Value of quantitative analysis in lung computed tomography in patients severely ill with COVID-19

Abstract

Introduction Quantitative computed tomography (QCT) is used to objectively assess the degree of parenchymal impairment in COVID-19 pneumonia. Materials and methods Retrospective study on 61 COVID-19 patients (severe and non-severe; 33 men, age 63+/-15 years) who underwent a CT scan due to tachypnea, dyspnoea or desaturation. QCT was performed using VCAR software. Patients’ clinical data was collected, including laboratory results and oxygenation support. The optimal cut-off point for CT parameters for predicting death and respiratory support was performed by maximizing the Youden Index in a receiver operating characteristic (ROC) curve analysis. Results The analysis revealed significantly greater progression of changes: ground-glass opacities (GGO) (31,42% v 13,89%, p<0.001), consolidation (11,85% v 3,32%, p<0.001) in patients with severe disease compared to non-severe disease. Five lobes were involved in all patients with severe disease. In non-severe patients, a positive correlation was found between severity of GGO, consolidation and emphysema and sex, tachypnea, chest x-ray (CXR) score on admission and laboratory parameters: CRP, D-dimer, ALT, lymphocyte count and lymphocyte/neutrophil ratio. In the group of severe patients, a correlation was found between sex, creatinine level and death. ROC analysis on death prediction was used to establish the cut-off point for GGO at 24.3% (AUC 0.8878, 95% CI 0.7889–0.9866; sensitivity 91.7%, specificity 75.5%), 5.6% for consolidation (AUC 0.7466, 95% CI 0.6009–0.8923; sensitivity 83.3%, specificity 59.2%), and 37.8% for total (GGO+consolidation) (AUC 0.8622, 95% CI 0.7525–0.972; sensitivity 75%, specificity 83.7%). The cut-off point for predicting respiratory support was established for GGO at 18.7% (AUC 0.7611, 95% CI 0.6268–0.8954; sensitivity 87.5%, specificity 64.4%), consolidation at 3.88% (AUC 0.7438, 95% CI 0.6146–0.8729; sensitivity 100%, specificity 46.7%), and total at 23.5% (AUC 0.7931, 95% CI 0.673–0.9131; sensitivity 93.8%, specificity 57.8%). Conclusion QCT is a good diagnostic tool which facilitates decision-making regarding intensification of oxygen support and transfer to an intensive care unit in patients severely ill with COVID-19 pneumonia. QCT can make an independent and simple screening tool to assess the risk of death, regardless of clinical symptoms. Usefulness of QCT to predict the risk of death is higher than to assess the indications for respiratory support.

When Conventional Oxygen Therapy Fails—The Effectiveness of High-Flow Nasal Oxygen Therapy in Patients with Respiratory Failure in the Course of COVID-19

“…Except age and comorbidities independently associated with higher risk of poor outcomes there are multiple biomarkers and results of image tests [29][30][31]. In our research, a significant association between death and ineffectiveness of HFNO was observed with respect to the values of CRP, procalcitonin, ferritin, D-dimers, creatinine, LDH and lymphocytes as well as intensification of changes in CT scans.…”

Can Deep Learning-Based Volumetric Analysis Predict Oxygen Demand Increase in Patients with COVID-19 Pneumonia?

“…The rapidly increasing GGOs may be associated with early-stage symptoms. Rorat M et al retrospectively analyzed 61 patients with COVID-19 who underwent a CT scan due to suspicious symptoms of pneumonia during deterioration of health [23]. Quantitative CT was performed using deep learning and revealed a significantly higher severity of changes in type of GGOs and consolidation in patients with severe disease than in those with nonsevere disease.…”

“…There are several papers that suggest the usefulness of CT evaluation using deep learning of COVID-19, but each has different end points and analysis methods [24,25]. Additionally, most of the studies focus on detection and diagnosis, and those that consider severity and prognosis are relatively rare [23]. The advantages of our method are as follows: 1.…”

Evolution of CT Findings and Lung Residue in Patients with COVID-19 Pneumonia: Quantitative Analysis of the Disease with a Computer Automatic Tool

“…Total lesion calculation was also performed, which made a total of ground-glass opacity and consolidation volumes [ 17 ]. The Thoracic VCAR is already in clinical practice in Chest CT affected by COVID-19 infections [ 17 , 29 , 30 ].…”