Well-aerated Lung Research Articles

Quantitative computed tomography assessment of pulmonary function and compensation after lobectomy and segmentectomy in lung cancer patients.

The effect of different surgical methods on postoperative lung function in patients with lung cancer is still inconclusive. The main objective of this study was to compare the effects of video-assisted thoracic surgery (VATS) lobectomy and segmentectomy on postoperative pulmonary function and compensatory changes in patients undergoing lung cancer surgery. A total of 120 patients (82 VATS lobectomy, 38 VATS segmentectomy) were assessed for demographic characteristics, baseline pulmonary function, tumor volume, T stage, and histological grade. Postoperative pulmonary function and compensatory changes [percentage of the well-aerated lung (WAL) total (or unilateral) lung volume (LV) (WAL%) and non-operated lung (NOL)] were measured at multiple time points up to 2 years. Logistic regression analysis identified factors associated with WAL% decline after 1 year. Both VATS lobectomy and segmentectomy led to a decrease in pulmonary function, with no significant difference in the extent of decline between the two groups (all P>0.05). Lobectomy triggered a more pronounced compensatory response, characterized by increased ipsilateral NOL volume over time. Segmentectomy induced minimal compensatory changes and had a minimal impact on pulmonary function. Factors associated with decreased pulmonary ventilation after 1 year differed between the lobectomy and segmentectomy groups. In the lobectomy group, a higher preoperative WAL% of ipsilateral NOL [odds ratio (OR) =1.073; 95% confidence interval (CI): 1.017-1.133; P=0.01] was associated with a higher risk of decline in pulmonary function, whereas in the segmentectomy group, the only influencing factor was the preoperative contralateral mean lung density (MLD) (OR =0.932; 95% CI: 0.884-0.984; P=0.01). Both lobectomy and segmentectomy after lung cancer surgery result in a decrease in WAL%, with lobectomy demonstrating a stronger pulmonary compensatory capacity. The application of quantitative computed tomography (CT) has shown significant value in predicting postoperative pulmonary function preservation and compensatory changes, providing strong support for personalized surgical decision-making.

Quantitative CT at Follow-Up of COVID-19 Pneumonia: Relationship with Pulmonary Function Tests.

The role of quantitative chest computed tomography (CT) is controversial in the follow-up of patients with COVID-19 pneumonia. The aim of this study was to test during the follow-up of COVID-19 pneumonia the association between pulmonary function tests (PFTs) and quantitative parameters extrapolated from follow-up (FU) CT scans performed at least 6 months after COVID-19 onset. The study included patients older than 18 years old, admitted to the emergency department of our institution between 29 February 2020 and 31 December 2020, with a diagnosis of COVID-19 pneumonia, who underwent chest CT at admission and FU CT at least 6 months later; PFTs were performed within 6 months of FU CT. At FU CT, quantitative parameters of well-aerated lung and pneumonia extent were identified both visually and by software using CT density thresholds. The association between PFTs and quantitative parameters was tested by the calculation of the Spearman's coefficient of rank correlation (rho). The study included 40 patients (38% females; median age 63 years old, IQR, 56-71 years old). A significant correlation was identified between low attenuation areas% (%LAAs) <950 Hounsfield units (HU) and both forced expiratory volume in 1s/forced vital capacity (FEV1/FVC) ratio (rho -0.410, 95% CIs -0.639--0.112, p = 0.008) and %DLCO (rho -0.426, 95% CIs -0.678--0.084, p = 0.017). The remaining quantitative parameters failed to demonstrate a significant association with PFTs (p > 0.05). At follow-up, CT scans performed at least 6 months after COVID-19 pneumonia onset showed %LAAs that were inversely associated with %DLCO and could be considered a marker of irreversible lung damage.

Mechanical power normalized to aerated lung predicts noninvasive ventilation failure and death and contributes to the benefits of proning in COVID-19 hypoxemic respiratory failure.

