Editor's Commentary

An introduction to articles published within the issue is presented on topics including the effects of physiotherapy on hemodynamics, gas exchange and cerebral physiology in ventilated subjects, an evaluation of four mechanical insufflation-exsufflation (MI-E) devices, and a session of intermittent intrapulmonary deflation technique and positive expiratory pressure therapy in chronic obstructive pulmonary disease (COPD) patients.

Quantitative computerized tomography evaluation of the effects of COVID-19 pneumonia on lung volume

Purpose: The aim of this study was o assess the relationship between lung volume decrease and computed tomography (CT) findings in patients with COVID-19 pneumonia in early period. Materials and Methods: Fifty-four patients were included in the study. The lung volume (LV) was calculated separately for each lung by software-based quantitative CT (QCT). Patient demographics, comorbidity and smoking status, CT findings, visual semi-quantitative CT severity scoring (CT-SS), and decrease of LV were analyzed. Results: The rate of volume decrease was not statistically related to, age, gender, smoking, or hospitalization status. When the correlation between follow-up CT (FUCT) LV and CT-SS was examined there were good inverse correlation on the right lung (r = -0.583;p = 0.001) and left lung (r = -0.478;p = 0.001). The rate of decrease in the right LV was significantly higher in patients with comorbidities compared to other patients. There was a statistically moderate inverse correlation between decrease of LV and CT-SS in the right lung (r = -0.294;p = 0.031), and no significant correlation was found between the decrease of LV and CT-SS in the left lung (r = -0.096;p = 0.489). Conclusion: The rate of lung damage and associated volume decrease both increase with the amount of parenchymal involvement in patients with COVID-19 pneumonia. This change is more frequent in patients with multiple comorbidities. Accurate interpretation of CT findings with quantitative data can help physicians to manage the disease.

COVID-19 CT Scan Lung Segmentation: How We Do It.

The National Health Systems have been severely stressed out by the COVID-19 pandemic because 14% of patients require hospitalization and oxygen support, and 5% require admission to an Intensive Care Unit (ICU). Relationship between COVID-19 prognosis and the extent of alterations on chest CT obtained by both visual and software-based quantification that expresses objective evaluations of the percentage of ventilated lung parenchyma compared to the affected one has been proven. While commercial applications for automatic medical image computing and visualization are expensive and limited in their spread, the open-source systems are characterized by not enough standardization and time-consuming troubles. We analyzed chest CT exams on 246 patients suspected of COVID-19 performed in the Emergency Department CT room. The lung parenchyma segmentation was obtained by a threshold-based method using the open-source 3D Slicer software and software tools called "Segment Editor" and "Segment Quantification." For the three main characteristics analyzed on lungs affected by COVID-19 pneumonia, a specifical densitometry value range was defined: from - 950 to - 700 HU for well-aerated parenchyma; from - 700 to - 250 HU for interstitial lung disease; from - 250 to 250 HU for parenchymal consolidation. For the well-aerated parenchyma and the interstitial alterations, the procedure was semi-automatic with low time consumption, whereas consolidations' analysis needed manual interventions by the operator. After the chest CT, 13% of the sample was admitted to intensive care, while 34% of them to the sub-intensive care. In patients moved to intensive care, the parenchyma analysis reported a higher crazy paving presentation. The quantitative analysis of the alterations affecting the lung parenchyma of patients with COVID-19 pneumonia can be performed by threshold method segmentation on 3D Slicer. The segmentation could have an important role in the quantification in different COVID-19 pneumonia presentations, allowing to help the clinician in the correct management of patients.

Comparison of semiquantitative chest CT scoring systems to estimate severity in coronavirus disease 2019 (COVID-19) pneumonia.

OBJECTIVES: To compare the clinical usefulness among three different semiquantitative computed tomography (CT) severity scoring systems for coronavirus disease 2019 (COVID-19) pneumonia. METHODS: Two radiologists independently reviewed chest CT images in 108 patients to rate three CT scoring systems (total CT score [TSS], chest CT score [CCTS], and CT severity score [CTSS]). We made a minor modification to CTSS. Quantitative dense area ratio (QDAR: the ratio of lung involvement to lung parenchyma) was calculated using the U-net model. Clinical severity at admission was classified as severe (n = 14) or mild (n = 94). Interobserver agreement, interpretation time, and degree of correlation with clinical severity as well as QDAR were evaluated. RESULTS: Interobserver agreement was excellent (intraclass correlation coefficient: 0.952-0.970, p < 0.001). Mean interpretation time was significantly longer in CTSS (48.9-80.0 s) than in TSS (25.7-41.7 s, p < 0.001) and CCTS (27.7-39.5 s, p < 0.001). Area under the curve for differentiating clinical severity at admission was 0.855-0.842 in TSS, 0.853-0.850 in CCTS, and 0.853-0.836 in CTSS. All scoring systems correlated with QDAR in the order of CCTS (ρ = 0.443-0.448), TSS (ρ = 0.435-0.437), and CTSS (ρ = 0.415-0.426). CONCLUSIONS: All semiquantitative scoring systems demonstrated substantial diagnostic performance for clinical severity at admission with excellent interobserver agreement. Interpretation time was significantly shorter in TSS and CCTS than in CTSS. The correlation between the scoring system and QDAR was highest in CCTS, followed by TSS and CTSS. CCTS appeared to be the most appropriate CT scoring system for clinical practice. KEY POINTS: • Three semiquantitative scoring systems demonstrate substantial accuracy (area under the curve: 0.836-0.855) for diagnosing clinical severity at admission and (area under the curve: 0.786-0.802) for risk of developing critical illness. • Total CT score (TSS) and chest CT score (CCTS) were considered to be more appropriate in terms of clinical usefulness as compared with CT severity score (CTSS), given the shorter interpretation time in TSS and CCTS, and the lowest correlation with quantitative dense area ratio in CTSS. • CCTS is assumed to distinguish subtle from mild lung involvement better than TSS by adopting a 5% threshold in scoring the degree of severity.

