Use of Deep Learning to Predict Acute Kidney Injury After Intravenous Contrast Media Administration: Prediction Model Development Study

Abstract

Background Precise prediction of contrast media–induced acute kidney injury (CIAKI) is an important issue because of its relationship with poor outcomes. Objective Herein, we examined whether a deep learning algorithm could predict the risk of intravenous CIAKI better than other machine learning and logistic regression models in patients undergoing computed tomography (CT). Methods A total of 14,185 patients who were administered intravenous contrast media for CT at the preventive and monitoring facility in Seoul National University Hospital were reviewed. CIAKI was defined as an increase in serum creatinine of ≥0.3 mg/dL within 2 days or ≥50% within 7 days. Using both time-varying and time-invariant features, machine learning models, such as the recurrent neural network (RNN), light gradient boosting machine (LGM), extreme gradient boosting machine (XGB), random forest (RF), decision tree (DT), support vector machine (SVM), κ-nearest neighbors, and logistic regression, were developed using a training set, and their performance was compared using the area under the receiver operating characteristic curve (AUROC) in a test set. Results CIAKI developed in 261 cases (1.8%). The RNN model had the highest AUROC of 0.755 (0.708-0.802) for predicting CIAKI, which was superior to that obtained from other machine learning models. Although CIAKI was defined as an increase in serum creatinine of ≥0.5 mg/dL or ≥25% within 3 days, the highest performance was achieved in the RNN model with an AUROC of 0.716 (95% confidence interval [CI] 0.664-0.768). In feature ranking analysis, the albumin level was the most highly contributing factor to RNN performance, followed by time-varying kidney function. Conclusions Application of a deep learning algorithm improves the predictability of intravenous CIAKI after CT, representing a basis for future clinical alarming and preventive systems.

A novel explainable online calculator for contrast-induced AKI in diabetics: a multi-centre validation and prospective evaluation study

“…Yin et al [ 23 ] constructed a CIAKI prediction model using 13 preprocedural indicators through an RF algorithm, revealing an AUC of 0.907 and an accuracy of 80.8%. Other researchers also found that GBDT [ 24 ] and RNN [ 25 ] could perform well in predicting CIAKI. Moreover, Sun et al [ 26 ] exhibited that in patients with ACS, the LASSO + LR-based nomogram model provided a better prediction of CIAKI than the Mehran score (AUC was 0.835 and 0.762, respectively).…”

Explainable Preoperative Automated Machine Learning Prediction Model for Cardiac Surgery-Associated Acute Kidney Injury

“…Traditional statistical models such as logistic regression analysis have been previously utilized to construct such prognostication tools [ 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 ]. In recent years, ML predictive algorithms have emerged as a method to handle high-dimensional, unstructured, and complex structured data including hospitalized patient with AKI [ 27 , 28 , 29 , 30 , 31 ]. While autoML has been shown to be very effective, with high predictive performance comparable to human hyperparameter optimization and with higher time-efficient workflow when compared to non-automated ML [ 41 , 43 ], autoML has never been utilized in the development of AKI prediction models.…”

“…Artificial intelligence (AI) and machine learning (ML) have been increasingly applied to individualized medicine [ 21 , 22 , 23 , 24 , 25 , 26 ], including the prediction of AKI in various settings [ 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 ]. ML algorithms can handle nonlinear, complex, and multidimensional data [ 36 , 37 ], and recent studies have shown high predictive performance from ML algorithms that outperform traditional statistical analyses [ 38 , 39 ].…”