Development and Validation of a Risk Prediction Tool for In-hospital Mortality After Thoracic Endovascular Repair in Patients with Blunt Thoracic Aortic Injury Using the Aortic Trauma Foundation Registry

BACKGROUND:The objective of our present effort was to use an international blunt thoracic aortic injury (BTAI) registry to create a prediction model identifying important preoperative and intraoperative factors associated with postoperative mortality, and to develop and validate a simple risk prediction tool that could assist with patient selection and risk stratification in this patient population. METHODS:For the purpose of the present study, all patients undergoing thoracic endovascular aortic repair (TEVAR) for BTAI and registered in the Aortic Trauma Foundation (ATF) database from January 2016 as of June 2022 were identified. Patients undergoing medical management or open repair were excluded. The primary outcome was binary in-hospital all-cause mortality. Two predictive models were generated: a preoperative model (i.e. only including variables before TEVAR or intention-to-treat) and a full model (i.e. also including variables after TEVAR or per-protocol). RESULTS:Out of a total of 944 cases included in the ATF registry until June 2022, 448 underwent TEVAR and were included in the study population. TEVAR for BTAI was associated with an 8.5% in-hospital all-cause mortality in the ATF dataset. These study subjects were subsequently divided using 3:1 random sampling in a derivation cohort (336; 75.0%) and a validation cohort (112; 25.0%). The median age was 38 years, and the majority of patients were male (350; 78%). A total of 38 variables were included in the final analysis. Of these, 17 variables were considered in the preoperative model, 9 variables were integrated in the full model, and 12 variables were excluded owing to either extremely low variance or strong correlation with other variables. The calibration graphs showed how both models from the ATF dataset tended to underestimate risk, mainly in intermediate-risk cases. The discriminative capacity was moderate in all models; the best performing model was the full model from the ATF dataset, as evident from both the Receiver Operating Characteristic curve (Area Under the Curve 0.84; 95% CI 0.74-0.91) and from the density graph. CONCLUSIONS:In this study, we developed and validated a contemporary risk prediction model, which incorporates several preoperative and postoperative variables and is strongly predictive of early mortality. While this model can reasonably predict in-hospital all-cause mortality, thereby assisting physicians with risk-stratification as well as inform patients and their caregivers, its intrinsic limitations must be taken into account and it should only be considered an adjunctive tool that may complement clinical judgment and shared decision-making.