Background: The sublingual microcirculation presumably exhibits disease-specific changes in function and morphology. Algorithm-based quantification of functional microcirculatory hemodynamic variables in handheld vital microscopy (HVM) has recently allowed identification of hemodynamic alterations in the microcirculation associated with COVID-19. In the present study we hypothesized that supervised deep machine learning could be used to identify previously unknown microcirculatory alterations, and combination with algorithmically quantified functional variables increases the model's performance to differentiate critically ill COVID-19 patients from healthy volunteers.
View Article and Find Full Text PDFObjectives: In this study, we hypothesized that coronavirus disease 2019 patients exhibit sublingual microcirculatory alterations caused by inflammation, coagulopathy, and hypoxemia.
Design: Multicenter case-controlled study.
Setting: Two ICUs in The Netherlands and one in Switzerland.