RESUMEN
Objective: To explore the risk factors of abdominal aortic enlargement (AAE) after thoracic endovascular aortic repair using two-stent graft implantation (TEVAR-TSI) for Stanford type B aortic dissection. Methods: The clinical and imaging data of patients who underwent TEVAR-TSI for Stanford type B aortic dissection in the First Affiliated Hospital of Hebei North University from January 2013 through September 2020 were retrospectively collected and analyzed. CT angiography (CTA) scans were performed before the procedure. Follow-up CTA scans were scheduled and performed in 1, 3, 6, and 12 months after the procedure and annually thereafter. The primary outcome was AAE. The risk factors of AAE after TEVAR-TSI were selected and survival analysis and multivariate logistic regression were conducted accordingly. Results: A total of 146 patients were regularly followed up at our hospital, with the median followup time of the entire cohort being 48 months (ranging from 12 to 84 months). During the followup period after TEVAR-TSI, the incidence of AAE was 19.9% (29/146). A total of 29 patients developed AAE (the AAE group), while 117 patients did not develop AAE (the non-AAE group). There were a total of 27 deaths, including 13 in the non-AAE group versus 14 in the AAE group. Distal aortic reoperation was performed on 10 patients, including 4 in the non-AAE group versus 6 in the AAE group. The cumulative long-term survival and freedom from distal aortic reoperation of the non-AAE group were both significantly better those of the AAE group ( P<0.05). Logistic multivariate regression analysis showed that independent risk factors of AAE after TEVAR-TSI included the following, partial thrombosis of the false lumen (odds ratio [ OR]=4.090, 95% confidence interval [ CI]: 1.539-10.867, P=0.005), the longer cumulative diameter of residual intimal tear above the level of the lowest renal arteries ( OR=1.290, 95% CI: 1.164-1.429, P=0.000), and shorter cumulative diameter of residual intimal tear below the level of the lowest renal arteries ( OR=0.487, 95% CI: 0.270-0.878, P=0.017). Conclusion: The prognosis of patients who developed AAE after TEVAR-TSI was not good. During followup visits, as precautions against the development of AAE, close attention should be paid to partial thrombosis of the false lumen, cumulative diameter of residual intimal tear above the level of the lowest renal arteries, and cumulative diameter of residual intimal tear below the level of the lowest renal arteries.
Asunto(s)
Aneurisma de la Aorta Torácica , Disección Aórtica , Implantación de Prótesis Vascular , Procedimientos Endovasculares , Trombosis , Disección Aórtica/etiología , Disección Aórtica/cirugía , Aneurisma de la Aorta Torácica/cirugía , Aortografía/métodos , Prótesis Vascular , Implantación de Prótesis Vascular/efectos adversos , Procedimientos Endovasculares/efectos adversos , Análisis Factorial , Humanos , Estudios Retrospectivos , Factores de Riesgo , Stents/efectos adversos , Trombosis/etiología , Trombosis/cirugía , Resultado del TratamientoRESUMEN
OBJECTIVE: To investigate the characteristics of aortic remodeling after thoracic endovascular aortic repair using two-stent graft implantation (TEVAR-TSI) for Stanford B aortic dissection. METHODS: The clinical and imaging data of 128 patients who underwent TEVAR-TSI for Stanford B aortic dissection in the First Affiliated Hospital of Hebei North University from January 2013 through May 2019 were retrospectively collected. CT images were obtained before (T 0) TEVAR-TSI and, 1 week (T 1), 3 months (T 2), 6 months (T 3), 1 year (T 4) after TEVAR-TSI. The maximum diameter of the true lumen and false lumen in the short axis view was accessed at five levels: L 1: the level of primary tear entry, L 2: the level of the bronchial bifurcation, L 3: the level of the distal of the first stent-graft, L 4: the level of the celiac trunk, L 5: the level of the lowest renal arteries. The false lumen thrombosis in the thoracic aorta and abdominal aorta were assessed at different times, the false lumen and true lumen changes in diameter were evaluated between the preoperative