# Mechanisms of morbidity after correcting aortic coarctations of varying severity

> **NIH NIH R01** · MEDICAL COLLEGE OF WISCONSIN · 2024 · $595,457

## Abstract

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 ...

## Key facts

- **NIH application ID:** 10978615
- **Project number:** 2R01HL142955-06
- **Recipient organization:** MEDICAL COLLEGE OF WISCONSIN
- **Principal Investigator:** John Frank LaDisa
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $595,457
- **Award type:** 2
- **Project period:** 2018-07-01 → 2028-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10978615

## Citation

> US National Institutes of Health, RePORTER application 10978615, Mechanisms of morbidity after correcting aortic coarctations of varying severity (2R01HL142955-06). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10978615. Licensed CC0.

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