Research interests
Population-level data analysis to identify patients at high risk for adverse sequelae of rhythm disorders who may benefit from early intervention[7]
ZL
Zak Loring
As of 2024 · Last known relationship
As of 2024 · Last known relationship
Listed grant for a noninvasive hemodynamic sensor patch for remote early detection and prevention of heart failure with left bundle branch block
Listed grant for cardiac function and proteomic biomarkers in individuals with perinatal HIV infection or exposure
Listed grant for ICD analysis
Listed grant for probing regional metabolism during exercise through coronary sinus sampling
Listed grant for score validation
Zak Loring is a Duke cardiologist whose work connects cardiac electrophysiology with data-driven approaches to rhythm disorders and device therapy. He is an Associate Professor of Medicine and a member of the Duke Clinical Research Institute. His clinical focus includes heart rhythm disorders and cardiac implantable electronic devices. His research uses electrocardiographic signal processing, analytic techniques, and population-level data to improve patient phenotyping, identify candidates for electrophysiology procedures, and predict which patients with left bundle branch block may benefit from early cardiac resynchronization therapy or conduction system pacing. Loring earned an M.D. from Duke in 2013, completed internal medicine residency at UCSF from 2013 to 2016, and completed cardiology fellowship at Duke from 2016 to 2020. He previously held an Assistant Professor of Medicine appointment.
Each topic is linked to its supporting source in Sources.
Training includes Cardiology fellowship at Duke University, School of Medicine, Internal medicine residency at University of California San Francisco, School of Medicine, Appeared with Jon Piccini on Heart Rhythm TV to discuss echocardiographic remodeling in atrial fibrillation, and MD, plus 3 more records.
Explore education and trainingAs of 2020
As of 2016
As of 2024
Completed 2013
Population-level data analysis to identify patients at high risk for adverse sequelae of rhythm disorders who may benefit from early intervention[7]
Predicting which patients with left bundle branch block may benefit from early cardiac resynchronization therapy or conduction system pacing[7]
Signal processing of electrocardiographic data and novel analytic techniques to phenotype patients and identify candidates for interventional electrophysiology procedures[7]