Dillon Dzikowicz, PhD, RN, PCCN

Bio

Dillon Dzikowicz, PhD, RN, PCCN, is an assistant professor in nursing at the University of Rochester, with a secondary appointment at the Clinical Cardiovascular Research Center at the University of Rochester Medical Center. Dzikowicz aims to advance cardiovascular care through research interests in cardiovascular disease, clinical informatics, and machine learning. His primary focus lies on the task of phenotyping physiological signals (such as electrocardiography, vectorcardiography, photoplethysmography, etc.) among patients with acute coronary syndromes and heart failure, as well as with elevated risk for sudden cardiac death. By leveraging his expertise in these areas, Dzikowicz aims to enhance understanding of these conditions and pave the way for more precise and personalized interventions.

Dzikowicz also contributes to the advancement of nursing practice and research as an active member of the American Association of Critical Care Nurses, American Heart Association, and the International Society for Computerized Electrocardiology and is a regular contributor to the American Journal of Critical Care’s ECG Puzzler, a case-study column which addresses electrocardiogram (ECG) interpretation for nurses. His work has garnered recognition and support from Sigma Theta Tau, Emergency Nurses Association, and the Council for the Advancement of Nursing Research. Most recently, his dissertation "Differentiating NSTEMI Patients with and without an Occluded Culprit Artery” received multiple recognitions including the 2023 ENRS/Council for the Advancement of Nursing Science Dissertation Award. Prior to joining the School of Nursing faculty in 2023, Dzikowicz completed his bachelor’s, master’s and PhD degrees at the University of Rochester School of Nursing. He currently practices as a cardiac nurse at Strong Memorial Hospital.

Current Focus

My current research focuses on utilizing machine learning and novel electrocardiographic features to improve the stratification of cardiovascular diseases including myocardial infarction (e.g. heart attack), heart failure, and sudden cardiac death. In particular, my dissertation research aims to improve the stratification of myocardial infarction patients who present without significant changes on the electrocardiogram. This research utilizes the electronic medical records of University of Rochester Medical Center providing a plethora of real-world data to inform scientific inquiry, and improve clinical practice and outcomes.