We queried the Vascular Quality Initiative TEVAR dataset from April 2011 to November 2017 to identify patients with BTAI as the indication for repair. Patient characteristics, injury grade, timing of repair, and technical aspects including left subclavian artery (LSCA) involvement and coverage were evaluated. Logistic regression was used to identify univariable predictors of the primary outcome of in-hospital mortality. A multivariable model was constructed to predict in-hospital mortality after TEVAR for traumatic aortic injury. The model was tested as a prediction tool, internally validated using 10-fold cross-validation approach, externally validated using early and late split samples, and finally simplified into a scoring system.
We identified 633 TEVAR cases performed for blunt trauma. The majority of patients were male (73.9%) with median age of 39 years (interquartile range, 27-56 years). Although 18.6% documented zone 2 or proximal involvement, 28.1% documented involvement or treatment of the LSCA. 8.9% of repairs were performed for a grade 1 injury, with an increase from 6.4% in 2014 to 16.7% in 2017 (P = .04). The overall in-hospital mortality rate was 7.3%. Independent predictors of mortality were age 60 year or greater (odds ratio [OR], 11.33; 95% confidence interval [CI], 5.30-24.23; P < .001), creatinine 1.2 or greater (OR, 5.28; 95% CI, 2.46-11.34; P < .001), male gender (OR, 4.26; 95% CI, 1.53-11.84; P = .005), Injury Severity Score of greater than 30 (OR, 3.86; 95% CI, 1.74-8.57; P = .001), and LSCA involvement (OR, 2.25; 95% CI, 1.11-4.53; P = .02). The model predicted in-hospital mortality with a C-statistic of 0.86 (95% CI, 0.80-0.92), and a simplified model based on a point system had a similar C-statistic of 0.86 (95% CI, 0.80-0.92; P = .44).
TEVAR for BTAI is associated with a 7.3% in-hospital mortality in the Vascular Quality Initiative. Treatment of grade 1 injuries has increased significantly in recent years. Factors most strongly associated with mortality include age, male gender, renal impairment, LSCA involvement, and high ISS score. A simple point score model based on these variables robustly predicts in-hospital mortality and may assist in appropriate patient selection and risk stratification.