Posts by Nazlee Zebardast, MD, MSc
-
Usage Patterns of Minimally Invasive Glaucoma Surgery Differ by Glaucoma Type
Researchers at Mass Eye and Ear, led by Nazlee Zebardast, MD, MSc, examined six years of nationwide U.S. data on standard and minimally invasive glaucoma surgery. They found that the specific glaucoma diagnosis influences the choice of procedure, as well as concurrent and subsequent surgeries.
-
Minimally Invasive Glaucoma Surgery Has Been Adopted Rapidly in the U.S.
Ophthalmologists at Massachusetts Eye and Ear/Massachusetts General Hospital have determined that between 2013 and 2018, the number of minimally invasive glaucoma surgeries surpassed the number of conventional glaucoma surgeries, despite limited evidence of long-term safety or effectiveness.
-
Using Genetics and Big Data to Determine Risk for Developing Glaucoma
Nazlee Zebardast, MD, MSc, director of Glaucoma Imaging at Mass Eye and Ear/Massachusetts General Hospital Department of Ophthalmology, discusses the use of big data to determine endophenotypes for disease in glaucoma.
Biography
Dr. Zebardast is a clinician scientist and full-time member of the Glaucoma Service at Mass Eye and Ear. She specializes in the treatment of adult glaucoma and combined glaucoma and cataracts, with a particular interest in minimally invasive glaucoma surgeries (MIGS).
She has received numerous awards and honors for her academic and research accomplishments and has published in top ophthalmology journals. With a focus on global health and epidemiology research, she has worked with researchers at Aravind Eye Institute in India to study angle-closure glaucoma, a blinding eye disease. Among other findings, this study determined that siblings of individuals with known angle closure have a greater than 1 in 3 risk of developing the condition. Additionally, she has worked with a many large datasets to understand the prevalence of eye disease and its impact. She plans to use imaging to better understand glaucoma progression in angle-closure eyes and to use big data and machine learning methodologies to detect glaucoma and characterize its progression.