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PURPOSE: To evaluate the relationship between the timing of thoracic endovascular aortic repair (TEVAR) for blunt thoracic aortic injury (BTAI) and prognosis. METHODS: This is a single-center retrospective cohort study. Patients who received TEVAR for BTAI at our institution from October 2016 to September 2023 were divided into 2 categories depending on the injury severity score (ISS) (≤ 25 vs. > 25) and when the TEVAR was performed for BTAI (within 24 h vs. after 24 h), respectively. The analysis included all patients who received TEVAR treatment after being diagnosed with BTAI through whole-body CT angiography. Patients treated with open repair and non-operative management were excluded. After propensity-score matching for various factors, outcomes during hospitalization and follow-up were compared. These factors included demographics, comorbidities, concomitant injuries, cause and location of aortic injury, Glasgow coma scale score, society for vascular surgery grading, hemoglobin concentration, creatinine concentration, shock, systolic blood pressure, and heart rate at admission. The comparison was conducted using SPSS 26 software. Continuous variables were presented as either the mean ± standard deviation or median (Q1, Q3), and were compared using either the t-test or the Mann-Whitney U test. Categorical variables were expressed as n (%), and comparisons were made between the 2 groups using the χ2 test or Fisher's exact test. Statistical significance was defined as a 2-sided p < 0.05. RESULTS: In total, 110 patients were involved in the study, with 65 (59.1%) patients having ISS scores > 25 and 32 (29.1%) receiving immediate TEVAR. The perioperative overall mortality rate in the group with ISS > 25 was significantly higher than that in the group with ISS ≤ 25 (11 (16.9%) vs. 2 (4.4%), p < 0.001). Upon admission, the elective group exhibited a notably higher Glasgow coma scale score (median (Q1, Q3)) compared to the immediate group (15 (12, 15) vs. 13.5 (9, 15), p = 0.039), while the creatinine concentration (median (Q1, Q3)) at admission was significantly higher in the immediate group (90.5 (63.8, 144.0) vs. 71.5 (58.3, 80.8), p = 0.012). The final sample included 52 matched patients. Complications occurred significantly less frequently in the elective group compared to the immediate group (16 (50.0%) vs. 3 (10.0%), p < 0.001). Single-factor analysis of variance showed that complications in hospitalized patients were significantly associated with immediate TEVAR as the sole independent risk factor (odds ratio: 9.000, 95% confidence interval: 2.266 - 35.752, p = 0.002). CONCLUSION: In this propensity-score matched analysis of patients undergoing TEVAR for BTAI, elective TEVAR was significantly associated with a lower risk of complication rates. In this study using propensity-score matching, patients who underwent elective TEVAR for BTAI had lower complication rates than immediate TEVAR.
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Background: Identification of vulnerable carotid plaque is important for the treatment and prevention of stroke. In previous studies, plaque vulnerability was assessed qualitatively. We aimed to develop a 3D carotid plaque radiomics model based on high-resolution magnetic resonance imaging (HRMRI) to quantitatively identify vulnerable plaques. Methods: Ninety patients with carotid atherosclerosis who underwent HRMRI were randomized into training and test cohorts. Using the radiological characteristics of carotid plaques, a traditional model was constructed. A 3D carotid plaque radiomics model was constructed using the radiomics features of 3D T1-SPACE and its contrast-enhanced sequences. A combined model was constructed using radiological and radiomics characteristics. Nomogram was generated based on the combined models, and ROC curves were utilized to assess the performance of each model. Results: 48 patients (53.33%) were symptomatic and 42 (46.67%) were asymptomatic. The traditional model was constructed using intraplaque hemorrhage, plaque enhancement, wall remodeling pattern, and lumen stenosis, and it provided an area under the curve (AUC) of 0.816 vs. 0.778 in the training and testing sets. In the two cohorts, the 3D carotid plaque radiomics model and the combined model had an AUC of 0.915 vs. 0.835 and 0.957 vs. 0.864, respectively. In the training set, both the radiomics model and the combination model outperformed the traditional model, but there was no significant difference between the radiomics model and the combined model. Conclusions: HRMRI-based 3D carotid radiomics models can improve the precision of detecting vulnerable carotid plaques, consequently improving risk classification and clinical decision-making in patients with carotid stenosis.