Abstract
The purpose of our work was to assess the independent and incremental value of AI-derived quantitative determination of lung lesions extent on initial CT scan for the prediction of clinical deterioration or death in patients hospitalized with COVID-19 pneumonia. 323 consecutive patients (mean age 65 ± 15 years, 192 men), with laboratory-confirmed COVID-19 and an abnormal chest CT scan, were admitted to the hospital between March and December 2020. The extent of consolidation and all lung opacities were quantified on an initial CT scan using a 3D automatic AI-based software. The outcome was known for all these patients. 85 (26.3%) patients died or experienced clinical deterioration, defined as intensive care unit admission. In multivariate regression based on clinical, biological and CT parameters, the extent of all opacities, and extent of consolidation were independent predictors of adverse outcomes, as were diabetes, heart disease, C-reactive protein, and neutrophils/lymphocytes ratio. The association of CT-derived measures with clinical and biological parameters significantly improved the risk prediction (p = 0.049). Automated quantification of lung disease at CT in COVID-19 pneumonia is useful to predict clinical deterioration or in-hospital death. Its combination with clinical and biological data improves risk prediction.
Highlights
The SARS-COV-2 is currently responsible for a worldwide pandemic that may lead to severe viral pneumonia with critical complications, including acute respiratory failure requiring admission in an intensive care unit (ICU) for mechanical ventilation or high flow oxygenation
Despite two studies showing that lung disease extent in COVID19 pneumonia assessed by visual scoring correlates with clinical disease severity [19,20], visual estimation of disease extent even done by experimented radiologists may be a source of variability
We recorded the delay between the beginning of symptoms and the first CT scan performed at admission or within the 48 h after admission and collected all biological data, including inflammation indicators that have been previously reported to be associated with the prognosis of COVID-19 [10]
Summary
The SARS-COV-2 is currently responsible for a worldwide pandemic that may lead to severe viral pneumonia with critical complications, including acute respiratory failure requiring admission in an intensive care unit (ICU) for mechanical ventilation or high flow oxygenation. Artificial Intelligence (AI) software developed to help radiologists in the quantification of lung involvement in COVID-19 may overcome this limitation [21]. Some investigators developed their own AI system for accurate quantitative measurements and prognosis of COVID-19 pneumonia using CT [22,23,24]. Their systems were trained and validated on data from a single-center or few centers located in the same geographical area. This may question the reproducibility of AI system performances when applied in other regions or countries
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