Computed Tomography-based Lung Residual Volume and Mortality of Patients With Coronavirus Disease-19 (COVID-19)

Abstract

Rationale and Objectives: To assess the effect of computed tomography (CT)-based residual lung volume (RLV) on mortality of patients with coronavirus disease 2019 (COVID-19). Materials and Methods: A single-center, retrospective study of a prospectively maintained database was performed. In total, 138 patients with COVID-19 were enrolled. Baseline chest CT scan was performed in all patients. CT-based automated and semi-automated lung segmentation was performed using the Alma Medical workstation to calculate normal lung volume, lung opacities volume, total lung volume, and RLV. The primary end point of the study was mortality. Univariate and multivariate analyses were performed to determine independent predictors of mortality. Results: Overall, 84 men (61%) and 54 women (39%) with a mean age of 47.3 years (±14.3 y) were included in the study. Overall mortality rate was 21% (29 patients) at a median time of 7 days (interquartile range, 4 to 11 d). Univariate analysis demonstrated that age, hypertension, and diabetes were associated with death (P<0.01). Similarly, patients who died had lower normal lung volume and RLV than patients who survived (P<0.01). Multivariate analysis demonstrated that low RLV was the only independent predictor of death (odds ratio, 1.042; 95% confidence interval, 10.2-10.65). Furthermore, receiver operating characteristic curve analysis demonstrated that a RLV ≤64% significantly increased the risk of death (odds ratio, 4.8; 95% confidence interval, 1.9-11.7). Conclusion: Overall mortality of patients with COVID-19 may reach 21%. Univariate and multivariate analyses demonstrated that reduced RLV was the principal independent predictor of death. Furthermore, RLV ≤64% is associated with a 4-fold increase on the risk of death.

Biomarker Evaluation for Prognostic Stratification of Patients with Covid-19: the Added Value of Quantitative Chest Ct

“…Though death rates were similar between these groups when values were >3.0 l, mortality increased significantly when volumes were below this threshold ( Figure 6 A & B). Comparable results have also been previously published by Montenegro et al , reporting that residual lung volume was an independent predictor of death among 138 patients with COVID-19 submitted to automated CT analysis [ 18 ]. These outcomes support the concept that automated CT data could provide complementary information to the multiple biomarker approach, especially in clinically stable patients initially classified at low to intermediate risk, such as those in groups I and II.…”

Semi-quantitative evaluation of chest computed tomography for coronavirus disease 2019 in a critical care unit: A case-control study

“…Furthermore, the cutoff value of residual lung volume was 64%, and the sensitivity and specificity were 85.3% and 50%, respectively. [14] In addition, others have reported that a mean CT value of −704 HU in the lungs of patients with COVID-19 had a sensitivity and specificity of 82% and 65%, respectively, for predicting COVID-19 severity. [15] Our results indicate that the association between the extent of abnormal opacity and mortality risk also applies to cases of severe COVID-19.…”

An Interpretable Chest CT Deep Learning Algorithm for Quantification of COVID-19 Lung Disease and Prediction of Inpatient Morbidity and Mortality

“…Indeed, some radiomic studies suggest the quantitative parenchymal involvement to be important indicators of severe outcomes. 15 , 16 , 17 The loss of accuracy from the severity scoring system can be attributed to the trade of interpretability for accuracy in any scaled heuristic. [18]…”