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Acute kidney injury (AKI) is associated with increased morbidity and mortality in intensive care units (ICU). The sudden episode of kidney failure may lead to end-stage renal disease (ESRD) or deaths, and has been related to significantly increasing costs of ICU admissions and treatments. Early prediction of AKI inpatient mortality will help decision-making, and benefit resource allocation in ICU. Therefore, it is crucial to develop an early warning system for AKI prediction. We aimed to create a more comprehensive predictive model for 1-year AKI mortality. A cohort of 2,247 patients with AKI was assembled, of which the in-hospital mortality was 36.67%. Longitudinal data of each patient were collected from the Medical Information Mart for Intensive Care III (MIMIC-III) dataset. An interpretable XGBoost risk model was developed and validated by 10-fold cross validation. Model predictors included 11 routinely collected AKI-related laboratory measurements, 8 complications of AKI, and demographic data. An artificial neural network (ANN) model was also developed in parallel for comparison. The XGBoost model demonstrated an area under the receiver-operating characteristic curve (AUC) of 0.83, which was superior to ANN (AUC = 0.79). Our model was able to predict mortality of AKI in ICU with high accuracy. Our model can predict 1-year AKI mortality. Furthermore, it had great potential for identifying at-risk patients in ICU. These findings indicated that our approach might offer opportunities for better resource utilization and better administration of AKI.
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