Coarctation of the Aorta

Crafoord, C; Ejrup, B; Gladnikoff, H.

doi:10.1136/thx.2.3.121

Cited by 30 publications

(2 citation statements)

References 23 publications

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“…There are also paradoxical results, for example, the maximum diameter of the terminal abdominal aorta was positively correlated with the diameter of the descending aorta, while the cross-sectional area of the terminal abdominal aorta showed a negative effect in all three predicted positions. We suspected that the maximal diameter of the terminal abdominal aorta represents an anatomical continuation, while the effect of the abdominal aortic terminal cross-sectional area is the result of hemodynamics, as severe abdominal aortic constriction can fully induce pressure overload and subsequent heart failure ( 23 – 25 ). Our work can be compared with previous study by Takashi Yamauchi and colleagues, who also investigated in the estimation of descending thoracic aortic diameter ( 26 ).…”

Section: Discussionmentioning

confidence: 99%

A machine learning approach for predicting descending thoracic aortic diameter

Jin

Wang

et al. 2023

Front. Cardiovasc. Med.

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BackgroundTo establish models for predicting descending thoracic aortic diameters and provide evidence for selecting the size of the stent graft for TBAD patients.MethodsA total of 200 candidates without severe deformation of aorta were included. CTA information was collected and 3D reconstructed. In the reconstructed CTA, a total of 12 cross-sections of peripheral vessels were made perpendicular to the axis of flow of the aorta. Parameters of the cross sections and basic clinical characteristics were used for prediction. The data was randomly split into the training set and the test set in an 8:2 ratio. To fully describe diameters of descending thoracic aorta, three predicted points were set based quadrisection, and a total of 12 models at three predicted points were established using four algorithms included linear regression (LR), support vector machine (SVM), Extra-Tree regression (ETR) and random forest regression (RFR). The performance of models was evaluated by mean square error (MSE) of the prediction value, and the ranking of feature importance was given by Shapley value. After modeling, prognosis of five TEVAR cases and stent oversizing were compared.ResultsWe identified a series of parameters which affect the diameter of descending thoracic aorta, including age, hypertension, the area of proximal edge of superior mesenteric artery, etc. Among four predictive models, all the MSEs of SVM models at three different predicted position were less than 2 mm2, with approximately 90% predicted diameters error less than 2 mm in the test sets. In patients with dSINE, stent oversizing was about 3 mm, while only 1 mm in patients without complications.ConclusionThe predictive models established by machine learning revealed the relationship between basic characteristics and diameters of different segment of descending aorta, which help to provide evidence for selecting the matching distal size of the stent for TBAD patients, thereby reducing the incidence of TEVAR complications.

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Section: Discussionmentioning

confidence: 99%

A machine learning approach for predicting descending thoracic aortic diameter

Jin

Wang

et al. 2023

Front. Cardiovasc. Med.

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“…Various surgical techniques have been developed for the treatment of aortic arch hypoplasia in patients with aortic coarctation and combined cardiac lesions over the K Autologous aortic arch reconstruction in isolated and combined cardiac lesions original article years. These include resection and end-to-end anastomosis [1,2], subclavian flap technique [3], as well as various forms of patch augmentation [4,5]. The latter are limited because they are predisposed to inadequate relief of obstruction and to aneurysm formation.…”

Section: Introductionmentioning

confidence: 99%

Autologous aortic arch reconstruction in isolated and combined cardiac lesions

et al. 2019

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Objectives Various surgical strategies have been reported for the treatment of aortic coarctation with hypoplastic aortic arch, including simple resection and end-to-end anastomosis as well as various forms of patch augmentation. These techniques are limited by inadequate relief of arch obstruction and use of patch material predisposed to recurrent obstruction or aneurysm formation. We report our experience with autologous aortic arch reconstruction in isolated and combined lesions, a technique that relieves even complex forms of arch reconstruction without patch material. Methods We retrospectively analyzed our institutional experience with autologous aortic arch reconstruction in isolated and combined cardiac lesions from

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Prolonged Experimental Occlusion of Thoracic Aorta During Hypothermia

Owens¹

1955

Arch Surg

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Coarctation of the Aorta

Cited by 30 publications

References 23 publications

A machine learning approach for predicting descending thoracic aortic diameter

A machine learning approach for predicting descending thoracic aortic diameter

Autologous aortic arch reconstruction in isolated and combined cardiac lesions

Prolonged Experimental Occlusion of Thoracic Aorta During Hypothermia

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