Two Intriguing Cases of Stanford Type A Acute Aortic Dissection

Ahmed, Taha; Nair, Raunak; Lak, Hassan Mehmood; Lodhi, Samra Haroon; Maroo, Anjli

doi:10.7759/cureus.6986

Cited by 4 publications

(8 citation statements)

References 16 publications

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“…This shows that this model is a good diagnostic model of AD. Clinically, the misdiagnosis rate of AD is close to 40% [3][4] [5]. The model performance data show that this algorithm can not only reduce the workload of doctors, but also reduce the misdiagnosis rate of AD to save patients' lives effectively.…”

Section: Discussionmentioning

confidence: 99%

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Cluster-Based Ensemble Learning Model for Rapid Detection of Aortic Dissection

Gao

Wang

Zhang

et al. 2021

Preprint

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Background: Aortic dissection (AD) is a rare and high-risk cardiovascular disease with dangerous morbidity and high mortality, so it needs rapid diagnosis and timely treatment. However, due to its complex and changeable clinical manifestations and the lack of special symptoms and signs, it is easy to cause missed diagnosis and misdiagnosis.Methods: The data set used in this paper comes from 53213 patients, which collected from XiangYa Hospital in Hunan Province from 2008 to 2016. The data includes 802 patients with aortic dissection and 52411 patients with non-aortic dissection. In order to help clinicians predict AD, we designed an ensemble learning model based on clustering: Cluster Random Under-sampling Smote-Tomek-link Bagging (CRST-Bagging). This model combines the advantages of clustering-based compound resampling (CRST) method and Bagging ensemble classifier. It achieves good results on aortic dissection data sets.Results: The model validates the effectiveness of the CRST sampling method on the AD data set. We compared the CRST-Bagging model with the classical ensemble models RUSBoost and SMOTE-Bagging on the AD data set. The experimental results show that the CRST-Bagging model has the best performance in the detection of AD. Model’s accuracy and recall rate are 83.6% and 80.7% respectively. And the F1 value is 82.1%, which is 4.8% and 1.6% higher than that of RUSBoost and SMOTE-Bagging model.Conclusions: The model proposed in this paper can be used as an auxiliary diagnostic model of AD to provide reference for clinical medicine. The model can also help doctors to judge whether patients need further imaging examination. Thus help to reduce the rate of clinical misdiagnosis and missed diagnosis of AD.

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Section: Discussionmentioning

confidence: 99%

“…It is easy to cause missed diagnosis and misdiagnosis [2]. According to the clinical statistics, the misdiagnosis rate of AD is more than 1/3 in the actual cases of AD [3][4] [5]. Mortality can reach as high as 50% within a week of onset and between 60 and 70% within a month.…”

Section: Background and Introductionmentioning

confidence: 99%

Cluster-Based Ensemble Learning Model for Rapid Detection of Aortic Dissection

Gao

Wang

Zhang

et al. 2021

Preprint

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“…When assessing a patient with suspected aortic dissection, the quality, radiation and intensity of pain should be considered. The most common presenting complaint is sudden-onset tearing chest pain radiating to the back, associated with a drop in blood pressure or pulse deficit in the upper limbs [1] . These symptoms are present in fewer than 50% of confirmed cases of acute aortic dissection (AAD) [2] .…”

Section: Case Descriptionmentioning

confidence: 99%

“…Stanford type A involves the ascending aorta and accounts for 60% of aortic dissections, while type B occurs distal to the left subclavian artery. Type A aortic dissection is misdiagnosed in 40% of cases due to overlap with acute coronary symptoms [1] . Type A presents with a higher mortality rate and therefore requires prompt surgical treatment and management [1] .…”

Section: Introductionmentioning

confidence: 99%

“…Type A aortic dissection is misdiagnosed in 40% of cases due to overlap with acute coronary symptoms [1] . Type A presents with a higher mortality rate and therefore requires prompt surgical treatment and management [1] . We present a case of type A aortic dissection in a middle-aged man without typical presenting symptoms in order to highlight the importance of using the Bentall procedure and to encourage a high index of suspicion in patients with atypical symptoms.…”

Section: Introductionmentioning

confidence: 99%

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Painless Presentation of a Deadly Disease: Type A Aortic Dissection Requiring the Bentall Procedure

Noori

Shah

Fichadiya

et al. 2022

European Journal of Case Reports in Internal Medicine

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Aortic dissection is a relatively uncommon, although catastrophic, disease which requires early and accurate diagnosis and treatment for patient survival. Aortic dissection can be difficult to diagnose due to the diverse symptom presentation, which can lead to later diagnosis, resulting in a higher mortality rate. Here we present a case of type A aortic dissection with a varied symptom presentation, highlighting the importance of early detection and the Bentall procedure for management of such cases. A 50-year-old man with no known medical history presented with bilateral lower extremity swelling and fatigue for 2 weeks. The patient denied any chest pain or dyspnoea. Vital signs showed blood pressure of 160/76 mmHg, pulse of 103 bpm, respiratory rate of 18, and temperature of 36.7°C. Laboratory findings indicated a BNP of 1901 pg/ml and troponin of 0.5 ng/ml. An initial diagnosis of decompensated heart failure was made, and IV Lasix was started. Subsequently, an echocardiogram indicated an EF of 50–55% and ascending dissection of the aorta. A CT angiogram of the chest and abdomen confirmed this diagnosis. This patient presented with unusual symptoms of aortic dissection without the typical presentation of chest pain. It is important to consider aortic dissection in a cardiac-related case as prompt imaging can help confirm the diagnosis. We explore the risks and benefits of the Bentall procedure for the management and early detection of aortic dissection.

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