Application of Machine Learning to Predict Acute Kidney Disease in Patients With Sepsis Associated Acute Kidney Injury - He, Jiawei; Lin, Jin; Duan, Meili (Frontiers in Medicine, 2021)
Background: We aimed to develop and validate machine learning models to predict the occurrence of acute kidney disease (AKD) in patients with sepsis-associated acute kidney injury (AKI). Methods: Using clinical data from patients with sepsis in the intensive care unit (ICU) at Beijing Friendship Hospital (BFH), we studied whether the following three machine learning models could predict the occurrence of AKD using demographic, laboratory, and other related variables: recurrent neural network-long short-term memory (RNN-LSTM), decision trees, and logistic regression. In addition, we externally validated the results in the Medical Information Mart for Intensive Care III (MIMIC III) database. The outcome was the diagnosis of AKD when defined as AKI prolonged for 7-90 days according to Acute Disease Quality Initiative-16. Results: Herein, 209 patients from BFH were included, with 55.5% of them diagnosed as having AKD. Furthermore, 509 patients were included from MIMIC III database, of which 46.4% were diagnosed as having AKD. Applying machine learning could successfully achieve very high accuracy (RNN-LSTM AUROC =1.000; decison trees AUROC=0.954; logistic regression AUROC=0.728), with RNN-LSTM showing the best results. Further analyses revealed that the change of non-renal Sequential Organ Failure Assessment (SOFA) score between the first day and third day (Δnon-renal SOFA) is instrumental in predicting the occurrence of AKD. Conclusion: Our results showed that machine learning, particularly RNN-LSTM, can accurately predict AKD occurrence. In addition, Δ SOFA non-renal plays an important role in predicting the occurrence of AKD.