Computed tomography semi-automated lung volume quantification in SARS-CoV-2-related pneumonia.

OBJECTIVES: To evaluate a semi-automated segmentation and ventilated lung quantification on chest computed tomography (CT) to assess lung involvement in patients affected by SARS-CoV-2. Results were compared with clinical and functional parameters and outcomes. METHODS: All images underwent quantitative analyses with a dedicated workstation using a semi-automatic lung segmentation software to compute ventilated lung volume (VLV), Ground-glass opacity (GGO) volume (GGO-V), and consolidation volume (CONS-V) as absolute volume and as a percentage of total lung volume (TLV). The ratio between CONS-V, GGO-V, and VLV (CONS-V/VLV and GGO-V/VLV, respectively), TLV (CONS-V/TLV, GGO-V/TLV, and GGO-V + CONS-V/TLV respectively), and the ratio between VLV and TLV (VLV/TLV) were calculated. RESULTS: A total of 108 patients were enrolled. GGO-V/TLV significantly correlated with WBC (r = 0.369), neutrophils (r = 0.446), platelets (r = 0.182), CRP (r = 0.190), PaCO2 (r = 0.176), HCO3- (r = 0.284), and PaO2/FiO2 (P/F) values (r = - 0.344). CONS-V/TLV significantly correlated with WBC (r = 0.294), neutrophils (r = 0.300), lymphocytes (r = -0.225), CRP (r = 0.306), PaCO2 (r = 0.227), pH (r = 0.162), HCO3- (r = 0.394), and P/F (r = - 0.419) values. Statistically significant differences between CONS-V, GGO-V, GGO-V/TLV, CONS-V/TLV, GGO-V/VLV, CONS-V/VLV, GGO-V + CONS-V/TLV, VLV/TLV, CT score, and invasive ventilation by ET were found (all p < 0.05). CONCLUSION: The use of quantitative semi-automated algorithm for lung CT elaboration effectively correlates the severity of SARS-CoV-2-related pneumonia with laboratory parameters and the need for invasive ventilation. KEY POINTS: • Pathological lung volumes, expressed both as GGO-V and as CONS-V, can be considered a useful tool in SARS-CoV-2-related pneumonia. • All lung volumes, expressed themselves and as ratio with TLV and VLV, correlate with laboratory data, in particular C-reactive protein and white blood cell count. • All lung volumes correlate with patient's outcome, in particular concerning invasive ventilation.

Compromised Lung Volume and Hemostatic Abnormalities in COVID-19 Pneumonia: Results from an Observational Study on 510 Consecutive Patients.

BACKGROUND: Hemostatic abnormalities have been described in COVID-19, and pulmonary microthrombosis was consistently found at autopsy with concomitant severe lung damage. METHODS: This is a retrospective observational cross-sectional study including consecutive patients with COVID-19 pneumonia who underwent unenhanced chest CT upon admittance at the emergency room (ER) in one large academic hospital. QCT was used for the calculation of compromised lung volume (%CL). Clinical data were retrieved from patients' files. Laboratory data were obtained upon presentation at the ER. AIM: The aim of this study was to evaluate the correlation between hemostatic abnormalities and lung involvement in patients affected by COVID-19 pneumonia as described using computer-aided quantitative evaluation of chest CT (quantitative CT (QCT)). RESULTS: A total of 510 consecutive patients (68% males), aged 67 years in median, diagnosed with COVID-19 pneumonia, who underwent unenhanced CT scan upon admission to the ER, were included. In all, 115 patients had %CL > 23%; compared to those with %CL < 23%, they showed higher levels of D-dimer, fibrinogen, and CRP, greater platelet count, and longer PT ratio. Via multivariate regression analysis, BMI ≥ 30 kg/m2, D-dimer levels > 500 ng/mL, CRP > 5.0 ng/mL and PT ratio > 1.2 were found to be independent predictors of a %CL > 23% (adjusted odds ratios (95% confidence intervals): 2.1 (1.1-4.0), 3.1 (1.6-5.8), 2.4 (1.3-4.5), and 3.4 (1.4-8.5), respectively). CONCLUSIONS: Hemostatic abnormalities in patients affected by COVID-19 correlate with the severity of lung injury as measured by %CL. Our results underline the pathogenetic role of hemostasis in COVID-19 pneumonia beyond the presence of clinically evident thromboembolic complications.