Are Interstitial Lung Abnormalities a Prognostic Factor of Worse Outcome in COVID-19 Pneumonia?

PURPOSE: To assess the association between interstitial lung abnormalities (ILAs) and worse outcome in patients affected by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) disease (COVID-19)-related pneumonia. MATERIALS AND METHODS: The study included patients older than 18 years, who were admitted at the emergency department between February 29 and April 30, 2020 with findings of COVID-19 pneumonia at chest computed tomography (CT), with positive reverse-transcription polymerase chain reaction nasal-pharyngeal swab for SARS-CoV-2, and with the availability of prepandemic chest CT. Prepandemic CTs were reviewed for the presence of ILAs, categorized as fibrotic in cases with associated architectural distortion, bronchiectasis, or honeycombing. Worse outcome was defined as intensive care unit (ICU) admission or death. Cox proportional hazards regression analysis was used to test the association between ICU admission/death and preexisting ILAs. RESULTS: The study included 147 patients (median age 73 y old; 95% CIs: 71-76-y old; 29% females). On prepandemic CTs, ILA were identified in 33/147 (22%) of the patients, 63% of which were fibrotic ILAs. Fibrotic ILAs were associated with higher risk of ICU admission or death in patients with COVID-19 pneumonia (hazard ratios: 2.73, 95% CIs: 1.50-4.97, P =0.001). CONCLUSIONS: In patients affected by COVID-19 pneumonia, preexisting fibrotic ILAs were an independent predictor of worse prognosis, with a 2.7 times increased risk of ICU admission or death. Chest CT scans obtained before the diagnosis of COVID-19 pneumonia should be carefully reviewed for the presence and characterization of ILAs.

Different Lung Parenchyma Quantification Using Dissimilar Segmentation Software: A Multi-Center Study for COVID-19 Patients.

BACKGROUND: Chest Computed Tomography (CT) imaging has played a central role in the diagnosis of interstitial pneumonia in patients affected by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and can be used to obtain the extent of lung involvement in COVID-19 pneumonia patients either qualitatively, via visual inspection, or quantitatively, via AI-based software. This study aims to compare the qualitative/quantitative pathological lung extension data on COVID-19 patients. Secondly, the quantitative data obtained were compared to verify their concordance since they were derived from three different lung segmentation software. METHODS: This double-center study includes a total of 120 COVID-19 patients (60 from each center) with positive reverse-transcription polymerase chain reaction (RT-PCR) who underwent a chest CT scan from November 2020 to February 2021. CT scans were analyzed retrospectively and independently in each center. Specifically, CT images were examined manually by two different and experienced radiologists for each center, providing the qualitative extent score of lung involvement, whereas the quantitative analysis was performed by one trained radiographer for each center using three different software: 3DSlicer, CT Lung Density Analysis, and CT Pulmo 3D. RESULTS: The agreement between radiologists for visual estimation of pneumonia at CT can be defined as good (ICC 0.79, 95% CI 0.73-0.84). The statistical tests show that 3DSlicer overestimates the measures assessed; however, ICC index returns a value of 0.92 (CI 0.90-0.94), indicating excellent reliability within the three software employed. ICC was also performed between each single software and the median of the visual score provided by the radiologists. This statistical analysis underlines that the best agreement is between 3D Slicer "LungCTAnalyzer" and the median of the visual score (0.75 with a CI 0.67-82 and with a median value of 22% of disease extension for the software and 25% for the visual values). CONCLUSIONS: This study provides for the first time a direct comparison between the actual gold standard, which is represented by the qualitative information described by radiologists, and novel quantitative AI-based techniques, here represented by three different commonly used lung segmentation software, underlying the importance of these specific values that in the future could be implemented as consistent prognostic and clinical course parameters.

Different Lung Parenchyma Quantification Using Dissimilar Segmentation Software: A Multi-Center Study for COVID-19 Patients

Background: Chest Computed Tomography (CT) imaging has played a central role in the diagnosis of interstitial pneumonia in patients affected by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and can be used to obtain the extent of lung involvement in COVID-19 pneumonia patients either qualitatively, via visual inspection, or quantitatively, via AI-based software. This study aims to compare the qualitative/quantitative pathological lung extension data on COVID-19 patients. Secondly, the quantitative data obtained were compared to verify their concordance since they were derived from three different lung segmentation software. Methods: This double-center study includes a total of 120 COVID-19 patients (60 from each center) with positive reverse-transcription polymerase chain reaction (RT-PCR) who underwent a chest CT scan from November 2020 to February 2021. CT scans were analyzed retrospectively and independently in each center. Specifically, CT images were examined manually by two different and experienced radiologists for each center, providing the qualitative extent score of lung involvement, whereas the quantitative analysis was performed by one trained radiographer for each center using three different software: 3DSlicer, CT Lung Density Analysis, and CT Pulmo 3D. Results: The agreement between radiologists for visual estimation of pneumonia at CT can be defined as good (ICC 0.79, 95% CI 0.73–0.84). The statistical tests show that 3DSlicer overestimates the measures assessed;however, ICC index returns a value of 0.92 (CI 0.90–0.94), indicating excellent reliability within the three software employed. ICC was also performed between each single software and the median of the visual score provided by the radiologists. This statistical analysis underlines that the best agreement is between 3D Slicer 'LungCTAnalyzer';and the median of the visual score (0.75 with a CI 0.67–82 and with a median value of 22% of disease extension for the software and 25% for the visual values). Conclusions: This study provides for the first time a direct comparison between the actual gold standard, which is represented by the qualitative information described by radiologists, and novel quantitative AI-based techniques, here represented by three different commonly used lung segmentation software, underlying the importance of these specific values that in the future could be implemented as consistent prognostic and clinical course parameters.

