Electronic Alerts for Acute Kidney Injury Amelioration (ELAIA-1)


Acute kidney injury (AKI) affects up to 20% of hospitalized patients and increases the risk of dying in the hospital by a factor of 10. However, AKI is asymptomatic and can go unrecognized by even well-trained medical providers.  Automated detection of AKI coupled with provider alerting has the potential to improve outcomes. We are conducting three randomized trials of AKI alerts to evaluate the ability of alerts to slow the progression of AKI, avoid dialysis, and save lives.

Electronic Alerts for Acute Kidney Injury Amelioration, ELAIA-1 will randomize roughly 6,000 patients with AKI to AKI alerts or usual care with a goal of determining whether alerts reduce the rate of worsening of acute kidney injury, dialysis, or death.

Bio Profile

Francis Perry Wilson, MD, MS

Principal Investigator

Assistant Professor of Medicine (Nephrology)

Interim Director, Program of Applied Translational Research

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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


Contact Information

For more information, or if you are interested in collaborating on this study, please contact Melissa Martin

Project Funding

Funding for this project comes, in part, from the following grants:

K23 DK097201 (NIH/NIDDK)
"Mediators & Prognostic Value of Muscle Mass & Function in Chronic Kidney Disease" 

R01 DK113191 (NIH/NIDDK) "Optimizing Electronic Alerts for Acute Kidney Injury" 

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