Sunday, October 27, 2024
10/27/2024
Project Summary/Abstract Coarctation of the aorta (CoA) is a congenital cardiovascular (CV) disease characterized by a severe stenosis of the main artery delivering blood from the heart to the body. CoA affects 5,000 to 8,000 births annually in the U.S. Unfortunately many CoA patients have morbidity even after successful surgery. Hypertension (HTN) is the most common form of morbidity, which is worsened by irreversible aortic remodeling and stiffening from coarctation induced mechanical stimuli. Treatment for CoA is currently based on a peak-to-peak blood pressure gradient (BPGpp) ≥20 mmHg quantified from invasive catheterization. Unfortunately, there is a lack of data for the current BPGpp treatment value. Preliminary data applied for the current research shows that a BPGpp <20 mmHg (i.e. below the current treatment threshold) alone does not avoid HTN, and can produce coarctation-induced mechanical stimuli resulting in pathologic aortic remodeling with stiffening. We are poised to propose new lower BPGpp threshold guidance that avoids irreversible aortic remodeling based on recent data. However, the ultimate link between new thresholds and HTN in CoA patients remains to be further investigated, which is the objective of the current research. Our central hypothesis is that a personalized medicine approach developed and implemented in the current project will improve objectivity and diagnostic tools for treatment of CoA to limit the severity and duration of mechanical consequences thereby reducing instances of HTN. Aim 1 will provide a rapid path to translation of new BPGpp thresholds by expanding machine-learning based decision tree methods using predictors governing follow-up HTN status as part of the largest study of outcomes from CoA patients to date. Aim 2 will use a computational growth and remodeling (G&R) algorithm to predict irreversible aortic remodeling and HTN in CoA patients. In contrast to the decision tree analysis from Aim 1 that is robust but does not natively consider mechanisms of aortic remodeling, prediction of HTN from the G&R algorithm is based on mechanical stimuli as well as patient factors. Aim 3 will scrutinize the diagnostic performance of a new index called the continuous flow pressure gradient relative to other indices of coarctation severity assessment in order to arrive at the most accurate means of determining BPGpp, which is a critical factor in the machine-learning based decision tree and G&R tools to be developed for clinical use. These aims will provide translational tools that ultimately prevent remodeling linked to HTN from coarctation-induced mechanical stimuli as part of the largest such study to date. The collective results will lead to new guidance that avoids unnecessary invasive testing and allows clinicians to intervene before precursors of HTN. Translating results from the current proposal in these ways is aligned with NHLBI's mission of prevention and treatment of CV disease, enhancing the health of all individuals so that they can live longer and more fulfilling lives, and educating the next generation of scientists in the methods applied for this purpose.
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