Our Team
Peter Kotanko, MD, FASN
Head of Biomedical Evidence Generation and
Renal Research Institute

Peter Kotanko, MD, is Research Director at the Renal Research Institute (RRI), New York. Prior to joining RRI, from 1997 to 2007 he served as vice chair of a department of internal medicine at an academic teaching hospital in Graz, Austria. Prior to moving to Graz in 1989, he worked from 1982 to 1989 in the Department of Physiology and the University Clinic of Internal Medicine in Innsbruck, Austria. From 1995 to 1996 he trained in nephrology at the Hammersmith Hospital, London, United Kingdom. He is Adjunct Professor of Medicine and Nephrology at the Icahn School of Medicine at Mount Sinai in New York and holds a teaching appointment at the Medical University of Innsbruck. He has authored and co-authored over 350 publications and book chapters, and he holds multiple patents in the field of kidney replacement therapy. He is an awardee of the 2019 KidneyX prize for innovations in dialysis and the 2021 KidneyX COVID-19 Kidney Care Challenge. He is a Fellow of the American Society of Nephrology.
Recent Articles by Peter Kotanko, MD, FASN
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Hemodialysis InternationalJul 21, 2023
Interactions between intradialytic central venous oxygen sa...
Priscila Preciado, Laura Rosales, Hanjie Zhang, Je...Abstract Introduction: In maintenance hemodialysis (HD) patients, low central venous oxygen saturation (ScvO2 ) and small decline in relative blood volume (RBV) have been associated with adverse outcomes. Here we explore the joint association between ScvO2 and RBV change in relation to all-cause mortality. Methods: We conducted a retrospective study in maintenance HD patients with central venous catheters as... -
Nephrology Dialysis TransplantationJul 03, 2023
Real-time prediction of intradialytic hypotension using mach...
Hanjie Zhang, Lin-Chun Wang, Sheetal Chaudhuri, Aa...Abstract Background: In maintenance hemodialysis patients, intradialytic hypotension (IDH) is a frequent complication that has been associated with poor clinical outcomes. Prediction of IDH may facilitate timely interventions and eventually reduce IDH rates. Methods: We developed a machine learning model to predict IDH in in-center hemodialysis patients 15-75 min in advance. IDH was defined as systolic blood... -
American journal of kidney diseases the official journal of the National Kidney FoundationJun 26, 2023
The Impact of a Wearable Activity Tracker and Structured Fee...
Rakesh Malhotra, Sina Rahimi, Ushma Agarwal, Ronit...Abstract Rationale & objective: People with end-stage kidney disease (ESKD) have very low physical activity, and the degree of inactivity is strongly associated with morbidity and mortality. We assessed the feasibility and effectiveness of a 12-week intervention coupling a wearable activity tracker (FitBit) and structured feedback coaching versus wearable activity tracker alone on changes in physical... -
Clinical journal of the American Society of NephrologyJun 01, 2023
Ultrafiltration Rate Levels in Hemodialysis Patients Associa...
Ariella Mermelstein, Jochen G Raimann, Yuedong Wan...Abstract Background: We hypothesized that the association of ultrafiltration rate with mortality in hemodialysis patients was differentially affected by weight and sex and sought to derive a sex- and weight-indexed ultrafiltration rate measure that captures the differential effects of these parameters on the association of ultrafiltration rate with mortality. Methods: Data were analyzed from the US Fresenius... -
Clinical journal of the American Society of NephrologyJun 01, 2023
Artificial Intelligence and Machine Learning in Dialysis: Re...
Peter Kotanko, Hanjie Zhang, Yuedong WangNo abstract available. -
Clinical journal of the American Society of NephrologyMay 11, 2023
Inclement Weather and Risk of Missing Scheduled Hemodialysis...
Richard V Remigio, Jochen G Raimann, Peter Kotanko...Abstract Background: Nonadherence to hemodialysis appointments could potentially result in health complications that can influence morbidity and mortality. We examined the association between different types of inclement weather and hemodialysis appointment adherence. Methods: We analyzed health records of 60,135 patients with kidney failure who received in-center hemodialysis treatment at Fresenius Kidney Care clinics across the Northeastern US... -
Hanjie Zhang, Sabrina Casper, Peter Kotanko, Joche...Abstract Background: Attaining the optimal balance between achieving adequate volume removal while preserving organ perfusion is a challenge for patients receiving maintenance hemodialysis. Current strategies to guide ultrafiltration are inadequate. Methods: We developed an approach to calculate plasma refill rate throughout hemodialysis using hematocrit and ultrafiltration data in a retrospective cohort of patients receiving maintenance hemodialysis at...
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Karlien J Ter Meulen, Xiaoling Ye, Yuedong Wang, L...No abstract available
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Hemodialysis InternationalNov 20, 2022
Predicting mortality risk in dialysis: Assessment of risk fa...
Sheetal Chaudhuri, John W Larkin, Murilo Guedes, Y...Abstract Introduction: Several factors affect the survival of End Stage Kidney Disease (ESKD) patients on dialysis. Machine learning (ML) models may help tackle multivariable and complex, often non-linear predictors of adverse clinical events in ESKD patients. In this study, we used advanced ML method as well as a traditional statistical method to develop and compare the... -
Paulo P Galuzio, Alhaji Cherif, Xia Tao, Ohnmar Th...Abstract In patients with kidney failure treated by hemodialysis, intradialytic arterial oxygen saturation (SaO2) time series present intermittent high-frequency high-amplitude oximetry patterns (IHHOP), which correlate with observed sleep-associated breathing disturbances. A new method for identifying such intermittent patterns is proposed. The method is based on the analysis of recurrence in the time series through the...


Diversity, creativity, and enthusiasm team up to advance patient care.
Peter Kotanko, MD, FASN
Head of Biomedical Evidence Generation and Renal Research Institute