Quantitative chest CT analysis in COVID-19 to predict the need for oxygenation support and intubation

Abstract

Objective Lombardy (Italy) was the epicentre of the COVID-19 pandemic in March 2020. The healthcare system suffered from a shortage of ICU beds and oxygenation support devices. In our Institution, most patients received chest CT at admission, only interpreted visually. Given the proven value of quantitative CT analysis (QCT) in the setting of ARDS, we tested QCT as an outcome predictor for COVID-19. Methods We performed a single-centre retrospective study on COVID-19 patients hospitalised from January 25, 2020, to April 28, 2020, who received CT at admission prompted by respiratory symptoms such as dyspnea or desaturation. QCT was performed using a semi-automated method (3D Slicer). Lungs were divided by Hounsfield unit intervals. Compromised lung (%CL) volume was the sum of poorly and non-aerated volumes (− 500, 100 HU). We collected patient's clinical data including oxygenation support throughout hospitalisation. Results Two hundred twenty-two patients (163 males, median age 66, IQR 54-6) were included; 75% received oxygenation support (20% intubation rate). Compromised lung volume was the most accurate outcome predictor (logistic regression, p < 0.001). %CL values in the 6-23% range increased risk of oxygenation support; values above 23% were at risk for intubation. %CL showed a negative correlation with PaO 2 /FiO 2 ratio (p < 0.001) and was a risk factor for in-hospital mortality (p < 0.001). Conclusions QCT provides new metrics of COVID-19. The compromised lung volume is accurate in predicting the need for oxygenation support and intubation and is a significant risk factor for in-hospital death. QCT may serve as a tool for the triaging process of COVID-19. Key Points • Quantitative computer-aided analysis of chest CT (QCT) provides new metrics of COVID-19. • The compromised lung volume measured in the − 500, 100 HU interval predicts oxygenation support and intubation and is a risk factor for in-hospital death. • Compromised lung values in the 6-23% range prompt oxygenation therapy; values above 23% increase the need for intubation.

Validation of Chest Computed Tomography Artificial Intelligence to Determine the Requirement for Mechanical Ventilation and Risk of Mortality in Hospitalized Coronavirus Disease-19 Patients in a Tertiary Care Center In Mexico City

“…Recently, Lessmann et al developed an AI system that accurately identified COVID-19 patients with high diagnostic performance and assigned SS in good agreement with the experienced radiologist 17 . Lanza et al also used computer-aided quantitative analysis of CT images to determine compromised lung volumes and predict the need for oxygenation support and intubation 18 . They found that patients with compromised lung volumes of > 23% were at risk for intubation.…”

Deep learning for automatic quantification of lung abnormalities in COVID-19 patients: First experience and correlation with clinical parameters

“…Another approach was applied by Colombi et al [ 18 ] and Lanza et al [ 19 ] determining the value of quantifying the extent of well-aerated lung at baseline chest CT in COVID-19 patients. Both groups used an open-source (not deep learning-based) 3D slicer software to quantify the percentage and absolute volume of well aerated lung in SARS-CoV-2 positive patients at baseline CT.…”

“…Comparing our results to that from Colombi et al [ 18 ], the deep learning algorithm provided a faster (120 s vs. 270 s) and more accurate segmentation with post-hoc corrections only necessary in 7% of cases (vs. 61 %). Lanza et al [ 19 ] reported a median time for segmentation of 11 min. The automatic chest CT analysis in our study was carried out on a standard PACS workstation of our institution.…”

A Prognostic Model for Death in COVID-19 Patients Presenting to the Emergency Room: The Added Value of Computed Tomography.

“…The potential prognostic role of CT-based assessment of lung disease extension has been suggested [13][14][15][16]27], and a few studies have included it in combined prognostic models [17][18][19][20]. Colombi et al found a slightly higher increase in AUC (from 0.83 to 0.86) when adding CT disease extension to a clinical model predictive of intensive care unit admission and/or death [17].…”