Concern exists that noninvasive ventilation (NIV) may promote ventilation-induced lung injury(VILI) and worsen outcome in acute hypoxemic respiratory failure (AHRF). Different individual ventilatory variables have been proposed to predict clinical outcomes, with inconsistent results.Mechanical power (MP), a measure of the energy transfer rate from the ventilator to the respiratory system during mechanical ventilation, might provide solutions for this issue in the framework of predictive, preventive and personalized medicine(PPPM). We explored (1)the impact of ventilator-delivered MP normalized to well-aerated lung(MPWAL) on physio-anatomical and clinical responses to NIV in COVID-19-related AHRF and (2)the effect of prone position(PP) on MPWAL. We analyzed 216 noninvasively ventilated COVID-19 patients (108 patients receiving PP + NIV and 108 propensity score-matched patients receiving supine NIV) with moderate-to-severe(paO2/FiO2 ratio < 200) AHRF enrolled in the PRO-NIV controlled non-randomized study(ISRCTN23016116).Quantification of differentially aerated lung volumes by lung ultrasonography (LUS) was validated against CT scans. Respiratory parameters were hourly recorded, ABG were performed 1h after each postural change. Time-weighed average values of ventilatory variables, including MPWAL, and gas exchange parameters(paO2/FiO2 ratio, dead space indices) were calculated for each ventilatory session. LUS and circulating biomarkers were assessed daily. Compared with supine position, PP was associated with a 34% MPWAL reduction, attributable largely to an absolute MP reduction and secondly to an enhanced lung reaeration.Patients receiving a high MPWAL during the 1st 24h of NIV [MPWAL(day 1)] had higher 28-d NIV failure (HR = 4.33,95%CI:3.09 - 5.98) and death (HR = 5.17,95%CI: 3.01 - 7.35) risks than those receiving a low MPWAL(day 1).In Cox multivariate analyses, MPWAL(day 1) remained independently associated with 28-d NIV failure(HR = 1.68,95%CI:1.15-2.41) and death (HR = 1.69,95%CI:1.22-2.32).MPWAL(day 1) outperformed other power measures and ventilatory variables as predictor of 28-d NIV failure(AUROC = 0.89;95%CI:0.85-0.93) and death (AUROC = 0.89;95%CI:0.85-0.94).MPWAL(day 1) predicted also gas exchange, ultrasonographic and inflammatory biomarker responses, as markers of VILI, on linear multivariate analysis. In the framework of PPPM, early bedside MPWAL calculation may provide added value to predict response to NIV and guide subsequent therapeutic choices i.e. prone position adoption during NIV or upgrading to invasive ventilation, to reduce hazardous MPWAL delivery, prevent VILI progression and improve clinical outcomes in COVID-19-related AHRF. The online version contains supplementary material available at 10.1007/s13167-023-00325-5.

Software-Based Assessment of Well-Aerated Lung at CT for Quantification of Predicted Pulmonary Function in Resected NSCLC.

Background: To test the agreement between postoperative pulmonary function tests 12 months after surgery (mpo-PFTs) for non-small cell lung cancer (NSCLC) and predicted lung function based on the quantification of well-aerated lung (WAL) at staging CT (sCT). Methods: We included patients with NSCLC who underwent lobectomy or segmentectomy without a history of thoracic radiotherapy or chemotherapy treatment with the availability of PFTs at 12 months follow-up. Postoperative predictive (ppo) lung function was calculated using the resected lobe WAL (the lung volume between −950 and −750 HU) at sCT. The Spearman correlation coefficient (rho) and intraclass correlation coefficient (ICC) were used to the test the agreement between WAL ppo-PFTs and mpo-PFTs. Results: the study included 40 patients (68 years-old, IQR 62−74 years-old; 26/40, 65% males). The WAL ppo-forced expiratory volume in 1 s (FEV1) and the ppo-diffusing capacity of the lung for carbon monoxide (%DLCO) were significantly correlated with corresponding mpo-PFTs (rho = 0.842 and 0.717 respectively; p < 0.001). The agreement with the corresponding mpo-PFTs of WAL ppo-FEV1 was excellent (ICC 0.904), while it was good (ICC 0.770) for WAL ppo-%DLCO. Conclusions: WAL ppo-FEV1 and WAL ppo-%DLCO at sCT showed, respectively, excellent and good agreement with corresponding mpo-PFTs measured 12 months after surgery for NSCLC. WAL is an easy parameter obtained by staging CT that can be used to estimate post-resection lung function for patients with borderline pulmonary function undergoing lung surgery.

Well-Aerated Lung and Mean Lung Density Quantified by CT at Discharge to Predict Pulmonary Diffusion Function 5 Months after COVID-19.

The aim of this study was to explore the predictive values of quantitative CT indices of the total lung and lung lobe tissue at discharge for the pulmonary diffusion function of coronavirus disease 2019 (COVID-19) patients at 5 months after symptom onset. A total of 90 patients with moderate and severe COVID-19 underwent CT scans at discharge, and pulmonary function tests (PFTs) were performed 5 months after symptom onset. The differences in quantitative CT and PFT results between Group 1 (patients with abnormal diffusion function) and Group 2 (patients with normal diffusion function) were compared by the chi-square test, Fisher's exact test or Mann-Whitney U test. Univariate analysis, stepwise linear regression and logistic regression were used to determine the predictors of diffusion function in convalescent patients. A total of 37.80% (34/90) of patients presented diffusion dysfunction at 5 months after symptom onset. The mean lung density (MLD) of the total lung tissue in Group 1 was higher than that in Group 2, and the percentage of the well-aerated lung (WAL) tissue volume (WAL%) of Group 1 was lower than that of Group 2 (all p &lt; 0.05). Multiple stepwise linear regression identified only WAL and WAL% of the left upper lobe (LUL) as parameters that positively correlated with the percent of the predicted value of diffusion capacity of the lungs for carbon monoxide (WAL: p = 0.002; WAL%: p = 0.004), and multiple stepwise logistic regression identified MLD and MLDLUL as independent predictors of diffusion dysfunction (MLD: OR (95%CI): 1.011 (1.001, 1.02), p = 0.035; MLDLUL: OR (95%CI): 1.016 (1.004, 1.027), p = 0.008). At five months after symptom onset, more than one-third of moderate and severe COVID-19 patients presented with diffusion dysfunction. The well-aerated lung and mean lung density quantified by CT at discharge could be predictors of diffusion function in convalesce.

