Development and validation of a deep neural network–based model to predict acute kidney injury following intravenous administration of iodinated contrast media in hospitalized patients with chronic kidney disease: a multicohort analysis

Abstract

ABSTRACT Background Stratification of chronic kidney disease (CKD) patients [estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m2] at risk for post-contrast acute kidney injury (PC-AKI) following intravenous administration of iodinated contrast media (ICM) is important for clinical decision-making and clinical trial enrollment. Methods The derivation and internal validation cohorts originated from the Second Xiangya Hospital. The external validation cohort was generated from the Xiangya Hospital and the openly accessible database Medical Information Mart for Intensive CareIV. PC-AKI was defined based on the serum creatinine criteria of the Kidney Disease: Improving Global Outcomes (KDIGO). Six feature selection methods were used to identify the most influential predictors from 79 candidate variables. Deep neural networks (DNNs) were used to establish the model and compared with logistic regression analyses. Model discrimination was evaluated by area under the receiver operating characteristic curve (AUC). Low-risk and high-risk cutoff points were set to stratify patients. Results Among 4218 encounters studied, PC-AKI occurred in 10.3, 10.4 and 11.4% of encounters in the derivation, internal and external validation cohorts, respectively. The 14 variables-based DNN model had significantly better performance than the logistic regression model with AUC being 0.939 (95% confidence interval: 0.916–0.958) and 0.940 (95% confidence interval: 0.909–0.954) in the internal and external validation cohorts, respectively, and showed promising discrimination in subgroup analyses (AUC ≥ 0.800). The observed PC-AKI risks increased significantly from the low- to intermediate- to high-risk group (<1.0 to >50%) and the accuracy of patients not developing PC-AKI was 99% in the low-risk category in both the internal and external validation cohorts. Conclusions A DNN model using routinely available variables can accurately discriminate the risk of PC-AKI of hospitalized CKD patients following intravenous administration of ICM.