Treatment Intensity Stratification in COVID-19 by Fully Automated Analysis of Pulmonary and Cardiovascular Metrics on Initial Chest CT using Deep Learning

Abstract ObjectivesTo predict ultimate treatment intensity of COVID-19 patients using pulmonary and cardiovascular metrics fully automatically extracted from initial chest CTs.Methods All patients tested positive for SARS-CoV-2 by RT-PCR at our emergency department between March 25 and April 14, 2020 were identified (n=391). For those patients, all initial chest CTs were analyzed (n=85). Multiple pulmonary and cardiovascular metrics were extracted using deep convolutional neural networks. Three clinical treatment intensity groups were defined according to the most intensive treatment of a patient, determined six weeks later: Group 1 (outpatient), group 2 (general ward), and group 3 (intensive care unit; ICU). Univariate analyses were performed to analyze differences between groups. Subsequently, multiple metrics were combined in two binary logistic regression analyses and resulting prediction probabilities used to classify whether a patient needed hospitalization or ICU care. For analysis of discriminatory power, ROC curves were plotted and areas-under-the-curves (AUCs) calculated.ResultsThe mean interval between presentation at the emergency department and the chest CT was 1.4 days. Among others, mean percentage of lung volume affected by opacities (PO) and mean total pericardial volume (TPV) increased statistically significantly with higher treatment intensity [from group 1 to 3, standard deviation in brackets: PO: 0.8%(1.5)–11.6%(13.1)–31.6%(20.1); TPV: 733.4ml(231.7)–866.2ml(211.2)–925.7ml(125.5); both: p<0.001]. AUCs were 0.85 (ICU vs. no ICU) and 0.94 (hospitalization vs. no hospitalization).Conclusions Metrics fully automatically extracted from initial chest CTs increase with treatment intensity of COVID-19 patients. This information can be exploited to prospectively manage allocation of healthcare resources.