AI-SCoRE (artificial intelligence-SARS CoV2 risk evaluation): a fast, objective and fully automated platform to predict the outcome in COVID-19 patients

PurposeTo develop and validate an effective and user-friendly AI platform based on a few unbiased clinical variables integrated with advanced CT automatic analysis for COVID-19 patients’ risk stratification.Material and MethodsIn total, 1575 consecutive COVID-19 adults admitted to 16 hospitals during wave 1 (February 16-April 29, 2020), submitted to chest CT within 72 h from admission, were retrospectively enrolled.In total, 107 variables were initially collected; 64 extracted from CT. The outcome was survival.A rigorous AI model selection framework was adopted for models selection and automatic CT data extraction. Model performances were compared in terms of AUC. A web–mobile interface was developed using Microsoft PowerApps environment. The platform was externally validated on 213 COVID-19 adults prospectively enrolled during wave 2 (October 14-December 31, 2020).ResultsThe final cohort included 1125 patients (292 non-survivors, 26%) and 24 variables. Logistic showed the best performance on the complete set of variables (AUC = 0.839 ± 0.009) as in models including a limited set of 13 and 5 variables (AUC = 0.840 ± 0.0093 and AUC = 0.834 ± 0.007). For non-inferior performance, the 5 variables model (age, sex, saturation, well-aerated lung parenchyma and cardiothoracic vascular calcium) was selected as the final model and the extraction of CT-derived parameters was fully automatized. The fully automatic model showed AUC = 0.842 (95% CI: 0.816–0.867) on wave 1 and was used to build a 0–100 scale risk score (AI-SCoRE). The predictive performance was confirmed on wave 2 (AUC 0.808; 95% CI: 0.7402–0.8766).ConclusionsAI-SCoRE is an effective and reliable platform for automatic risk stratification of COVID-19 patients based on a few unbiased clinical data and CT automatic analysis.

COVID-19 pneumonia: Prediction of patient outcome by CT-based quantitative lung parenchyma analysis combined with laboratory parameters.

ObjectivesTo evaluate the prognostic value of fully automatic lung quantification based on spectral computed tomography (CT) and laboratory parameters for combined outcome prediction in COVID-19 pneumonia.MethodsCT images of 53 hospitalized COVID-19 patients including virtual monochromatic reconstructions at 40-140keV were analyzed using a fully automated software system. Quantitative CT (QCT) parameters including mean and percentiles of lung density, fibrosis index (FIBI-700, defined as the percentage of segmented lung voxels ≥-700 HU), quantification of ground-glass opacities and well-aerated lung areas were analyzed. QCT parameters were correlated to laboratory and patient outcome parameters (hospitalization, days on intensive care unit, invasive and non-invasive ventilation).ResultsBest correlations were found for laboratory parameters LDH (r = 0.54), CRP (r = 0.49), Procalcitonin (r = 0.37) and partial pressure of oxygen (r = 0.35) with the QCT parameter 75th percentile of lung density. LDH, Procalcitonin, 75th percentile of lung density and FIBI-700 were the strongest independent predictors of patients’ outcome in terms of days of invasive ventilation. The combination of LDH and Procalcitonin with either 75th percentile of lung density or FIBI-700 achieved a r2 of 0.84 and 1.0 as well as an area under the receiver operating characteristic curve (AUC) of 0.99 and 1.0 for the prediction of the need of invasive ventilation.ConclusionsQCT parameters in combination with laboratory parameters could deliver a feasible prognostic tool for the prediction of invasive ventilation in patients with COVID-19 pneumonia.

