Andrew D. Legarreta, MD, joined the University of Pittsburgh Department of Neurological Surgery residency program in July 2019, after earning his medical degree from Vanderbilt University School of Medicine. He completed his undergraduate studies at Duke University, where he earned a bachelor of arts in history.
During his time in medical school, Dr. Legarreta focused on the effects of sports-related concussions among high school athletes. His research specifically explored predictors of post-concussion syndrome and, separately, analyzed structural and functional neuroimaging findings in football players. His peer-reviewed studies have been presented in both oral and poster formats at regional and national neurosurgical conferences.
In his residency, Dr. Legarreta has concentrated on minimally invasive spine surgery, particularly in the context of deformity correction. His current research involves the application of machine learning techniques to various aspects of spine surgery.
In his leisure time, Dr. Legarreta enjoys playing guitar, traveling internationally and playing golf. He is originally from Buffalo, New York.
Dr. Legarreta’s publications can be reviewed through the National Library of Medicine's publication database.
Specialized Areas of Interest
Professional Organization Membership
Education & Training
- BA, History, Duke University, 2014
- MD, Vanderbilt University School of Medicine, 2019
Honors & Awards
- Cornelius Vanderbilt Scholarship, Vanderbilt University School of Medicine, 2015-19
Research Activities
Dr. Legarreta’s 2024–25 research focused on spine biomechanics, imaging analytics, and perioperative risk. He co-authored studies identifying predictors of subsidence after lateral lumbar interbody fusion, showing that increased fusion length was protective, while lateral plating was not. He contributed to machine learning analyses using CT-derived Hounsfield units as a surrogate for DEXA in assessing bone quality and fracture risk. Additional projects explored revision strategies for proximal junctional failure, the use of AI for radiology summarization, and evaluation of digital neurosurgical education platforms.