Abstract 11629: Multicenter Development and Validation of a Machine Learning Risk Model to Predict Right Ventricular Failure Following Mechanical Circulatory Support: The STOP-RVF Score
Christos P. Kyriakopoulos(University of Utah), Stavros G. Drakos(University of Utah), Rami Alharethi(Intermountain Medical Center), Palak Shah(Alaska Heart and Vascular Institute), Theodoros V. Giannouchos(University of Alabama at Birmingham), Naila Ijaz(University of the Punjab), James C. Fang, Konstantinos Sideris(University of Utah), Elizabeth Dranow(University of Utah), Craig H. Selzman(University of Utah), Antigone Koliopoulou(University of Utah), Michael Bonios(University of Utah), Adithya Peruri(Henry Ford Hospital), Iosif Taleb(University of Utah), Josef Stehlik(Intermountain Medical Center), W. Caine(University of Utah), Daniel Tang(Bon Secours Heart & Vascular Institute), M. Nelson(Intermountain Healthcare), Jennifer Cowger(Henry Ford Health System), Zachary Demertzis(Henry Ford Hospital), Ashley Elmer(Intermountain Healthcare), Thomas C. Hanff(University of Pennsylvania), Hassan Nemeh(Henry Ford Hospital), Omar Wever‐Pinzon(University of Utah)
Cited by 0
Related Papers
Shared Genetic Predisposition in Peripartum and Dilated Cardiomyopathies
|New England Journal of Medicine|2016|597
2022 ACC/AHA/HFSA Guideline for the Management of Heart Failure
|Journal of Cardiac Failure|2022|406
Interventions for Frailty Among Older Adults With Cardiovascular Disease
|Journal of the American College of Cardiology|2022|393
HF STATS 2024: Heart Failure Epidemiology and Outcomes Statistics An Updated 2024 Report from the Heart Failure Society of America
|Journal of Cardiac Failure|2024|357