2016 IARS Mentored Research Award $150,000
Baylor College of Medicine
Dr. Vu’s Research
Predictive Analytic Tool for Diagnosis of Coronary Allograft Vasculopathy
The objective of this project is to develop and study a new predictive algorithm for pediatric transplant associated coronary allograft vasculopathy (CAV), an immunologic phenomenon affecting 50% of patients 5-15 years after orthotopic heart transplants. Diagnosis of disease remains difficult due to nonspecific symptomatology, and many patients are subjected to the increased morbidity and mortality of numerous cardiac catheterizations and exposure to anesthesia when disease is suspected. To improve diagnosis, the performance of a novel electrocardiogram (ECG) algorithm in distinguishing CAV will be retrospectively tested in patients undergoing cardiac catheterization after heart transplantation. The algorithm has the capability of measuring dynamic ST segment changes of a reconstructed 3-dimensional ST segment vector from standard 5 lead ECG in real-time. In addition, biomarker panel testing utilizing markers of inflammation and myocyte injury will be performed to establish a multivariate model to improve the predictive power in CAV diagnosis. Finally, utilizing discrete laboratory and real time physiologic variables obtained in the cardiac catheterization lab (heart rate, heart rate variability, ST instability), a predictive analytic algorithm will be developed as a risk-assessment tool for CAV in comparison to the current gold standard of diagnosis, angiography. Because CAV remains one of the leading causes of morbidity after pediatric orthotopic heart transplantation, a predictive analytic tool will aid in the earlier diagnosis of this critical disease, and in the future may guide therapies, such as medical management, revascularization, and alert the need for retransplantation.
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