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?

Bio Profile

Francis Perry Wilson, MD, MS

Assistant Professor of Medicine (Nephrology)

Interim Director, Program of Applied Translational Research

View Full Profile

Bio Profile

Ugochukwu Ugwuowo

Postdoctoral Associate

View Full Profile

Aditya Biswas
Research Assistant, Yale University

Boian Etropolski
Data Scientist, Yale University

Harold Feldman, MD, MSCE
George S. Pepper Professor of Public Health and Preventative Medicine, Yale University

Amit Garg, MD, PhD
Professor of Medicine (Nephrology), Western University, London, Ontario, Canada

Stephen Latham, JD, PhD
Director, Interdisciplinary Center for Bioethics, Yale University 

Haiqun Lin, MD, PhD
Associate Professor of Biostatistics, School of Public Health, Yale University

Melissa Martin
Research Associate, Yale University

Paul M. Palevsky, MD
Professor of Medicine, Renal-Electrolyte Division, University of Pittsburgh 

Chirag R. Parikh, MD, PhD
Adjunct Professor of Medicine, Yale University 

Yu Yamamoto
Statistician, Yale University