Artificial Intelligence to Predict Outcomes in Patients with Acute Kidney Injury on Continuous Renal Replacement Therapy

ABSTRACT Acute kidney injury (AKI) affects up to half of critically ill patients admitted to intensive care units (ICU). In patients with AKI and hemodynamic instability, continuous renal replacement therapy (CRRT) is the preferred dialysis modality. ICU mortality in this vulnerable population is high but kidney recovery occurs in up to two-thirds of survivors. Universally accepted and accurate approaches for predicting survival or kidney recovery in these patients do not exist currently. This is clinically relevant as prediction of key outcomes could guide decision-making of CRRT delivery, goals of acute care, and personalized post-ICU care according to kidney recovery prognosis. Since there are no proven interventions to improve outcomes in these patients, identification of modifiable risk factors and sub-phenotypes is necessary to develop precision medicine approaches in CRRT. Due to advances in artificial intelligence (AI) and availability of multi-modal data, deep learning (DL) –a subset of AI– is a valuable approach that allows construction of accurate and reliable risk prediction models. Further, the use of novel algorithms such as the Feasible Solution Algorithm (FSA) could help identify patient sub-phenotypes and model applications. We propose to develop and validate innovative and reproducible DL approaches to predict RRT-free survival at actionable timepoints and use FSA to identify patient sub-phenotypes with differing RRT-free survival risk according to multi- modal data. Our published preliminary data demonstrated superiority of DL models compared to optimized logistic regression for RRT-free survival prediction. Prediction of 24-hour mortality was improved by incorporating time-series data during CRRT. We hypothesize that time-series multi-modal data (including EHR and CRRT machine data) will generate accurate and generalizable risk prediction to guide clinical interventions and identify sub-phenotypes for model interpretation and clinical utility testing. We will utilize datasets from 9 institutions that encompass multi-modal EHR clinical data and programmatic and therapy data from CRRT machines for model and sub-phenotyping development, testing, and independent validation. This innovative research will 1) assist development of clinical decision support platforms to guide informed CRRT delivery and improve clinical outcomes and 2) identify sub-phenotypes of patients that could benefit from more personalized and testable novel CRRT interventions.