Frontline Science: COVID-19 infection induces readily detectable morphologic and inflammation-related phenotypic changes in peripheral blood monocytes.

Excessive monocyte/macrophage activation with the development of a cytokine storm and subsequent acute lung injury, leading to acute respiratory distress syndrome (ARDS), is a feared consequence of infection with COVID-19. The ability to recognize and potentially intervene early in those patients at greatest risk of developing this complication could be of great clinical utility. In this study, we performed flow cytometric analysis of peripheral blood samples from 34 COVID-19 patients in early 2020 in an attempt to identify factors that could help predict the severity of disease and patient outcome. Although we did not detect significant differences in the number of monocytes between patients with COVID-19 and normal healthy individuals, we did identify significant morphologic and functional differences, which are more pronounced in patients requiring prolonged hospitalization and intensive care unit (ICU) admission. Patients with COVID-19 have larger than normal monocytes, easily identified on forward scatter (FSC), side scatter analysis by routine flow cytometry, with the presence of a distinct population of monocytes with high FSC (FSC-high). On more detailed analysis, these CD14+ CD16+ , FSC-high monocytes show features of mixed M1/M2 macrophage polarization with higher expression of CD80+ and CD206+ compared with the residual FSC-low monocytes and secretion of higher levels of IL-6, IL-10, and TNF-α, when compared with the normal controls. In conclusion, the detection and serial monitoring of this subset of inflammatory monocytes using flow cytometry could be of great help in guiding the prognostication and treatment of patients with COVID-19 and merits further evaluation.

Towards an Artificial Intelligence Framework for Data-Driven Prediction of Coronavirus Clinical Severity

The virus SARS-CoV2, which causes coronavirus disease (COVID-19) has become a pandemic and has spread to every inhabited continent. Given the increasing caseload, there is an urgent need to augment clinical skills in order to identify from among the many mild cases the few that will progress to critical illness. We present a first step towards building an artificial intelligence (AI) framework, with predictive analytics (PA) capabilities applied to real patient data, to provide rapid clinical decision-making support. COVID-19 has presented a pressing need as a) clinicians are still developing clinical acumen to this novel disease and b) resource limitations in a surging pandemic require difficult resource allocation decisions. The objectives of this research are: (1) to algorithmically identify the combinations of clinical characteristics of COVID-19 that predict outcomes, and (2) to develop a tool with AI capabilities that will predict patients at risk for more severe illness on initial presentation. The predictive models learn from historical data to help predict who will develop acute respiratory distress syndrome (ARDS), a severe outcome in COVID-19. Our results, based on data from two hospitals in Wenzhou, Zhejiang, China, identified features on initial presentation with COVID-19 that were most predictive of later development of ARDS. A mildly elevated alanine aminotransferase (ALT) (a liver enzyme), the presence of myalgias (body aches), and an elevated hemoglobin (red blood cells), in this order, are the clinical features, on presentation, that are the most predictive. The predictive models that learned from historical data of patients from these two hospitals achieved 70% to 80% accuracy in predicting severe cases.