# Risk stratification of uncomplicated type B aortic dissection using clinical and engineering analysis

> **NIH NIH R01** · EMORY UNIVERSITY · 2024 · $534,965

## Abstract

Project Summary
 Type B Aortic Dissection (TBAD) is a lethal disease which occurs when a tear develops in
the inner lining (intimal layer) of the aorta, causing the layers of the aortic wall to separate (dissect)
creating “true” and “false” lumens. Complicated TBADs with presence of either organ
malperfusion or aortic rupture have a high in-hospital mortality rate and require emergent surgical
or endovascular therapy. Uncomplicated TBADs have been traditionally managed with optimal
medical therapy (OMT) consisting of aggressive anti-hypertensive therapy and surveillance
imaging. OMT results in low in-hospital mortality rates, but dismal long-term survival rates of 48-
66%, and overall intervention-free survival rates of less than 50% secondary to aortic aneurysm
formation and rupture. These poor long-term outcomes support a paradigm change in the
treatment of the uncomplicated TBADs. Thus, there is an urgent and unmet clinical need for
promptly identifying those uncomplicated TBAD patients that will likely fail OMT in the acute phase,
and thus benefit from early intervention such as Thoracic Endovascular Aortic Repair (TEVAR).
 Therefore, the objective of this project is to develop a risk stratification model for predicting
both failure of OMT and the optimal timing of intervention in uncomplicated TBAD patients. To
achieve this goal, a retrospective analysis will be conducted for about 500 uncomplicated TBAD
patients from the Emory Aortic Databank. Clinical and anatomic data will be harvested from the
electronic medical record and image studies to identify predictors of OMT failure. Next, using the
same patient database, a series of mechanical experiments will be performed to obtain
hyperelastic and failure properties of the TBAD tissues, from which rupture/tear risk metrics will
be developed. Fluid-structure interaction (FSI) analyses will be validated and applied to obtain
“heat maps” of hemodynamic and wall stress fields. The risk indices will be consequently
extracted. For patients with longitudinal imaging data, TBAD progression will be predicted using
an integrated growth and remodeling (G&R) and dissection propagation model. Critical
biomechanical parameters will be identified as potential predictors of OMT failure. Finally,
machine learning (ML) techniques will be used to combine clinical and biomechanical predictors
to develop a multi-factorial, personalized TBAD risk stratification model. To evaluate the
performance of the proposed approach, we will recruit and perform a longitudinal follow-up study
of 35 acute uncomplicated TBAD patients to validate our approach by comparing the ML-model-
prediction results with actual clinical outcomes.

## Key facts

- **NIH application ID:** 10897983
- **Project number:** 5R01HL155537-04
- **Recipient organization:** EMORY UNIVERSITY
- **Principal Investigator:** Bradley Graham Leshnower
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $534,965
- **Award type:** 5
- **Project period:** 2021-09-20 → 2026-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10897983

## Citation

> US National Institutes of Health, RePORTER application 10897983, Risk stratification of uncomplicated type B aortic dissection using clinical and engineering analysis (5R01HL155537-04). Retrieved via AI Analytics 2026-06-10 from https://api.ai-analytics.org/grant/nih/10897983. Licensed CC0.

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