Distinguishing acute from chronic aortic dissections using CT imaging features

Orabi, Norman A.; Quint, Leslie E.; Watcharotone, Kuanwong; Nan, Bin; Williams, David M.; Kim, Karen M.

doi:10.1007/s10554-018-1398-x

“…CTA findings [ 13 , 14 ] of aortic dissection include intimal and calcified internal shift, division of the aorta into true and false lumens by an intimal flap and formation of a double-lumen aorta. The presence of pleural and pericardial effusions can clarify the involvement of aortic branches such as the brachiocephalic trunk of the aortic arch and right internal carotid, subclavian, visceral superior mesenteric, renal, and iliac arteries.…”

Section: Discussionmentioning

confidence: 99%

Early Diagnosis and Treatment of Nine Patients with Severe Multiple Injuries Accompanied by Traumatic Aortic Dissection during Emergency Treatment

Zhang

¹

,

Xu

²

,

Zhang

³

et al. 2022

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Objective. This study is aimed at investigating the early diagnosis and efficacy of emergency treatments of nine patients with severe multiple injuries accompanied by traumatic aortic dissection (TAD). Methods. Patients who sustained severe multiple injuries accompanied by TAD following a car accident ( n = 6 ) and falls from a height ( n = 3 ) were treated in the emergency department of our hospital from October 2017 to July 2021. Data of these patients, including seven men and two women (average age, 53 ± 15.2 years; range, 18–83 years) were analysed retrospectively. Upon hospital arrival, the multidisciplinary treatment (MDT) trauma team, composed of doctors and nurses, immediately performed resuscitation following the Green Channel Consultation and Treatment Process for Severe Multiple Injuries. Life-threatening injuries were managed urgently. Blood tests and blood preparation and bedside B-scan ultrasonography and CT were performed. Aortic computed tomography angiography (CTA) was conducted decisively in patients suspected of TAD so that endovascular graft exclusion (EVGE) with the aortic covered stent can be performed promptly, followed by emergency management, second-stage surgery, and intensive care according to the injury control strategy. Results. This study included nine patients suffering from severe multiple injuries accompanied by Stanford type B TAD, with injury severity scores ranging from 35 to 43 points. Six patients underwent EVGE while receiving emergency treatment, whereas two patients who also had intracranial haemorrhage underwent selective EVGE. One case of TAD missed in the emergency department was detected 13 days after hospitalisation; therefore, the patient promptly underwent EVGE. Emergency procedures performed included exploratory laparotomy and splenectomy ( n = 2 ), thoracic closed drainage ( n = 5 ), haemothoracotomy ( n = 3 ), second-stage fracture surgery ( n = 4 ), and tracheotomy ( n = 1 ). Postinjury complications included haemorrhagic shock, coagulation disorders, hyoxaemia, pulmonary infection, renal insufficiency, and hypoproteinaemia; however, all patients recovered after intensive care treatment. Aortic CTA after EVGE revealed the disappearance of the dissection and the resorption of the intermural haematoma. However, varying degrees of stenosis or occlusion were observed in the left subclavian artery. Nine patients with severe multiple injuries were treated satisfactorily by the MDT, without fatalities, and all patients were discharged for rehabilitation. Conclusion. In this study, procedures including resuscitation, urgent aortic CTA for definitive diagnosis, prompt EVGE, emergency injury control surgery, second-stage definitive surgery, intensive care treatment, and rehabilitation were rationally performed by the emergency MDT trauma team. Overall, this continuous and seamless process is a key factor for the successful treatment of patients with severe multiple injuries accompanied by TAD.

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“…Computed tomography angiography is the best imaging modality to diagnose AD ( 2 , 3 , 18 , 19 ), while CTA is commonly restricted in some emergency departments, especially in rural or underserved areas that lack technical and staff support ( 4 ). Making the best use of non-contrast CT scans to assist the early warning of AD in clinical practice is of great significance and has the potential to greatly improve patient outcomes.…”

Section: Discussionmentioning

confidence: 99%

Advanced Warning of Aortic Dissection on Non-Contrast CT: The Combination of Deep Learning and Morphological Characteristics

Yi

¹

,

Mao²,

Wang³

et al. 2022

Front. Cardiovasc. Med.

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Background: The identification of aortic dissection (AD) at baseline plays a crucial role in clinical practice. Non-contrast CT scans are widely available, convenient, and easy to perform. However, the detection of AD on non-contrast CT scans by radiologists currently lacks sensitivity and is suboptimal.Methods: A total of 452 patients who underwent aortic CT angiography (CTA) were enrolled retrospectively from two medical centers in China to form the internal cohort (341 patients, 139 patients with AD, 202 patients with non-AD) and the external testing cohort (111 patients, 46 patients with AD, 65 patients with non-AD). The internal cohort was divided into the training cohort (n = 238), validation cohort (n = 35), and internal testing cohort (n = 68). Morphological characteristics were extracted from the aortic segmentation. A deep-integrated model based on the Gaussian Naive Bayes algorithm was built to differentiate AD from non-AD, using the combination of the three-dimensional (3D) deep-learning model score and morphological characteristics. The areas under the receiver operating characteristic curve (AUCs), accuracy, sensitivity, and specificity were used to evaluate the model performance. The proposed model was also compared with the subjective assessment of radiologists.Results: After the combination of all the morphological characteristics, our proposed deep-integrated model significantly outperformed the 3D deep-learning model (AUC: 0.948 vs. 0.803 in the internal testing cohort and 0.969 vs. 0.814 in the external testing cohort, both p < 0.05). The accuracy, sensitivity, and specificity of our model reached 0.897, 0.862, and 0.923 in the internal testing cohort and 0.730, 0.978, and 0.554 in the external testing cohort, respectively. The accuracy for AD detection showed no significant difference between our model and the radiologists (p > 0.05).Conclusion: The proposed model presented good performance for AD detection on non-contrast CT scans; thus, early diagnosis and prompt treatment would be available.

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“… 4 Due to the atypical clinical presentation of our patient, radiological assessment was particularly useful as we did anticipate an element of chronicity. 5 …”

Section: Discussionmentioning

confidence: 99%