; extreme gradient boosting; light gradient boosting; and multilayer perceptron were developed, using 42 features. Among 22,984 ED patients who underwent CE-CT; 1,862(8.1%) developed CA-AKI. The LGB model performed the best (AUROC = 0.731). Its top 10 features, in order of importance for predicting CA-AKI, were baseline serum creatinine; systolic blood pressure; serum albumin; estimated glomerular filtration rate; blood urea nitrogen; body weight; serum uric acid; hemoglobin; triglyceride; and body temperature. Given the difficulty of predicting risk of CA-AKI in ED, this model can help clinicians with early recognition of AKI and nephroprotective point-of-care interventions.