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Ismail El-Hamamsy, MD, PhD (he/him/his)

Randall B. Griepp Professor of Surgery
The Mount Sinai Hospital
New York, New York, United States

Ismail El-Hamamsy, MD, PhD, is the Randall B. Griepp Professor of Cardiovascular Surgery at the Icahn School of Medicine at Mount Sinai and the System Director of Aortic Surgery for the Mount Sinai Health System.

Dr. El-Hamamsy’s principal surgical focus is reconstructive aortic valve surgery in young adults, including patients with heritable and connective tissue disorders. Widely acknowledged as one the world’s best aortic surgeons, Dr. El-Hamamsy has performed more than 1000 Ross and valve-sparing procedures. Notably, he has spearheaded a global Ross renaissance over the last 15 years thanks to his groundbreaking research and unique expertise with the operation. Indeed, the Ross Procedure is the only aortic surgery that has been shown to restore patients’ life expectancy to that of their healthy peers. In addition to the Ross procedure, Dr. El-Hamamsy has pioneered techniques for bicuspid aortic valve repair and valve-sparing aortic root replacement, and established one of the busiest aortic practices worldwide.

In recognition of his leadership, scholarship and surgical skills, Dr. El-Hamamsy was elected President of the Heart Valve Society by his peers, as well as to the Board of Directors of the American Association for Thoracic Surgery – the world’s oldest and most prestigious organization dedicated to thoracic surgery. He is also a Fellow of the Royal College of Surgeons of Canada.

As a researcher, Dr. El-Hamamsy has published more than 250 scientific papers in peer-reviewed journals including The Lancet, the Journal of the American College of Cardiology, and Circulation among others. He has also co-authored several book chapters in leading medical textbooks; and has delivered more than 150 invited lectures around the world. Dr. El-Hamamsy currently leads a research team exploring innovative surgical approaches to aortic valve reconstruction, as well as exploring the natural history of aortic aneurysms using machine learning tools.

Disclosure information not submitted.

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