An extensive chronic aortic dissection presenting with acute embolic stroke

Neurological Manifestations of Aortic Dissection: A Scoping Review

Aortic dissection (AD) is the most prevalent aortic pathology, with an incidence of 2.6-3.5 cases per 100,000 inhabitants per year with mortality rates as high as 90% at 3 months without proper management. Despite the presence of typical symptoms, it has been reported that up to 38% of cases go unnoticed in the initial evaluation, either due to additional symptomatology or the absence of classic symptoms. In 28% of cases the diagnosis is made post-mortem, highlighting the severity and importance of timely diagnosis. The main goals of this study were to identify the primary neurological manifestations of acute AD and the frequency of these manifestations in type A and B AD according to the Stanford classification. A total of 2,734 records were retrieved from two databases, of which 2,611 were excluded. Therefore, 123 articles were obtained and 88 were evaluated for eligibility. Consequently, 79 articles were included in the review. The abstracts identified in the search were screened by seven blinded independent authors and excluded those who lacked relevance. The authors read the full texts independently to determine inclusion. A third reviewer adjudicated discrepancies when opinions were at odds. There were 169 patients diagnosed with Stanford type A, of which 43.8% displayed quantitative impairment of consciousness. Within this group, transient loss of consciousness was observed in 34.3% of cases. Motor syndrome was present in 54.5%, with 23.7% of patients presenting with hemiparesis or hemiplegia. 3.6% had facial paresis or paralysis and less frequently monoparesis, paraparesis, hyperreflexia, spasticity, and muscle spasms. 7.6% presented with language impairments including Broca's aphasia and dysarthria. Seizures were present in 3.6%. Neuro-ophthalmological symptoms were 2.9%, cortical or cognitive symptoms and headache were 2.3%, cerebellar syndrome was 1.8%, and sensory syndrome was 1.2%. Of 86 patients diagnosed with Stanford type B, 57.0% displayed motor syndrome, with 50.0% being paraparesis or paraplegia, 4.7% monoplegia, and 2.3% hemiplegia or hemiparesis. Additionally, impairment of consciousness was present in 26.7%, sensory syndrome in 12.8%, and language impairments in 3.5%. In the emergency department, understanding the neurological manifestations in both Stanford type A and B dissections emerges as a pivotal cornerstone for conducting precise assessments and efficient patient management. Given the wide spectrum of manifestations, we emphasize the importance of suspecting AD when neurological disorders are associated with the classic symptoms. In these cases, we encourage a complete neurological examination to be performed and a multidisciplinary group assembled to tackle this entity.

Ischemic Stroke as a Form of Presentation of Aortic Dissection: A Case Report

Aortic dissection is a rare condition with a high mortality rate, the clinical manifestations of which depend on the involvement of aortic branches. We describe the case of a male patient with neurological deficits accompanied by dorsal-lumbar pain and exuberant sympathetic hyperactivity, diagnosed with ascending and descending aorta dissection, initially managed in the prehospital setting. This case reinforces the importance of maintaining high levels of suspicion for rare causes of cerebrovascular accidents.

The Construction of a Risk Prediction Model Based on Neural Network for Pre-operative Acute Ischemic Stroke in Acute Type A Aortic Dissection Patients

Objective: To establish a pre-operative acute ischemic stroke risk (AIS) prediction model using the deep neural network in patients with acute type A aortic dissection (ATAAD). Methods: Between January 2015 and February 2019, 300 ATAAD patients diagnosed by aorta CTA were analyzed retrospectively. Patients were divided into two groups according to the presence or absence of pre-operative AIS. Pre-operative AIS risk prediction models based on different machine learning algorithm was established with clinical, transthoracic echocardiography (TTE) and CTA imaging characteristics as input. The performance of the difference models was evaluated using the receiver operating characteristic (ROC), precision-recall curve (PRC) and decision curve analysis (DCA). Results: Pre-operative AIS was detected in 86 of 300 patients with ATAAD (28.7%). The cohort was split into a training (211, 70% patients) and validation cohort (89, 30% patients) according to stratified sampling strategy. The constructed deep neural network model had the best performance on the discrimination of AIS group compare with other machine learning model, with an accuracy of 0.934 (95% CI: 0.891-0.963), 0.921 (95% CI: 0.845-0.968), sensitivity of 0.934, 0.960, specificity of 0.933, 0.906, and AUC of 0.982 (95% CI: 0.967-0.997), 0.964 (95% CI: 0.932-0.997) in the training and validation cohort, respectively. Conclusion: The established risk prediction model based on the deep neural network method may have the big potential to evaluate the risk of pre-operative AIS in patients with ATAAD.