and postoperative CT scan. RESULTS: The stented segment of the descending thoracic aorta was evaluated (L 1-L 3): The true lumen diameter showed an increasing trend and the false lumen diameter showed an decreasing trend at levels L 1, L 2, and L 3, the change of true lumen diameter was positively correlated with the follow-up time ( r=0.721, 0.827, 0.893, P<0.05), and the change rate of true lumen diameter was positively correlated with the follow-up time ( r=0.763, 0.818, 0.902, P<0.05), and the change of false lumen diameter was negatively correlated with the follow-up time ( r=-0.750, -0.927, -0.934, P<0.05), and the change rate of false lumen diameter was negatively correlated with the follow-up time (-0.774, -0.935, -0.952, P<0.05). When the unstented segment of the abdominal aorta was evaluated (L 4-L 5), the average true lumen diameter at the level of celiac trunk increased significantly at 1 year by 13.7% ( P=0.007), however, the average false lumen diameter did not change over time ( P=0.406). The average true lumen diameter and false lumen diameter at the level of the lowest renal arteries increased over time as well, the average true lumen increased by 10.1%, and the average false lumen increased by 13.6% ( P=0.048, 0.017). Besides, the complete false lumen thrombosis rate of the stented segment of the descending thoracic aorta was higher than that of the unstented segment of the abdominal aorta.e complete false lumen thrombosis rate of the stented segment of the descending thoracic aorta was higher than that of the unstented segment of the abdominal aorta. CONCLUSION: After receiving TEVAR-TSI, Stanford type B aortic dissection patients had high thrombosis absorption rate in the thoracic aortic segment covered by stent, and the aortic remodeling was more ideal. The aortic remodeling effect in the abdominal aortic segment not covered was not ideal, and the inner diameter of the abdominal aorta tended to increase. Therefore, close follow-up monitoring should be conducted.
Asunto(s)
Aneurisma de la Aorta Torácica , Disección Aórtica , Implantación de Prótesis Vascular , Procedimientos Endovasculares , Disección Aórtica/diagnóstico por imagen , Disección Aórtica/cirugía , Aorta Torácica/diagnóstico por imagen , Aorta Torácica/cirugía , Aneurisma de la Aorta Torácica/diagnóstico por imagen , Aneurisma de la Aorta Torácica/cirugía , Humanos , Estudios Retrospectivos , Stents , Resultado del TratamientoRESUMEN
BACKGROUND: Prediction of cardiovascular disease (CVD) is important in clinical practice. Machine learning (ML) may offer an improved alternative to current CVD risk stratification in individual patients. We aim to identify important predictors and compare ML models with traditional models according to their prediction performance in a large long-term follow-up cohort. METHODS: The Atherosclerosis Risk in Communities (ARIC) study was designed to study the progression of subclinical disease to cardiovascular events over a 25-year follow-up period. All phenotypic variables at visit 1 were obtained. All-cause death, CVD, and coronary heart disease were the outcomes for analysis. The ML framework involved variable selection using the random survival forest (RSF) method, model building, and 5-fold cross-validation. Model performance was evaluated by discrimination using the Harrell concordance index (C-index), accuracy using the Brier score (BS), and interpretability using the number of variables in the model. RESULTS: Of the 14,842 participants in ARIC, the average age was 54.2 years, with 45.2% male and 26.2% Black participants. Thirty-eight unique variables were selected in the RSF top 20 importance ranking of all 6 outcomes. Aging, hypertension, glucose metabolism, renal function, coagulation, adiposity, and sodium retention dominated the predictions of all outcomes. The ML models outperformed the regression models and established risk scores with a higher C-index, lower BS, and varied interpretability. CONCLUSIONS: The ML framework is useful for identifying important predictors of CVD and for developing models with robust performance compared with existing risk models.