CT-based pathological lung volume and adverse outcomes of patients with Coronavirus Disease 2019 (COVID-19)

Objective: To assess the association between CT-based percentage of pathological lung opacities volume (%PLOV) and the occurrence of adverse outcomes of patients with COVID-19. Methods: An observational, longitudinal, single-center study was performed including patients with COVID-19. CT-based lung segmentation was performed to calculate %PLOV. The primary endpoint was the occurrence of adverse lung event (ALE), defined as ICU admission, the use of mechanical ventilation, or death. Mann-Whitney U test was performed for univariate analysis. Logistic regression analysis was performed to determine independent predictors of critical illness. Results: 138 patients (84 men [61%]) with a mean age of 47.3 years were enrolled. Median %PLOV was 28.64% (interquartile range [IQR], 6.33-47.22%). ALE occurred in 52 patients (38%) with an overall mortality rate of 21% (29 patients). Multivariate analysis demonstrated that %PLOV was an independent predictor of ALE with an Odds ratio of 1.049 (95% confidence interval [CI], 1.014-1.085) (p < 0.01). Furthermore, a %PLOV of 64% demonstrated a 25.5-fold increased risk of ALE with a sensitivity and specificity higher than 75% (p < 0.01). Conclusion: The quantitative evaluation of chest CT impacts the determination of severity of COVID-19 pneumonia on admission. %PLOV was the strongest predictor for the development of ALE in hospitalized patients.

Abstract licence: CC BY-NC-ND

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