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Updated: Dec 19, 2025

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Real-Time Assessment of Spinal Cord Microperfusion in a Porcine Model of Ischemia/Reperfusion
Published on: December 10, 2020
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New Preoperative Spinal Cord Ischemia Risk Stratification Model for Patients Undergoing Thoracic Endovascular Aortic
Albeir Y Mousa1, Ramez Morcos2, Mike Broce3
1Department of Surgery, Robert C. Byrd Health Sciences Center/West Virginia University, Charleston Area Medical Center, Vascular Center of Excellence, WV, USA.
Vascular and Endovascular Surgery
|June 5, 2020
Summary
Spinal cord ischemia (SCI) is a feared complication of Thoracic Endovascular Aortic Repair (TEVAR). This study identified key predictors of SCI and developed a risk score to aid clinical decisions and patient consent.
Area of Science:
- Vascular Surgery
- Cardiovascular Research
- Neurosurgery
Background:
- Spinal cord ischemia (SCI) is a significant complication following Thoracic Endovascular Aortic Repair (TEVAR).
- Accurate risk assessment is crucial for patient management and procedural planning.
Purpose of the Study:
- To identify significant predictors of SCI after TEVAR.
- To develop a simple, clinically applicable risk score model for SCI.
Main Methods:
- Retrospective review of the Society of Vascular Surgery/Vascular Quality Initiative national data set (2014-2018).
- Analysis of preoperative demographics, procedure-related variables, and SCI clinical details.
- Development of a SCI risk score using multivariable logistic regression.
Main Results:
- The study analyzed 7889 patients undergoing TEVAR.
- Predictors of increased SCI risk include age, celiac coverage, smoking, dialysis, multiple aortic devices, emergent surgery, adjunct procedures, longer device length, higher ASA class, and longer procedure time.
- High-volume centers and higher eGFR (≥60) were associated with decreased SCI risk.
Conclusions:
- SCI remains a serious complication of TEVAR, with a 3.6% incidence in this series, nearly 60% of which were permanent.
- The developed risk score model can assist in clinical decision-making, patient consent, and procedural strategy optimization.

