A deep-learning model to continuously predict severe acute kidney injury based on urine output changes in critically ill patients

BACKGROUND: Acute Kidney Injury (AKI), a frequentcomplication of pateints in theIntensive Care Unit (ICU), is associated with a high mortality rate. Early prediction of AKI is essential in order to trigger the use of preventive careactions.METHODS: The aim of this study was to ascertain the accuracy of two mathematical analysis models in obtaining a predictive score for AKI development. A deep learning model based on a urine output trends was compared with a logistic regression analysis for AKI prediction in stages 2 and 3 (defined as the simultaneous increase of serum creatinine and decrease of urine output, according to the Acute Kidney Injury Network (AKIN) guidelines). Two retrospective datasets including 35,573 ICU patients were analyzed. Urine output data were used to train and test the logistic regression and the deep learning model.RESULTS: The deep learning model definedan area under the curve (AUC) of 0.89 (±0.01), sensitivity=0.8 and specificity=0.84, which was higher than the logistic regression analysis. The deep learning model was able to predict 88% of AKI cases more than 12h before their onset: for every 6 patients identified as being at risk of AKI by the deep learning model, 5 experienced the event. On the contrary, for every 12 patients not considered to be at risk by the model, 2 developed AKI.CONCLUSION: In conclusion, by using urine output trends, deep learning analysis was able to predict AKI episodes more than 12h in advance, and with a higher accuracy than the classical urine output thresholds. We suggest that this algorithm could be integrated inthe ICU setting to better manage, and potentially prevent, AKI episodes.