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One or more keywords matched the following properties of Koyner, Jay
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overview 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.
One or more keywords matched the following items that are connected to Koyner, Jay
Item TypeName
Concept Consensus Development Conferences as Topic
Academic Article Development and standardization of a furosemide stress test to predict the severity of acute kidney injury.
Academic Article Development of a Multicenter Ward-Based AKI Prediction Model.
Academic Article The Development of a Machine Learning Inpatient Acute Kidney Injury Prediction Model.
Academic Article Cardiac and Vascular Surgery-Associated Acute Kidney Injury: The 20th International Consensus Conference of the ADQI (Acute Disease Quality Initiative) Group.
Academic Article Drug management in acute kidney disease - Report of the Acute Disease Quality Initiative XVI meeting.
Academic Article Internal and External Validation of a Machine Learning Risk Score for Acute Kidney Injury.
Academic Article Not All Sepsis-Associated Acute Kidney Injury Is the Same: There May Be an App for That.
Concept Machine Learning
Grant Using Machine Learning for Early Recognition and Personalized Treatment of Acute Kidney Injury
Grant An Early Real-Time Electronic Health Record Risk Algorithm for the Prevention and Treatment of Acute Kidney Injury: A Randomized Trial of an Early Standardized, Personalized Nephrology Intervention
Academic Article Predicting the Development of Renal Replacement Therapy Indications by Combining the Furosemide Stress Test and Chemokine (C-C Motif) Ligand 14 in a Cohort of Postsurgical Patients.
Academic Article Development and external validation of multimodal postoperative acute kidney injury risk machine learning models.
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