# Computational model-driven design to mitigate vein graft failure after coronary artery bypass

> **NIH NIH R01** · STANFORD UNIVERSITY · 2022 · $752,426

## Abstract

Coronary artery bypass graft (CABG) surgery is the gold standard treatment for patients with diffuse, multi-vessel
coronary artery disease, with >350,000 surgeries performed each year in the USA. Due to the limited availability
of arterial grafts, saphenous vein grafts (SVG) are used in >95% of patients. Despite advances in surgical
technique and post-surgical management, SVG stenoses and occlusions occur at alarmingly high rates: 5-10%
of SVGs fail within one month after surgery, 25% within 12-18 months, and 40-50% within 10 years, resulting in
significant morbidity and mortality. Currently, there are no clinically available means to prevent SVG failure
following CABG beyond optimal medical therapy. Mechanical stimuli, including hemodynamic loads and
associated vessel wall deformations and stresses, are known to contribute to the cell-mediated structural
changes leading to SVG failure, yet, the precise mechanobiological mechanisms remain poorly understood. In
preliminary studies, we quantified mechanical stimuli in CABG simulations, identifying hemodynamic markers
associated with SVG stenosis. Importantly, we introduced the first computational growth and remodeling (G&R)
framework that can delineate adaptive vs. maladaptive responses of vein grafts, incorporating optimization to
accelerate parameter estimation. With this model, we then predicted that an external bioabsorbable sheath,
present over a short post-operative period, could mitigate intermediate-term graft failure. Our scientific premise
is supported by a preliminary in vivo ovine study. Our collaborative multi-disciplinary team will address this
critical unmet need through tightly integrated computational model-driven design, experimental, and
clinical approaches to uncover arterialization mechanisms and evaluate a novel bioabsorbable sheath
device for SVG failure prevention. In Aim 1, we will develop the first G&R model of SVG arterialization
incorporating inflammation. We will inform and validate the model with data from a longitudinal rabbit surgical
study, in which we will perform surgery to interpose a jugular graft in the carotid artery. In Aim 2, we will
synthesize these data and models into a first-of-its-kind 3D fluid-solid-growth (FSG) simulator to predict SVG
disease progression, validated against an independent subset of animal data. To further inform our models, we
will characterize human SVG tissue with biaxial tissue testing. We will increase rigor by incorporating uncertainty
quantification. In Aim 3, we will design, optimize and evaluate a novel external sheath device for the prevention
of SVG failure, integrating in silico and large animal in vivo studies. We will rapidly 3D print sheath designs from
a unique class of bioabsorbable elastomeric materials with tunable degradation rates. This proposal brings
together a multidisciplinary team with expertise in cardiovascular simulation, vascular mechanobiology,
optimization, imaging, biomaterials, additive manufacturing, ...

## Key facts

- **NIH application ID:** 10539814
- **Project number:** 1R01HL159954-01A1
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Jay D. Humphrey
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $752,426
- **Award type:** 1
- **Project period:** 2022-08-15 → 2026-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10539814

## Citation

> US National Institutes of Health, RePORTER application 10539814, Computational model-driven design to mitigate vein graft failure after coronary artery bypass (1R01HL159954-01A1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10539814. Licensed CC0.

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