COVID-19 pneumonia: Prediction of patient outcome by CT-based quantitative lung parenchyma analysis combined with laboratory parameters

OBJECTIVES: To evaluate the prognostic value of fully automatic lung quantification based on spectral computed tomography (CT) and laboratory parameters for combined outcome prediction in COVID-19 pneumonia. METHODS: CT images of 53 hospitalized COVID-19 patients including virtual monochromatic reconstructions at 40-140keV were analyzed using a fully automated software system. Quantitative CT (QCT) parameters including mean and percentiles of lung density, fibrosis index (FIBI(-700), defined as the percentage of segmented lung voxels ≥-700 HU), quantification of ground-glass opacities and well-aerated lung areas were analyzed. QCT parameters were correlated to laboratory and patient outcome parameters (hospitalization, days on intensive care unit, invasive and non-invasive ventilation). RESULTS: Best correlations were found for laboratory parameters LDH (r = 0.54), CRP (r = 0.49), Procalcitonin (r = 0.37) and partial pressure of oxygen (r = 0.35) with the QCT parameter 75(th) percentile of lung density. LDH, Procalcitonin, 75(th) percentile of lung density and FIBI-(700) were the strongest independent predictors of patients’ outcome in terms of days of invasive ventilation. The combination of LDH and Procalcitonin with either 75(th) percentile of lung density or FIBI(-700) achieved a r(2) of 0.84 and 1.0 as well as an area under the receiver operating characteristic curve (AUC) of 0.99 and 1.0 for the prediction of the need of invasive ventilation. CONCLUSIONS: QCT parameters in combination with laboratory parameters could deliver a feasible prognostic tool for the prediction of invasive ventilation in patients with COVID-19 pneumonia.