Predictive value of computed tomography for short-term mortality in patients with acute respiratory distress syndrome: a systematic review

The best available evidence and the predictive value of computed tomography (CT) findings for prognosis in patients with acute respiratory distress syndrome (ARDS) are unknown. We systematically searched three electronic databases (MEDLINE, CENTRAL, and ClinicalTrials.gov). A total of 410 patients from six observational studies were included in this systematic review. Of these, 143 patients (34.9%) died due to ARDS in short-term. As for CT grade, the CTs used ranged from 4- to 320-row. The index test included diffuse attenuations in one study, affected lung in one study, well-aerated lung region/predicted total lung capacity in one study, CT score in one study and high-resolution CT score in two studies. Considering the CT findings, pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were 62% (95% confidence interval [CI] 30–88%), 76% (95% CI 57–89%), 2.58 (95% CI 2.05–2.73), 0.50 (95% CI 0.21–0.79), and 5.16 (95% CI 2.59–3.46), respectively. This systematic review revealed that there were major differences in the definitions of CT findings, and that the integration of CT findings might not be adequate for predicting short-term mortality in ARDS. Standardisation of CT findings and accumulation of further studies by CT with unified standards are warranted.

A Soft Labeling Approach to Develop Automated Algorithms that Incorporate Uncertainty in Pulmonary Opacification on Chest CT using COVID-19 Pneumonia

Hard data labels for automated algorithm training are binary and cannot incorporate uncertainty between labels. We proposed and evaluated a soft labeling methodology to quantify opacification and percent well-aerated lung (%WAL) on chest CT, that considers uncertainty in segmenting pulmonary opacifications and reduces labeling burden. We retrospectively sourced 760 COVID-19 chest CT scans from five international centers between January and June 2020. We created pixel-wise labels for >27,000 axial slices that classify three pulmonary opacification patterns: pure ground-glass, crazy-paving, consolidation. We also quantified %WAL as the total area of lung without opacifications. Inter-user hard label variability was quantified using Shannon entropy (range=0-1.39, low-high entropy/variability). We incorporated a soft labeling and modeling cycle following an initial model with hard labels and compared performance using point-wise accuracy and intersection-over-union of opacity labels with ground-truth, and correlation with ground-truth %WAL. Hard labels annotated by 12 radiologists demonstrated large inter-user variability (3.37% of pixels achieved complete agreement). Our soft labeling approach increased point-wise accuracy from 60.0% to 84.3% (p=0.01) compared to hard labeling at predicting opacification type and area involvement. The soft label model accurately predicted %WAL (R=0.900) compared to the hard label model (R=0.856), but the improvement was not statistically significant (p=0.349). Our soft labeling approach increased accuracy for automated quantification and classification of pulmonary opacification on chest CT. Although we developed the model on COVID-19, our intent is broad application for pulmonary opacification contexts and to provide a foundation for future development using soft labeling methods.

Chest X-ray versus chest computed tomography for outcome prediction in hospitalized patients with COVID-19

The purpose of this study was to compare the prognostic value of chest X-ray (CXR) and chest computed tomography (CT) in a group of hospitalized patients with COVID-19. For this study, we retrospectively selected a cohort of 106 hospitalized patients with COVID-19 who underwent both CXR and chest CT at admission. For each patient, the pulmonary involvement was ranked by applying the Brixia score for CXR and the percentage of well-aerated lung (WAL) for CT. The Brixia score was assigned at admission (A-Brixia score) and during hospitalization. During hospitalization, only the highest score (H-Brixia score) was considered. At admission, the percentage of WAL (A-CT%WAL) was quantified using a dedicated software. On logistic regression analyses, H-Brixia score was the most effective radiological marker for predicting in-hospital mortality and invasive mechanical ventilation. Additionally, A-CT%WAL did not provide substantial advantages in the risk stratification of hospitalized patients with COVID-19 compared to A-Brixia score.

Sildenafil for treating patients with COVID-19 and perfusion mismatch: a pilot randomized trial

BackgroundSARS-CoV-2 seems to affect the regulation of pulmonary perfusion. Hypoperfusion in areas of well-aerated lung parenchyma results in a ventilation–perfusion mismatch that can be characterized using subtraction computed tomography angiography (sCTA). This study aims to evaluate the efficacy of oral sildenafil in treating COVID-19 inpatients showing perfusion abnormalities in sCTA.MethodsTriple-blinded, randomized, placebo-controlled trial was conducted in Chile in a tertiary-care hospital able to provide on-site sCTA scans and ventilatory support when needed between August 2020 and March 2021. In total, 82 eligible adults were admitted to the ED with RT-PCR-confirmed or highly probable SARS-COV-2 infection and sCTA performed within 24 h of admission showing perfusion abnormalities in areas of well-aerated lung parenchyma; 42 were excluded and 40 participants were enrolled and randomized (1:1 ratio) once hospitalized. The active intervention group received sildenafil (25 mg orally three times a day for seven days), and the control group received identical placebo capsules in the same way. Primary outcomes were differences in oxygenation parameters measured daily during follow-up (PaO2/FiO2 ratio and A-a gradient). Secondary outcomes included admission to the ICU, requirement of non-invasive ventilation, invasive mechanical ventilation (IMV), and mortality rates. Analysis was performed on an intention-to-treat basis.ResultsTotally, 40 participants were enrolled (20 in the placebo group and 20 in the sildenafil group); 33 [82.5%] were male; and median age was 57 [IQR 41–68] years. No significant differences in mean PaO2/FiO2 ratios and A-a gradients were found between groups (repeated-measures ANOVA p = 0.67 and p = 0.69). IMV was required in 4 patients who received placebo and none in the sildenafil arm (logrank p = 0.04). Patients in the sildenafil arm showed a significantly shorter median length of hospital stay than the placebo group (9 IQR 7–12 days vs. 12 IQR 9–21 days, p = 0.04).ConclusionsNo statistically significant differences were found in the oxygenation parameters. Sildenafil treatment could have a potential therapeutic role regarding the need for IMV in COVID-19 patients with specific perfusion patterns in sCTA. A large-scale study is needed to confirm these results.Trial Registration: Sildenafil for treating patients with COVID-19 and perfusion mismatch: a pilot randomized trial, NCT04489446, Registered 28 July 2020, https://clinicaltrials.gov/ct2/show/NCT04489446.

