Physiologic phenotypes in blunt thoracic aortic injury: implications for risk stratification and surgical decision-making using machine learning
Machine learning identified three physiologic BTAI phenotypes with distinct presentations and mortality despite similar management patterns. XAI showed that perfusion and metabolic markers, not anatomy alone, drive phenotype structure. These data reinforce the potential for physiologic phenotyping to enhance prognostication and guide BTAI decision-making.
Authors
- Phillip Jenkins (ORCID: https://orcid.org/0000-0001-8661-9442)
- Victor Andujo
- Castigliano Murthy Bhamidipati (ORCID: https://orcid.org/0000-0003-2180-9178)
- Michael Kolesnikov (ORCID: https://orcid.org/0000-0002-2833-9829)
- Ruchi Thanawala (ORCID: https://orcid.org/0000-0002-1419-4737)
- Julie Doberne
- Tiffany Lian
- Shelby Willis
- Justin Regner
Institutions
- Oregon Health & Science University (US)
Publication Details
- Journal
- Frontiers in Digital Health
- Published
- 2026-02-09
- DOI
- https://doi.org/10.3389/fdgth.2026.1745987
- Primary Topic
- Aortic Disease and Treatment Approaches
- Type
- article
- Field-Weighted Citation Impact
- 0.00