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.