Total Lung and Lobar Quantitative Assessment Based on Paired Inspiratory-Expiratory Chest CT in Healthy Adults: Correlation with Pulmonary Ventilatory Function.

Objective: To provide the quantitative volumetric data of the total lung and lobes in inspiration and expiration from healthy adults, and to explore the value of paired inspiratory–expiratory chest CT scan in pulmonary ventilatory function and further explore the influence of each lobe on ventilation. Methods: A total of 65 adults (29 males and 36 females) with normal clinical pulmonary function test (PFT) and paired inspiratory–expiratory chest CT scan were retrospectively enrolled. The inspiratory and expiratory volumetric indexes of the total lung (TL) and 5 lobes (left upper lobe [LUL], left lower lobe [LLL], right upper lobe [RUL], right middle lobe [RML], and right lower lobe [RLL]) were obtained by Philips IntelliSpace Portal image postprocessing workstation, including inspiratory lung volume (LVin), expiratory lung volume (LVex), volume change (∆LV), and well-aerated lung volume (WAL, lung tissue with CT threshold between −950 and −750 HU in inspiratory scan). Spearman correlation analysis was used to explore the correlation between CT quantitative indexes of the total lung and ventilatory function indexes (including total lung capacity [TLC], residual volume [RV], and force vital capacity [FVC]). Multiple stepwise regression analysis was used to explore the influence of each lobe on ventilation. Results: At end-inspiratory phase, the LVin-TL was 4664.6 (4282.7, 5916.2) mL, the WALTL was 4173 (3639.6, 5250.9) mL; both showed excellent correlation with TLC (LVin-TL: r = 0.890, p < 0.001; WALTL: r = 0.879, p < 0.001). From multiple linear regression analysis with lobar CT indexes as variables, the LVin and WAL of these two lobes, LLL and RUL, showed a significant relationship with TLC. At end-expiratory phase, the LVex-TL was 2325.2 (1969.7, 2722.5) mL with good correlation with RV (r = 0.811, p < 0.001), of which the LVex of RUL and RML had a significant relationship with RV. For the volumetric change within breathing, the ∆LVTL was 2485.6 (2169.8, 3078.1) mL with good correlation with FVC (r = 0.719, p < 0.001), moreover, WALTL showed a better correlation with FVC (r = 0.817, p < 0.001) than that of ∆LVTL. Likewise, there was also a strong association between ∆LV, WAL of these two lobes (LLL and RUL), and FVC. Conclusions: The quantitative indexes derived from paired inspiratory–expiratory chest CT could reflect the clinical pulmonary ventilatory function, LLL, and RUL give greater impact on ventilation. Thus, the pulmonary functional evaluation needs to be more precise and not limited to the total lung level.

Visual and Quantitative Assessment of COVID-19 Pneumonia on Chest CT: The Relationship with Disease Severity and Clinical Findings.

Lungs are the primary organ involved in COVID-19, and the severity of pneumonia in COVID-19 patients is an important cause of morbidity and mortality. We aimed to evaluate the pneumonia severity through the visual and quantitative assessment on chest computed tomography (CT) in patients with coronavirus disease 2019 (COVID-19) and compare the CT findings with clinical and laboratory findings. We retrospectively evaluated adult COVID-19 patients who underwent chest CT along with theirclinical scores, laboratory findings, and length of hospital stay. Two independent radiologists visually evaluated the pneumonia severity on chest CT (VSQS). Quantitative CT (QCT) assessment was performed using a free DICOM viewer, and the percentage of the well-aerated lung (%WAL), high-attenuation areas (%HAA) at different threshold values, and mean lung attenuation (MLA) values were calculated. The relationship between CT scores and the clinical, laboratory data, and the length of hospital stay were evaluated in this cross-sectional study. The student's t-test and chi-square test were used to analyze the differences between the variables. The Pearson correlation test analyzed the correlation between the variables. The diagnostic performance of the variables was assessed using the receiver operating characteristic (ROC) analysis. The VSQS and QCT scores were significantly correlated with procalcitonin, d-dimer, ferritin, and C-reactive protein levels. Both VSQ and QCT scores were significantly correlated with the disease severity (p < 0.001). Among the QCT parameters, the %HAA-600 value showed the best correlation with the VSQS (r = 730, p < 0.001). VSQS and QCT scores had high sensitivity and specificity in distinguishing disease severity and predicting prolonged hospitalization. The VSQS and QCT scores can help manage the COVID-19 and predict the duration of the hospitalization.

