# Uncertainty aware virtual treatment planning for peripheral pulmonary artery stenosis

> **NIH NIH R01** · STANFORD UNIVERSITY · 2024 · $676,028

## Abstract

Congenital heart disease (CHD) affects 1/100 babies and is the leading cause of infant mortality in the U.S.
Pulmonary artery (PA) stenosis is common in CHD patients and is particularly challenging to treat when occurring
in the periphery of the PA tree. Peripheral pulmonary stenosis (PPS), often consisting of numerous vessel
narrowings at proximal and distal bifurcation levels, can lead to persistent RV hypertension, RV failure, and even
death. Most institutions treat PPS patients with stenting and angioplasty limited to the proximal (central and lobar)
PAs only. These catheter-based interventions, however, are often ineffective at reducing right ventricular
pressures and are associated with poor and unpredictable outcomes. Comprehensive surgical reconstruction,
involving patch augmentation of ALL stenoses (central, lobar, segmental PAs), can achieve long-term RV
pressure reduction with low morbidity and mortality, but requires >10-hour procedures and specialized expertise
available only at select institutions. Because treatment strategies continue to be debated nationally, and
outcomes remain poor, there is a pressing unmet need for novel clinical decision support tools. We aim to
develop two complementary modeling methods to support clinical decision making in CHD patients with
PPS: 1) a mechanistic multiscale model of pulmonary fluid solid growth melding fluid structure
interaction (FSI) and vascular growth and remodeling (G&R), and 2) a real-time uncertainty-aware digital
twin model for virtual treatment planning to aid clinicians in identifying optimal treatment strategies. To
accomplish these goals, we propose three specific aims: (1) Characterize mechanical and immunohistochemical
properties of PA tissue in human PPS patients via biaxial testing and histology; (2) Develop and validate a
computational modeling framework (melding hemodynamics and G&R) capable of predicting post-treatment
hemodynamics in PPS; and (3) Develop and validate a fast, interactive Bayesian modeling framework for virtual
treatment planning under uncertainty to aid near real-time clinical decision making for PPS, leveraging reduced
order models. Our proposed study will tightly integrate modeling and experiments to improve physiological fidelity
and clinical relevance of patient-specific models in an understudied patient population. The biaxial mechanical
characterization of pediatric human PA tissue will provide much needed data on tissue properties in CHD which
are currently absent from the literature. This project assembles an interdisciplinary team of engineers with
expertise in hemodynamics modeling, cardiovascular biomechanics, mechanical characterization of biological
tissues, and uncertainty quantification, and clinicians with expertise in pediatric cardiology/pulmonary vascular
abnormalities, cardiothoracic surgery, and cardiac catheterization. Our translational objectives are to: (1)
systematically compare treatment options for PPS and thus challenge the cur...

## Key facts

- **NIH application ID:** 10844440
- **Project number:** 5R01HL167516-02
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Jeffrey A. Feinstein
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $676,028
- **Award type:** 5
- **Project period:** 2023-07-01 → 2028-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10844440

## Citation

> US National Institutes of Health, RePORTER application 10844440, Uncertainty aware virtual treatment planning for peripheral pulmonary artery stenosis (5R01HL167516-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10844440. Licensed CC0.

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