Jay L. Koyner, MD, is an Associate Professor of Medicine in the Section of Nephrology at the University of Chicago. He completed his undergraduate degree in Biophysics at The Johns Hopkins University. He then went on to complete medical school at the State University of New York at Stony Brook where he awarded a degree with distinction in research following completion of a Howard Hughes Medical Institute Research Fellowship. Dr. Koyner completed his internal medicine and nephrology training at the University of Chicago, where he currently serves as the Medical Director of the Inpatient Dialysis Unit and Director of ICU Nephrology. He is an expert in the care of patients at risk for and diagnosed with acute kidney injury (AKI) . He is spoken nationally and internationally on AKI and a variety of topics in the field of Critical Care Nephrology. Over the last decade he has served many roles for the American Society of Nephrology, including being a member of the Acute Kidney Injury Advisory Group, Co-Director of the Critical Care Nephrology pre-course (2014 to 2018), Co-Editor of the Nephrology Self-Assessment Program (NephSAP) for Acute Kidney Injury and Critical Care Nephrology (2016 to 2019) and currently he sits on the Scientific Advisory Board of the National Kidney Foundation. He has served on the editorial review board of the Clinical Journal of the American Society of Nephrology, The American Journal of Nephrology and Advances in Chronic Kidney Disease. In addition to being a dedicated clinician educator, Dr. Koyner’s critical care nephrology research interests have focused on the utilization of plasma and urine biomarkers to improve patient risk stratification and outcomes in the setting of AKI. He has contributed to several multicenter studies investigating biomarkers of AKI, including the TRIBE-AKI study, the Furosemide Stress Test study and several industry sponsored investigations. More recently he has begun developing and implementing an electronic health record derived AKI risk score, with the goal of improving the care of patients at high risk for the development of severe hospital acquired AKI. He has published over 90 peer-reviewed articles and book chapters on AKI and the care of patients with kidney injury in the ICU.
The University of Chicago
Chicago. IL
- Nephrology
2006
The University of Chicago
Chicago
- Internal Medicine
2004
Stony Brook School of Medicine
Stony Brook, NY
MD - Medicine
2001
The Johns Hopkins University
Baltimore, MD
BA - Biophysics
1996
10 tips on how to use dynamic risk assessment and alerts for AKI.
10 tips on how to use dynamic risk assessment and alerts for AKI. Clin Kidney J. 2024 Nov; 17(11):sfae325.
PMID: 39588357
Efficacy and safety of therapeutic alpha-1-microglobulin RMC-035 in reducing kidney injury after cardiac surgery: a multicentre, randomised, double-blind, parallel group, phase 2a trial.
Efficacy and safety of therapeutic alpha-1-microglobulin RMC-035 in reducing kidney injury after cardiac surgery: a multicentre, randomised, double-blind, parallel group, phase 2a trial. EClinicalMedicine. 2024 Oct; 76:102830.
PMID: 39318788
Recommendations for clinical trial design in acute kidney injury from the 31st acute disease quality initiative consensus conference. A consensus statement.
Recommendations for clinical trial design in acute kidney injury from the 31st acute disease quality initiative consensus conference. A consensus statement. Intensive Care Med. 2024 Sep; 50(9):1426-1437.
PMID: 39115567
The Road to Precision Medicine for Acute Kidney Injury.
The Road to Precision Medicine for Acute Kidney Injury. Crit Care Med. 2024 Jul 01; 52(7):1127-1137.
PMID: 38869385
Development and external validation of deep learning clinical prediction models using variable-length time series data.
Development and external validation of deep learning clinical prediction models using variable-length time series data. J Am Med Inform Assoc. 2024 May 20; 31(6):1322-1330.
PMID: 38679906
Using artificial intelligence to predict mortality in AKI patients: a systematic review/meta-analysis.
Using artificial intelligence to predict mortality in AKI patients: a systematic review/meta-analysis. Clin Kidney J. 2024 Jun; 17(6):sfae150.
PMID: 38903953
CCL14 testing to guide clinical practice in patients with AKI: Results from an international expert panel.
CCL14 testing to guide clinical practice in patients with AKI: Results from an international expert panel. J Crit Care. 2024 Aug; 82:154816.
PMID: 38678981
Assessing the role of Chemokine (C-C motif) ligand 14 in AKI: a European consensus meeting.
Assessing the role of Chemokine (C-C motif) ligand 14 in AKI: a European consensus meeting. Ren Fail. 2024 Dec; 46(1):2345747.
PMID: 38666354
CCL14 Predicts Oliguria and Dialysis Requirement in Patients with Moderate to Severe Acute Kidney Injury.
CCL14 Predicts Oliguria and Dialysis Requirement in Patients with Moderate to Severe Acute Kidney Injury. Blood Purif. 2024; 53(7):548-556.
PMID: 38636476
Sepsis-associated acute kidney injury: recent advances in enrichment strategies, sub-phenotyping and clinical trials.
Sepsis-associated acute kidney injury: recent advances in enrichment strategies, sub-phenotyping and clinical trials. Crit Care. 2024 03 21; 28(1):92.
PMID: 38515121
Mid-Career Award
American Society of Nephrology
2019