Post-Acute Sequelae of COVID-19 Pneumonia: Six-month Chest CT Follow-up.

BackgroundThe long-term post acute pulmonary sequelae of COVID-19 remain unknown.PurposeTo evaluate lung injury in patients affected by COVID-19 pneumonia at six-month follow-up compared to baseline chest CT.MethodsFrom March 19th,2020 to May 24th,2020, patients with moderate to severe COVID-19 pneumonia and baseline Chest CT were prospectively enrolled at six-months follow-up. CT qualitative findings, semi-quantitative Lungs Severity Score (LSS) and well-aerated lung quantitative Chest CT (QCCT) were analyzed. Baseline LSS and QCCT performances in predicting fibrotic-like changes (reticular pattern and/or honeycombing) at six-month follow-up Chest CT were tested with receiver operating characteristic curves. Univariable and multivariable logistic regression analysis were used to test clinical and radiological features predictive of fibrotic-like changes. The multivariable analysis was performed with clinical parameters alone (clinical model), radiological parameters alone (radiological model) and the combination of clinical and radiological parameters (combined model).ResultsOne-hundred-eighteen patients, with both baseline and six-month follow-up Chest CT, were included in the study (62 female, mean age 65±12 years). At follow-up Chest CT, 85/118 (72%) patients showed fibrotic-like changes and 49/118 (42%) showed GGOs. Baseline LSS (>14), QCCT (≤3.75L and ≤80%) showed an excellent performance in predicting fibrotic-like changes at Chest CT follow-up. In the multivariable analysis, AUC was .89 (95%CI .77-.96) for the clinical model, .81 (95%CI .68-.9) for the radiological model and .92 (95%CI .81-.98)for the combined model.ConclusionAt six-month follow-up Chest CT, 72% of patients showed late sequelae, in particular fibrotic-like changes. Baseline LSS and QCCT of well-aerated lung showed an excellent performance in predicting fibrotic-like changes at six-month Chest CT (AUC>.88). Male sex, cough, lymphocytosis and QCCT well-aerated lung were significant predictors of fibrotic-like changes at six-month with an inverse correlation (AUC .92).See also the editorial by Wells and Devaraj.

Quantitative assessment of lung involvement on chest CT at admission: Impact on hypoxia and outcome in COVID-19 patients

BackgroundThe aim of this study was to quantify COVID-19 pneumonia features using CT performed at time of admission to emergency department in order to predict patients' hypoxia during the hospitalization and outcome. MethodsConsecutive chest CT performed in the emergency department between March 1st and April 7th 2020 for COVID-19 pneumonia were analyzed. The three features of pneumonia (GGO, semi-consolidation and consolidation) and the percentage of well-aerated lung were quantified using a HU threshold based software. ROC curves identified the optimal cut-off values of CT parameters to predict hypoxia worsening and hospital discharge. Multiple Cox proportional hazards regression was used to analyze the capability of CT quantitative features, demographic and clinical variables to predict the time to hospital discharge. ResultsSeventy-seven patients (median age 56-years-old, 51 men) with COVID-19 pneumonia at CT were enrolled. The quantitative features of COVID-19 pneumonia were not associated to age, sex and time-from-symptoms onset, whereas higher number of comorbidities was correlated to lower well-aerated parenchyma ratio (rho = −0.234, p = 0.04) and increased semi-consolidation ratio (rho = −0.303, p = 0.008).Well-aerated lung (≤57%), semi-consolidation (≥17%) and consolidation (≥9%) predicted worst hypoxemia during hospitalization, with moderate areas under curves (AUC 0.76, 0.75, 0.77, respectively). Multiple Cox regression identified younger age (p < 0.01), female sex (p < 0.001), longer time-from-symptoms onset (p = 0.049), semi-consolidation ≤17% (p < 0.01) and consolidation ≤13% (p = 0.03) as independent predictors of shorter time to hospital discharge. ConclusionQuantification of pneumonia features on admitting chest CT predicted hypoxia worsening during hospitalization and time to hospital discharge in COVID-19 patients.

