ABSTRACT
A novel SARS -CoV2 virus (COVID 19) that led to an outbreak of pneumonia in Wuhan, China is now known to involve multiple organ systems presenting with acute respiratory distress syndrome, gastrointestinal disease, cardiac disease, skin lesions, renal involvement, hepatic damage, and multi organ failure. Even though the mortality and morbidity of this disease is higher in the older age group, clusters of children having severe illness requiring critical care have also been identified. Severe disease in children infected with COVID 19 is characterized by a multisystem inflammatory condition and a clinical presentation similar to Kawasaki disease. COVID 19 and its multi organ involvement is due to the underlying hyperinflammatory syndrome leading to a surge in cytokine levels causing injury to several cell types including cardiac myocytes and endothelial cells. The binding of SARS CoV2 to the ACE 2 receptor expressed in the endothelial cells, pneumocytes and myocardial cells is responsible for the clinical manifestations occurring in skin, heart and other organs. © Springer Nature Switzerland AG 2021.
ABSTRACT
Introduction: Standard, un-gated chest CT can be used as the basis of detailed segmentation of the atrial and ventricular cardiac chambers. In conditions such as COVID19 where dedicated cardiac imaging may be hazardous or unavailable atlas-based machine learning tools allow automatic quantification of cardiac morphology and may allow early detection of abnormalities. Purpose: To develop an automated screening tool to detect cardiac changes associated with COVID19 on chest/lung CT to allow early treatment and appropriate selection of patients for dedicated cardiac imaging. Methods: A previously validated atlas-based cardiac contouring algorithm was modified to work within the setting of variable and severe lung pathology. The modified technique was used to segment the left and right atria and ventricles from non-contrast CT scans. We applied the developed algorithm to the Moscow University COVID19 CT dataset. 1110 scans were available. COVID19 severity was graded 0 to 4. Grade 4 was not used in analysis due to insufficient numbers. Cardiac chamber sizes were compared according to COVID19 severity status. In a limited cohort of repeat studies, the feasibility of polar mapping to demonstrated serial morphological change was tested. Results: A statistically significant increase of average cardiac chamber volumes was noted relative to mild Grade 0 COVID19 at every incremental severity grade (Figure 1). Changes in average ventricular volumes were greater (up to 15.2% and 16.9% for left and right ventricles) than changes in atrial volumes (up 12.1% and 7.6% for left and right atria). Automated quantification was successful in the large majority of cases and interpatient polar mapping of sequential data to detect progressive chamber enlargement appears feasible (Figure 2). Conclusion: Machine learning methods permit automatic quantification of cardiac chamber size from standard lung CT scans. Cardiac changes on lung CT examinations may be used to identify cardiac abnormalities at an early stage and could be useful to triage individuals for dedicated cardiac investigations. With further refinement, this method may be useful to detect and track temporal cardiac changes in COVID19, as well as in other pulmonary pathology and conditions in which chest CT is routinely used. (Figure Presented).