Inter-ethnic differences in valve morphology, valvular dysfunction, and aortopathy between Asian and European patients with bicuspid aortic valve

Wkf, Kong; Regeer, MV; Poh, Kian Keong; Jw, Yip; Rosendael, Philippe J. van; Tc, Yeo; E, Tay; Kamperidis, Vasileios; Et, van der Velde; Mertens, Bart; Marsan, Nina Ajmone; Delgado, Victoria; Bax, JJ

doi:10.1093/eurheartj/ehx562

Cited by 50 publications

(46 citation statements)

References 29 publications

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“…The presence of a raphe appears to have some relevance since valve dysfunction and aortic dilation are more likely when a raphe is present. 31 There is a trend towards more type 1 valve morphology in patients from Asia, 32 Turner syndrome, 33 DiGeorge and Marfan syndrome, and Shone's complex, 16 whereas type 2 is predominate in trisomy 21 16 ( Fig 2).…”

Section: Valve Morphologymentioning

confidence: 99%

Clinical and pathophysiological aspects of bicuspid aortic valve disease

et al. 2018

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A bicuspid aortic valve is not only a common congenital heart defect but also an enigmatic condition that can cause a large spectrum of diseases, such as aortic valve stenosis and severe heart failure in newborns whereas aortic dissection in adults. On the contrary, a bicuspid aortic valve can also occur with normal function throughout life and never need treatment. Numerous genetic mechanisms are involved in the abnormal cellular functions that may cause abnormal development of the aortic valve during early foetal life. As several chromosomal disorders are also associated with a bicuspid valve, there does not appear to be an apparent common trigger to the abnormal development of the aortic valve. The clinical care of the bicuspid aortic valve patient has been changed by a significant body of evidence that has improved the understanding of the natural history of the disease, including when to best intervene with valve replacement and when to provide prophylactic aortic root surgery. Moreover, as bicuspid valve disease is also part of various syndromes, we can identify high-risk patients in whom a bicuspid valve is much more unfavourable than in the normal population. This review provides an overview of all aspects of the bicuspid aortic valve condition and gives an updated perspective on issues from pathophysiology to clinical care of bicuspid aortic valve disease and associated aortic disease in asymptomatic, symptomatic, and pregnant patients, as well as our viewpoint on population screening.

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Section: Valve Morphologymentioning

confidence: 99%

Clinical and pathophysiological aspects of bicuspid aortic valve disease

et al. 2018

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“…The right and left coronary leaflet (RL) were most common accounted for about 80%, the right and non-coronary leaflet (RN) was about 17% and left and non-coronary leaflet (LN) was 2%. Compared to Asians, type 0 BAV was more frequently among Europeans, whereas the incidence of RN-BAV with a raphe was higher in Asian (Kong et al, 2018). Aortic stiffness was measured by pulse wave velocity (PWV) using velocity-encoded magnetic resonance imaging (VENC-MRI) and patients with R-NC fusion were manifested as greater PWV than patients with R-L fusion phenotype.…”

Section: Morphology: Different Classification Endow Different Effectsmentioning

confidence: 90%

Bicuspid Aortic Valve: An Update in Morphology, Genetics, Biomarker, Complications, Imaging Diagnosis and Treatment

Liu

Xie

et al. 2019

Front. Physiol.

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The bicuspid aortic valve, a kind of heart disease that comes from parents, has been paid attention around the world. Although most bicuspid aortic valve (BAV) patients will suffer from some complications including aortic stenosis, aortic regurgitation, endocarditis, and heart dysfunction in the late stage of the disease, there is none symptom in the childhood, which restrains us to diagnose and treatment in the onset phase of BAV. Hemodynamic abnormalities induced by the malformations of the valves in BAV patients for a long time will cause BAV-associated aortopathy: including progress aortic dilation, aneurysm, dissection and rupture, cardiac cyst and even sudden death. At present, preventive surgical intervention is the only effective method used in this situation and the diameter of the aorta is the primary reference criterion for surgery. And the treatment effects are always not satisfactory for patients and clinicians. Therefore, we need more methods to evaluate the progression of BAV and the surgery value and the appropriate intervention time by combining basic research with clinical treatment. In this review, advances in morphology, genetic, biomarkers, diagnosis and treatments are summarized, which expects to provide an update about BAV. It is our supreme expectations to provide some evidences for BAV early screening and diagnosis, and in our opinion, personalized surgical strategy is the trend of future BAV treatment.

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“…Despite the high performance of AVC burden for diagnosing severe AS reported in previous studies, in our study population, the AVC burden exhibited a notably lower diagnostic performance. We assume that this may be due to inter-study differences in patient characteristics, such as race and valve morphology type (27,28). Compared with previous studies conducted in Western countries, our study population comprised East Asians, who have smaller body sizes and smaller AV cusps.…”

Section: Discussionmentioning

confidence: 99%

Performance of Prediction Models for Diagnosing Severe Aortic Stenosis Based on Aortic Valve Calcium on Cardiac Computed Tomography: Incorporation of Radiomics and Machine Learning

et al. 2021

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Objective We aimed to develop a prediction model for diagnosing severe aortic stenosis (AS) using computed tomography (CT) radiomics features of aortic valve calcium (AVC) and machine learning (ML) algorithms. Materials and Methods We retrospectively enrolled 408 patients who underwent cardiac CT between March 2010 and August 2017 and had echocardiographic examinations (240 patients with severe AS on echocardiography [the severe AS group] and 168 patients without severe AS [the non-severe AS group]). Data were divided into a training set (312 patients) and a validation set (96 patients). Using non-contrast-enhanced cardiac CT scans, AVC was segmented, and 128 radiomics features for AVC were extracted. After feature selection was performed with three ML algorithms (least absolute shrinkage and selection operator [LASSO], random forests [RFs], and eXtreme Gradient Boosting [XGBoost]), model classifiers for diagnosing severe AS on echocardiography were developed in combination with three different model classifier methods (logistic regression, RF, and XGBoost). The performance (c-index) of each radiomics prediction model was compared with predictions based on AVC volume and score. Results The radiomics scores derived from LASSO were significantly different between the severe AS and non-severe AS groups in the validation set (median, 1.563 vs. 0.197, respectively, p < 0.001). A radiomics prediction model based on feature selection by LASSO + model classifier by XGBoost showed the highest c-index of 0.921 (95% confidence interval [CI], 0.869–0.973) in the validation set. Compared to prediction models based on AVC volume and score (c-indexes of 0.894 [95% CI, 0.815–0.948] and 0.899 [95% CI, 0.820–0.951], respectively), eight and three of the nine radiomics prediction models showed higher discrimination abilities for severe AS. However, the differences were not statistically significant ( p > 0.05 for all). Conclusion Models based on the radiomics features of AVC and ML algorithms may perform well for diagnosing severe AS, but the added value compared to AVC volume and score should be investigated further.

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Inter-ethnic differences in valve morphology, valvular dysfunction, and aortopathy between Asian and European patients with bicuspid aortic valve

Cited by 50 publications

References 29 publications

Clinical and pathophysiological aspects of bicuspid aortic valve disease

Clinical and pathophysiological aspects of bicuspid aortic valve disease

Bicuspid Aortic Valve: An Update in Morphology, Genetics, Biomarker, Complications, Imaging Diagnosis and Treatment

Performance of Prediction Models for Diagnosing Severe Aortic Stenosis Based on Aortic Valve Calcium on Cardiac Computed Tomography: Incorporation of Radiomics and Machine Learning

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