Correlation between lung ultrasound and chest CT patterns with estimation of pulmonary burden in COVID-19 patients

PurposeThe capability of lung ultrasound (LUS) to distinguish the different pulmonary patterns of COVID-19 and quantify the disease burden compared to chest CT is still unclear. MethodsPCR-confirmed COVID-19 patients who underwent both LUS and chest CT at the Emergency Department were retrospectively analysed. In both modalities, twelve peripheral lung zones were identified and given a Severity Score basing on main lesion pattern. On CT scans the well-aerated lung volume (%WALV) was visually estimated. Per-patient and per-zone assessments of LUS classification performance taking CT findings as reference were performed, further revisioning the images in case of discordant results. Correlations between number of disease-positive lung zones, Severity Score and %WALV on both LUS and CT were assessed. The area under receiver operating characteristic curve (AUC) was calculated to determine LUS performance in detecting %WALV ≤ 70 %. ResultsThe study included 219 COVID-19 patients with abnormal chest CT. LUS correctly identified as positive 217 (99 %) patients, but per-zone analysis showed sensitivity = 75 % and specificity = 66 %. The revision of the 121 (55 %) cases with positive LUS and negative CT revealed COVID-compatible lesions in 42 (38 %) CT scans. Number of disease-positive zones, Severity Score and %WALV between LUS and CT showed moderate correlations. The AUCs for LUS Severity Score and number of LUS-positive zones did not differ in detecting %WALV ≤ 70 %. ConclusionLUS in COVID-19 is valuable for case identification but shows only moderate correlation with CT findings as for lesion patterns and severity quantification. The number of disease-positive lung zones in LUS alone was sufficient to discriminate relevant disease burden.

Computed Tomography Scan Evidence of Bronchospasm and Its Resolution During Computed Tomography Coronary Angiogram in a Child With Kawasaki Disease: A Case Report

Bronchospasm in children is common; however, due to its sudden nature, radiographic correlation is uncommon. We planned a computed tomography (CT) coronary angiogram for a 5-year-old child for evaluation of Kawasaki disease. The child started to desaturate during the CT scanning after intravenous contrast injection under conscious sedation. CT scan documented spasm of trachea and bronchi, as well as crowding of ribs and elevated diaphragm during the event. Repeat CT scan documented well-aerated lung fields. The development of acute bronchospasm under anesthesia results in definite changes in the CT scan of the thoracic cavity as evidenced by this incident.

A patient-specific approach for quantitative and automatic analysis of computed tomography images in lung disease: Application to COVID-19 patients.

PurposeQuantitative metrics in lung computed tomography (CT) images have been widely used, often without a clear connection with physiology. This work proposes a patient-independent model for the estimation of well-aerated volume of lungs in CT images (WAVE).MethodsA Gaussian fit, with mean (Mu.f) and width (Sigma.f) values, was applied to the lower CT histogram data points of the lung to provide the estimation of the well-aerated lung volume (WAVE.f). Independence from CT reconstruction parameters and respiratory cycle was analysed using healthy lung CT images and 4DCT acquisitions. The Gaussian metrics and first order radiomic features calculated for a third cohort of COVID-19 patients were compared with those relative to healthy lungs. Each lung was further segmented in 24 subregions and a new biomarker derived from Gaussian fit parameter Mu.f was proposed to represent the local density changes.ResultsWAVE.f resulted independent from the respiratory motion in 80% of the cases. Differences of 1%, 2% and up to 14% resulted comparing a moderate iterative strength and FBP algorithm, 1 and 3 mm of slice thickness and different reconstruction kernel. Healthy subjects were significantly different from COVID-19 patients for all the metrics calculated. Graphical representation of the local biomarker provides spatial and quantitative information in a single 2D picture.ConclusionsUnlike other metrics based on fixed histogram thresholds, this model is able to consider the inter- and intra-subject variability. In addition, it defines a local biomarker to quantify the severity of the disease, independently of the observer.

Sildenafil for the Treatment of Patients With COVID-19 and Perfusion Mismatch on Subtraction Computed Tomography Angiography: A Pilot, Placebo-Controlled Randomised Trial

