The connection between hospital mortality and inflamation markers in COVID-19 patients and ischaemic heart disease

The purpose of the work to investigate the connection between hospital mortality and markers of systemic inflammation in COVID-19 patients with ischemic heart disease (IHD).Material and methods. The data of 52 patients’cases of diseases on COVID-19 with IHD who underwent inpatient treatment were analyzed. The first group included 40 patients who were discharged with recovery, and the second group included 12 patients who died. The degree of systemic inflammation syndrome in the selected groups of patients was estimated by the number of leukocytes and cellular composition of peripheral blood upon admission to the hospital, and based on the data received, the value of hematological integral indices was calculated.Results. It has been established that group II patients who died of cardiovascular complications connected with COVID-19 had significantly higher levels of systemic inflammatory response, which exhibits a significant increase in the total blood white blood cell content and increases in the percentage of neutrophils with a decrease in the percentage of lymphocytes and is substantially higher than the integrated haematological indices: leukocyte shift index, index of the ratio of neutrophils to lymphocytes and index of the ratio of neutrophils to monocytes. During the systemic inflammatory reaction, it has been observed that the inflammatory process aggravates the coronary atherosclerotic plaque making them more susceptible to rupture. Therefore, we can assume that existing IHD combined with an enhanced inflammatory response can lead to heart damage in patients infected with SARS-CoV- 2.Conclusion. Lymphocytopenia, excessive activation of the inflammatory cascade and heart damage are important features of COVID-19 disease and have high predictive value.

Coronary Plaque Characterization From Optical Coherence Tomography Imaging With a Two-Pathway Cascade Convolutional Neural Network Architecture

Background: The morphological structure and tissue composition of a coronary atherosclerotic plaque determine its stability, which can be assessed by intravascular optical coherence tomography (OCT) imaging. However, plaque characterization relies on the interpretation of large datasets by well-trained observers. This study aims to develop a convolutional neural network (CNN) method to automatically extract tissue features from OCT images to characterize the main components of a coronary atherosclerotic plaque (fibrous, lipid, and calcification). The method is based on a novel CNN architecture called TwopathCNN, which is utilized in a cascaded structure. According to the evaluation, this proposed method is effective and robust in the characterization of coronary plaque composition from in vivo OCT imaging. On average, the method achieves 0.86 in F1-score and 0.88 in accuracy. The TwopathCNN architecture and cascaded structure show significant improvement in performance (p < 0.05). CNN with cascaded structure can greatly improve the performance of characterization compared to the conventional CNN methods and machine learning methods. This method has a higher efficiency, which may be proven to be a promising diagnostic tool in the detection of coronary plaques.