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.

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 hospitalized from January 25th, 2020 to April 28th 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 (-500,100HU). We collected patient’s clinical data including oxygenation support throughout hospitalization.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 PaO2/FiO2 ratio (p<.001) and was a risk factor for in-hospital mortality (p<.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.

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Several patients who have recovered from COVID-19 pneumonia showed persistent infection at follow-up chest CT (31-63 days after disease onset) despite being asymptomatic and testing negative at rRT-PCR.

Background Quantitative CT (QCT) analysis is an invaluable diagnostic tool to assess lung injury and predict prognosis of patients affected by COVID-19 pneumonia. PTX3 was recently described as one of the most reliable serological predictors of clinical deterioration and short-term mortality. The present study was designed to evaluate a correlation between serological biomarkers of inflammation and lung injury measured by QCT. Methods This retrospective monocentric study analysed a cohort of patients diagnosed with COVID-19 and admitted because of respiratory failure, or significant radiological involvement on chest CT scan. All patients, males and non-pregnant females older than 18 years, underwent chest CT scan and laboratory testing at admission. Exclusion criteria were defined by concurrent acute pathological processes and ongoing specific treatments which could interfere with immune activity. The cohort was stratified based on severity in mild and severe forms. Compromised lung at QCT was then correlated to serological biomarkers representative of Sars-CoV-2. We further developed a multivariable logistic model to predict CT data and clinical deterioration based on a specific molecular signature. Internal cross-validation led to evaluate discrimination, calibration, and clinical utility of the tool that was provided by a score to simplify its application. Findings 592 patients were recruited between March 19th and December 1st, 2020. Applying exclusion criteria which consider confounders, the cohort resulted in 366 individuals characterized by 177 mild and 189 severe forms. In our predictive model, blood levels of PTX3, CRP and LDH were found to correlate with QCT values in mild COVID-19 disease. A signature of these three biomarkers had a high predictive accuracy in detecting compromised lungs as assessed by QCT. The score was elaborated and resulted representative of lung CT damage leading to clinical deterioration and oxygen need in mild disease. Interpretation The LDH, PTX3, CRP blood signature can serve as a strong correlate of compromised lung in COVID-19, possibly integrating cellular damage, systemic inflammation, myeloid and endothelial cell activation.