Background: Severe gas exchange impairment can develop even in initial stages of COVID-19 pneumonia. SARS-CoV-2 seems to affect the regulation of pulmonary perfusion, through a renin-angiotensin system imbalance that occurs as a result of the virus binding to ACE2. Hypoperfusion to areas of well-aerated lung parenchyma results in a ventilation-perfusion mismatch that can be characterized using subtraction computed tomography angiography (sCTA). We aimed to investigate the potential efficacy and safety of oral sildenafil in the treatment of inpatients with COVID-19 that showed perfusion abnormalities in sCTA.Methods: This triple-blind, single-centre, placebo-controlled trial, was done at Hospital Naval Almirante Nef, Chile. We enrolled adult patients (>18 years of age) with clinically diagnosed, highly probable or confirmed COVID-19 infection, that were admitted for inpatient treatment, and with a sCTA carried out within 24 hours of admission that showed perfusion abnormalities in areas of well-aerated lung parenchyma. Patients were randomly assigned (1:1) to receive sildenafil (25 mg orally three times a day for seven days) or placebo. The primary endpoint was the change in the ratio between arterial oxygen partial pressure and fractional inspired oxygen (PaO2/FiO2 ratio), and a co-primary endpoint was the change in alveolar-arterial oxygen gradient (A-a gradient), both of which were calculated 1 hour after administration of the intervention on the first day, and on a daily basis during follow-up, using samples from an arterial line in all patients, and analysed using a two-way repeated-measures analysis of variance (ANOVA). Secondary endpoints including intensive care unit admission, the requirement of non-invasive mechanical ventilation or requirement of high-flow nasal cannula, initiation of mechanical ventilation and mortality rates were evaluated using survival analysis with the Kaplan-Meier method and log-rank statistic. The trial is now done. This trial is registered with ClinicalTrials.gov, NCT04489446.Findings: Between August 20, 2020 and March 31, 2021, we recruited 20 patients per group. No statistically significant differences in mean PaO2/FiO2 ratios (repeated measures ANOVA p=0·67) and A-a gradients (repeated measures ANOVA p=0·69) were found between study groups. Invasive mechanical ventilation had to be initiated in four patients, all in the placebo arm, showing a statistically significant difference with the sildenafil arm (logrank p=0·04). Patients allocated to the sildenafil arm also showed a significantly shorter median length of stay in the hospital when compared with the placebo group (9 IQR 7-12 days vs 12 IQR 9-21 days, p=0·04).Interpretation: Sildenafil treatment decreased the need for initiation of invasive mechanical ventilation and reduced the length of stay in the hospital. However, there were no statistically significant differences in the oxygenation parameter (PaO2/FiO2 ratio and A-a gradient). The improvement in clinically relevant outcomes, without significant differences in specific laboratory outcomes, warrants the conduction of larger trials using sildenafil for the treatment of COVID-19 patients with specific perfusion patterns in sCTA.Clinical Trial Registration Details: This trial is registered with ClinicalTrials.gov, NCT04489446.Funding Information: There were no external sources of funding for this investigation.Declaration of Interests: We declare no competing interests.Ethics Approval Statement: The study was approved by the ethics committee of Hospital Naval Almirante Nef, and by the ethics committee of Universidad Andres Bello, Chile. All patients provided written informed consent before randomisation.

Chest CT\u2013derived pulmonary artery enlargement at the admission predicts overall survival in COVID-19 patients: insight from 1461 consecutive patients in Italy

ObjectivesEnlarged main pulmonary artery diameter (MPAD) resulted to be associated with pulmonary hypertension and mortality in a non-COVID-19 setting. The aim was to investigate and validate the association between MPAD enlargement and overall survival in COVID-19 patients.MethodsThis is a cohort study on 1469 consecutive COVID-19 patients submitted to chest CT within 72 h from admission in seven tertiary level hospitals in Northern Italy, between March 1 and April 20, 2020. Derivation cohort (n = 761) included patients from the first three participating hospitals; validation cohort (n = 633) included patients from the remaining hospitals. CT images were centrally analyzed in a core-lab blinded to clinical data. The prognostic value of MPAD on overall survival was evaluated at adjusted and multivariable Cox’s regression analysis on the derivation cohort. The final multivariable model was tested on the validation cohort.ResultsIn the derivation cohort, the median age was 69 (IQR, 58–77) years and 537 (70.6%) were males. In the validation cohort, the median age was 69 (IQR, 59–77) years with 421 (66.5%) males. Enlarged MPAD (≥ 31 mm) was a predictor of mortality at adjusted (hazard ratio, HR [95%CI]: 1.741 [1.253–2.418], p < 0.001) and multivariable regression analysis (HR [95%CI]: 1.592 [1.154–2.196], p = 0.005), together with male gender, old age, high creatinine, low well-aerated lung volume, and high pneumonia extension (c-index [95%CI] = 0.826 [0.796–0.851]). Model discrimination was confirmed on the validation cohort (c-index [95%CI] = 0.789 [0.758–0.823]), also using CT measurements from a second reader (c-index [95%CI] = 0.790 [0.753;0.825]).ConclusionEnlarged MPAD (≥ 31 mm) at admitting chest CT is an independent predictor of mortality in COVID-19.Key Points•Enlargement of main pulmonary artery diameter at chest CT performed within 72 h from the admission was associated with a higher rate of in-hospital mortality in COVID-19 patients.•Enlargement of main pulmonary artery diameter (≥ 31 mm) was an independent predictor of death in COVID-19 patients at adjusted and multivariable regression analysis.•The combined evaluation of clinical findings, lung CT features, and main pulmonary artery diameter may be useful for risk stratification in COVID-19 patients.