Associate Professor of Medicine (Nephrology) and Public Health (Chronic Disease Epidemiology); Director, Clinical and Translational Research Accelerator (CTRA); Course Director, Interpretation of the Medical Literature; Co Director, Human Genetics and Clinical Research Core
Predicting Imminent AKI (AKI Tomorrow)
While AKI carries substantial risk, there remains no therapeutic intervention that can alter the course of AKI once it develops, beyond optimizing usual care. Our AKI Tomorrow project studies the use of a real-time predictive model to identify hospitalized patients who are at risk of developing AKI within the next 24 hours.
AKI Tomorrow Pilot Phase: Currently Active. This study will enroll 30 patients whom we predict may develop AKI in the next 24 hours. After informed consent, we obtain blood, urine, and access to their electronic medical record to determine the accuracy of our algorithm and measure biomarkers to understand the underlying pathophysiology in order to guide the additional steps required to improve care.
AKI Tomorrow Full Study: Anticipated start date of January 2019. By exporting data to an "artificial intelligence" server, we can create models that predict AKI with much greater fidelity and feed those predictions back into the clinical system.
Future Directions. We will integrate this predictive engine with clinical laboratory medicine to facilitate reflex testing of residual blood samples to augment the information already available in the electronic health record. Additionally, we will evaluate the efficacy of our AKI Tomorrow model. Does it meaningfully improve care for hospitalized patients by randomizing to usual care vs. prediction-based intervention?