780 Appearing and disappearing right-heart thrombus: a case reporto in COVID-19 patient

Aims Venous thromboembolism represents frequent complication of patients with severe COVID-19 disease. Several reports about atypical thrombosis are described, rarely it has been described a right venticular thrombus during the course of infection. We report a case of right endoventricular thrombosis in a patient with SARS-Cov-2 pneumonia. Methods and results A 58-year-old man was admitted to our ward for severe respiratory failure in interstitial pneumonia. The nasopharyngeal swab for COVID-19 resulted positive. Steroids and prophylaxis with LMWH were started, associated to CPAP to maintain good gas exchange. During hospitalization a venous ECD was performed with evidence of left popliteal thrombosis despite the therapy. d-Dimer was 4463 ng/ml. A new onset AF was documented at the telemetry, without troponin elevation. A cardiac ultrasound was performed showing a right endoventricular lesion of 1.8 cm adhering to the free wall of the right ventricle. A CT-pulmonary angiogram (CTPA) resulted negative for pulmonary embolism and confirmed suspected right ventricular thrombus. Treatment with fondaparinux 7.5 mg was started. After 10 days, cardiac ultrasound shown complete resolution of thrombosis, and CT confirmed the disappearing of the mass. Dabigatran 150 mg twice/day was started. Patient clinically improved and he was discharged after 20 days of hospitalization. Conclusions SARS-CoV-2 infection may cause inflammation with cytokine storm and hypercoagulability leading to venous thromboembolism. Atypical thrombus formation was reported, including right-ventricle free wall. Early caridac ultrasound was critical to make diagnosis and starting prompt treatment, therefore routine cardiac ultrasound is mandatory in severe COVID-19 patients.

Focused Cardiac Ultrasound Examination as a Tool for Diagnosis of Infective Endocarditis and Myocarditis in Dogs and Cats

Symptoms of infective endocarditis (IE) and myocarditis are usually nonspecific and include fever, apathy, and loss of appetite. This condition can lead to severe heart failure with ascites or/and fluid in the thoracic cavity or/and in the pericardial sac. We describe infective endocarditis and myocarditis in 3 dogs and 4 cats. In all animals, the initial diagnosis was performed on the basis of a focused cardiac ultrasound examination performed by a general practitioner after a training in this technique. The initial findings were confirmed by a board-certified specialist in veterinary cardiology. Post mortem positive microbiological results from valves were obtained in 4 of 7 patients. Methicillin-resistant Staphylococcus aureus was confirmed in 2 cases and Staphylococcus epidermidis was confirmed in 2 cases, one of which included Enterococcus sp. coinfection. Histopathological examination confirmed initial diagnosis in 5 of 7 animals. In the remaining 2 patients, the time elapsed from the onset of clinical symptoms to death was about 1 month and no active inflammation but massive fibrosis was found microscopically. This is, to our best knowledge, the first report of IE and myocarditis diagnosed in small animals using focused cardiac ultrasound examination. Therefore, we conclude that common usage of this technique by trained general veterinarians may increase the rate of diagnosed patients with these conditions.

Analysis of Cardiac Ultrasound Images of Critically Ill Patients Using Deep Learning

Cardiovascular disease remains a substantial cause of morbidity and mortality in the developed world and is becoming an increasingly important cause of death in developing countries too. While current cardiovascular treatments can assist to reduce the risk of this disease, a large number of patients still retain a high risk of experiencing a life-threatening cardiovascular event. Thus, the advent of new treatments methods capable of reducing this residual risk remains an important healthcare objective. This paper proposes a deep learning-based method for section recognition of cardiac ultrasound images of critically ill cardiac patients. A convolution neural network (CNN) is used to classify the standard ultrasound video data. The ultrasound video data is parsed into a static image, and InceptionV3 and ResNet50 networks are used to classify eight ultrasound static sections, and the ResNet50 with better classification accuracy is selected as the standard network for classification. The correlation between the ultrasound video data frames is used to construct the ResNet50 + LSTM model. Next, the time-series features of the two-dimensional image sequence are extracted and the classification of the ultrasound section video data is realized. Experimental results show that the proposed cardiac ultrasound image recognition model has good performance and can meet the requirements of clinical section classification accuracy.