ABSTRACT
Objective To summarize the incidence of cardiac allograft vasculopathy (CAV) after heart transplantation and the effect on the long-term survival of recipients. Methods Clinical data of 1 006 heart transplant recipients were retrospectively analyzed. Of 48 CAV patients, 4 cases were not included in this analysis due to lack of imaging evidence. A total of 1 002 recipients were divided into the CAV group (n=44) and non-CAV group (n=958) according to the incidence of CAV. The incidence of CAV was summarized. Clinical data of all patients were statistically compared between two groups. Imaging diagnosis, coronary artery disease, drug treatment and complications, postoperative survival and causes of death of CAV patients were analyzed. Results Among 1 006 heart transplant recipients, 48 cases (4.77%) developed CAV. Compared with the non-CAV group, the proportion of preoperative smoking history, preoperative hypertension history, coronary artery disease and perioperative infection was significantly higher in the CAV group (all P < 0.05). Among 44 patients diagnosed with CAV by imaging examination, 24 cases were diagnosed with CAV by coronary CT angiography (CTA), 4 cases by coronary angiography (CAG), and 16 cases by coronary CTA combined with CAG. Among 44 patients, the proportion of grade Ⅰ CAV was 45% (20/44), 30% (13/44) for grade Ⅱ CAV and 25% (11/44) for grade Ⅲ CAV, respectively. All patients received long-term use of statins after operation, and 20 patients were given with antiplatelet drugs. Among 44 CAV patients, 11 patients underwent percutaneous coronary intervention, 6 cases received repeated heart transplantation, and 8 patients died. Kaplan-Meier survival analysis demonstrated that there was no significant difference in the long-term survival rate between the CAV and non-CAV groups (P > 0.05), whereas the survival rate of patients tended to decline after the diagnosis of CAV (at postoperative 6-7 years). The long-term survival rates of patients with grade Ⅰ, grade Ⅱ and grade Ⅲ CAV showed no significant difference (P > 0.05). Even for patients with grade Ⅰ CAV, the long-term survival rate tended to decline. Conclusions CAV is a common and intractable complication following heart transplantation, and the long-term survival rate of patients after the diagnosis of CAV tended to decline. Deepening understanding of CAV, prompt prevention, diagnosis and treatment should be delivered to improve the long-term survival rate of patients after heart transplantation.
ABSTRACT
INTRODUCTION: The rupture of atherosclerotic plaques causes millions of death yearly. It is known that the kind of predominant tissue is associated with its dangerousness. In addition, the mechanical properties of plaques have been proved to be a good parameter to characterize the type of tissue, important information for therapeutic decisions. METHODS: Therefore, we present an alternative and simple way to discriminate tissues. The procedure relies on computing an index, the ratio of the plaque area variation of a suspecting plaque, using images acquired with vessel and plaques, pre and post-deformation, under different intraluminal pressure. Numerical phantoms of coronary cross-sections with different morphological aspects, and simulated with a range of properties, were used for evaluation. RESULTS: The outcomes provided by this index and a widely used one were compared, so as to measure their correspondence. As a result, correlations up to 99%, a strong agreement with Bland-Altman and very similar histograms between the two indices, have shown a good level of equivalence between the methods. CONCLUSION: The results demonstrated that the proposed index discriminates highly lipidic from fibro-lipidic and calcified tissues in many situations, as good as the widely used index, yet the proposed method is much simpler to be computed.
ABSTRACT
Por ser capaz de mostrar aspectos morfológicos e patológicos de ateroscleroses, o Ultrassom Intravascular (IVUS) se tornou uma das modalidades de imagens médicas mais confiáveis e empregadas em intervenções cardíacas. As características de sua imagem aumentam as chances de um bom diagnóstico, resultando em terapias mais precisas. O estudo de segmentação da fronteira média-adventícia, dentre muitas aplicações, é importante para o aprendizado das propriedades mecânicas e determinação de algumas medidas específicas (raio, diâmetro, etc.) em vasos e placas. Neste trabalho, uma associação de técnicas de processamento de imagens está sendo proposta para atingir alta acurácia na segmentação da borda média-adventícia. Para tanto, foi feita uma combinação das seguintes técnicas: Redução do Speckle por Difusão Anisotrópica (SRAD), Wavelet, Otsu e Morfologia Matemática. Primeiramente, é usado SRAD para atenuar os ruídos speckle. Posteriormente, é executada Transformada Wavelet para extração das características dos vasos e placas. Uma versão binarizada dessas características é criada na qual o limiar ótimo é definido por Otsu. Finalmente, é usada Morfologia Matemática para obtenção do formato da adventícia. O método proposto é avaliado ao segmentar 100 imagens de alta complexidade, obtendo uma média de Verdadeiro Positivo (TP(%)) = 92,83 ± 4,91, Falso Positivo (FP(%)) = 3,43 ± 3,47, Falso Negativo (FN(%)) = 7,17 ± 4,91, Máximo Falso Positivo (MaxFP(mm)) = 0,27 ± 0,22, Máximo Falso Negativo (MaxFN(mm)) = 0,31 ± 0,2. A eficácia do nosso método é demonstrada, comparando este resultado com outro trabalho recente na literatura.
By being able to show morphological and pathological aspects of atherosclerosis, the Intravascular Ultrasound (IVUS) be¬came one of the most reliable and employed medical imaging modality in cardiac interventions. Its image characteristics in¬crease the chances of a good diagnostic, resulting in a precise therapy. The study of media-adventitia borders segmentation in IVUS, among many applications, is important for learning about the mechanical properties and determining some specific measurements (radius, diameter, etc.) in vases and plaques. An approach is proposed to achieve high accuracy in media-adventitia borders segmentation, by making a combination of different image processing operations: Speckle Reducing Anisotropic Diffusion (SRAD), Wavelet, Otsu and Mathematical Morphology. Firstly, SRAD is applied to attenuate the speckle noise. Next, the vessel and plaque features are extracted by performing Wavelet Transform. Optimal thresholding is car¬ried out by Otsu method to create a binarized version of these features. Then, Mathematical Morphology operations are used to obtain an adventitia shape. The proposed approach is evaluated by segmenting 100 challenging images, obtaining an average of True Positive (TP(%)) = 92.83 ± 4.91, False Positive (FP(%)) = 3.43 ± 3.47, False Negative (FN(%)) = 7.17 ± 4.91, Max False Positive (MaxFP(mm)) = 0.27 ± 0.22, Max False Negative (MaxFN(mm)) = 0.31 ± 0.2. The effectiveness of our approach is demonstrated by comparing this result with another recent work in the literature.