Background and aimsPrevious studies reported a high prevalence of concomitant coronary artery disease (CAD) in patients with Type B aortic dissection (TBAD). However, there is too limited data on the impact of CAD on prognosis in patients with TBAD. The present study aimed to assess the short-term and long-term impact of CAD on patients with acute or subacute TBAD undergoing thoracic endovascular aortic repair (TEVAR).MethodsWe retrospectively evaluated 463 patients with acute or subacute TBAD undergoing TEVAR from a prospectively maintained database from 2010 to 2017. CAD was defined before TEVAR by coronary angiography. Multivariable logistic and cox regression analyses were performed to evaluate the relationship between CAD and the short-term as well as long-term outcomes.ResultsAccording to the results of coronary angiography, the 463 patients were divided into the following two groups: CAD group (N = 148), non-CAD group (N = 315). In total, 12 (2.6%) in-hospital deaths and 54 (12%) all-cause deaths following a median follow-up of 48.1 months were recorded. Multivariable analysis revealed that CAD was an independent predictor of in-hospital major adverse clinical events (MACE) (odd ratio [OR], 2.33; 95% confidence interval [CI], 1.07–5.08; p = 0.033), long-term mortality [hazard ratio (HR), 2.11, 95% CI, 1.19–3.74, P = 0.011] and long-term MACE (HR, 1.95, 95% CI, 1.26–3.02, P = 0.003). To further clarify the relationship between the severity of CAD and long-term outcomes, we categorized patients into three groups: zero-vessel disease, single-vessel disease and multi-vessel disease. The long-term mortality (9.7 vs. 14.4 vs. 21.2%, P = 0.045), and long-term MACE (16.8 vs. 22.2 vs. 40.4%, P = 0.001) increased with the number of identified stenosed coronary vessels. Multivariable analysis indicated that, multi-vessel disease was independently associated with long-term mortality (HR, 2.38, 95% CI, 1.16–4.89, P = 0.018) and long-term MACE (HR, 2.79, 95% CI, 1.65–4.73, P = 0.001), compared with zero-vessel disease.ConclusionsCAD was associated with short-term and long-term worse outcomes in patients with acute or subacute TBAD undergoing TEVAR. Furthermore, the severity of CAD was also associated with worse long-term prognosis. Therefore, CAD could be considered as a useful independent predictor for pre-TEVAR risk stratification in patients with TBAD.
OBJECTIVES To investigate the impact of machine-learning derived baseline lean psoas muscle area (LPMA) for patients undergoing thoracic endovascular aortic repair. METHODS A retrospective study was undertaken of acute and subacute complicated type B aortic dissection patients who underwent endovascular treatment from 2010 to 2017. LPMA (a marker of frailty) was calculated by multiplying psoas muscle area and density measured at L3 level from the computed tomography. The optimal cut-off value of LPMA was determined by the Cox hazard model with restricted cubic spline. RESULTS A total of 428 patients who met the inclusion criteria were included in this study. Patients were classified into low LPMA group (n = 218) and high LPMA group (n = 210) using the cut-off value of 395 cm2·HU. An automatic muscle segmentation algorithm was developed based on U-Net architecture. There was high correlation between machine-learning method and manual measurement for psoas muscle area (r = 0.91, P < 0.001) and density (r = 0.90, P < 0.001). Multivariable regression analyses revealed that baseline low LPMA (<395 cm2·HU) was an independent positive predictor for 30-day (Odds ratio 5.62, 95% confidence interval [CI] 1.20–26.23, P = 0.028) and follow-up (Hazard ratio [HR] 5.62, 95% CI 2.68–11.79, P < 0.001) mortality. Propensity-score matching and subgroup analysis based on age (<65 vs ≥ 65 years) confirmed the independent association between baseline LPMA and follow-up mortality. CONCLUSIONS Baseline LPMA could profoundly affect the prognosis of patients undergoing thoracic endovascular aortic repair. It was feasible to integrate the automatic muscle measurements into clinical routine.
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