Role of computed tomography in predicting critical disease in patients with covid-19 pneumonia: A retrospective study using a semiautomatic quantitative method. Issue 130 (September 2020) - Leonardi, Andrea; Scipione, Roberto; Alfieri, Giulia; Petrillo, Roberta; Dolciami, Miriam; Ciccarelli, Fabio; Perotti, Stefano; Cartocci, Gaia; Scala, Annarita; Imperiale, Carmela; Iafrate, Franco; Francone, Marco; et al.
Abstract: Background: So far, only a few studies evaluated the correlation between CT features and clinical outcome in patients with COVID-19 pneumonia. Purpose: To evaluate CT ability in differentiating critically ill patients requiring invasive ventilation from patients with less severe disease. Methods: We retrospectively collected data from patients admitted to our institution for COVID-19 pneumonia between March 5th-24th. Patients were considered critically ill or non-critically ill, depending on the need for mechanical ventilation. CT images from both groups were analyzed for the assessment of qualitative features and disease extension, using a quantitative semiautomatic method. We evaluated the differences between the two groups for clinical, laboratory and CT data. Analyses were conducted on a per-protocol basis. Results: 189 patients were analyzed. PaO2 /FIO2 ratio and oxygen saturation (SaO2 ) were decreased in critically ill patients. At CT, mixed pattern (ground glass opacities (GGO) and consolidation) and GGO alone were more frequent respectively in critically ill and in non-critically ill patients (p < 0.05). Lung volume involvement was significantly higher in critically ill patients (38.5 % vs. 5.8 %, p < 0.05). A cut-off of 23.0 % of lung involvement showed 96 % sensitivity and 96 % specificity in distinguishing critically ill patients from patients with less severe disease. The fraction of involved lung was related to lactate dehydrogenase (LDH) levels, PaO2 /FIO2 ratio and SaO2 (p < 0.05). Conclusion: Lung disease extension, assessed using quantitative CT, has a significant relationship with clinical severity and may predict the need for invasive ventilation in patients with COVID-19.