Chest radiography or computed tomography for COVID-19 pneumonia? Comparative study in a simulated triage setting.

INTRODUCTION: For the management of patients referred to respiratory triage during the early stages of the severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) pandemic, either chest radiography or computed tomography (CT) were used as first-line diagnostic tools. The aim of this study was to compare the impact on the triage, diagnosis and prognosis of patients with suspected COVID-19 when clinical decisions are derived from reconstructed chest radiography or from CT. METHODS: We reconstructed chest radiographs from high-resolution CT (HRCT) scans. Five clinical observers independently reviewed clinical charts of 300 subjects with suspected COVID-19 pneumonia, integrated with either a reconstructed chest radiography or HRCT report in two consecutive blinded and randomised sessions: clinical decisions were recorded for each session. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and prognostic value were compared between reconstructed chest radiography and HRCT. The best radiological integration was also examined to develop an optimised respiratory triage algorithm. RESULTS: Interobserver agreement was fair (Kendall's W=0.365, p<0.001) by the reconstructed chest radiography-based protocol and good (Kendall's W=0.654, p<0.001) by the CT-based protocol. NPV assisted by reconstructed chest radiography (31.4%) was lower than that of HRCT (77.9%). In case of indeterminate or typical radiological appearance for COVID-19 pneumonia, extent of disease on reconstructed chest radiography or HRCT were the only two imaging variables that were similarly linked to mortality by adjusted multivariable models CONCLUSIONS: The present findings suggest that clinical triage is safely assisted by chest radiography. An integrated algorithm using first-line chest radiography and contingent use of HRCT can help optimise management and prognostication of COVID-19.

Prediction of 28-day mortality in critically ill patients with COVID-19: Development and internal validation of a clinical prediction model.

BACKGROUND: COVID-19 pandemic has rapidly required a high demand of hospitalization and an increased number of intensive care units (ICUs) admission. Therefore, it became mandatory to develop prognostic models to evaluate critical COVID-19 patients. MATERIALS AND METHODS: We retrospectively evaluate a cohort of consecutive COVID-19 critically ill patients admitted to ICU with a confirmed diagnosis of SARS-CoV-2 pneumonia. A multivariable Cox regression model including demographic, clinical and laboratory findings was developed to assess the predictive value of these variables. Internal validation was performed using the bootstrap resampling technique. The model's discriminatory ability was assessed with Harrell's C-statistic and the goodness-of-fit was evaluated with calibration plot. RESULTS: 242 patients were included [median age, 64 years (56-71 IQR), 196 (81%) males]. Hypertension was the most common comorbidity (46.7%), followed by diabetes (15.3%) and heart disease (14.5%). Eighty-five patients (35.1%) died within 28 days after ICU admission and the median time from ICU admission to death was 11 days (IQR 6-18). In multivariable model after internal validation, age, obesity, procaltitonin, SOFA score and PaO2/FiO2 resulted as independent predictors of 28-day mortality. The C-statistic of the model showed a very good discriminatory capacity (0.82). CONCLUSIONS: We present the results of a multivariable prediction model for mortality of critically ill COVID-19 patients admitted to ICU. After adjustment for other factors, age, obesity, procalcitonin, SOFA and PaO2/FiO2 were independently associated with 28-day mortality in critically ill COVID-19 patients. The calibration plot revealed good agreements between the observed and expected probability of death.

Qualitative and quantitative chest CT parameters as predictors of specific mortality in COVID-19 patients.

PURPOSE: To test the association between death and both qualitative and quantitative CT parameters obtained visually and by software in coronavirus disease (COVID-19) early outbreak. METHODS: The study analyzed retrospectively patients underwent chest CT at hospital admission for COVID-19 pneumonia suspicion, between February 21 and March 6, 2020. CT was performed in case of hypoxemia or moderate-to-severe dyspnea. CT scans were analyzed for quantitative and qualitative features obtained visually and by software. Cox proportional hazards regression analysis examined the association between variables and overall survival (OS). Three models were built for stratification of mortality risk: clinical, clinical/visual CT evaluation, and clinical/software-based CT assessment. AUC for each model was used to assess performance in predicting death. RESULTS: The study included 248 patients (70% males, median age 68 years). Death occurred in 78/248 (32%) patients. Visual pneumonia extent > 40% (HR 2.15, 95% CI 1.2-3.85, P = 0.01), %high attenuation area - 700 HU > 35% (HR 2.17, 95% CI 1.2-3.94, P = 0.01), exudative consolidations (HR 2.85-2.93, 95% CI 1.61-5.05/1.66-5.16, P < 0.001), visual CAC score > 1 (HR 2.76-3.32, 95% CI 1.4-5.45/1.71-6.46, P < 0.01/P < 0.001), and CT classified as COVID-19 and other disease (HR 1.92-2.03, 95% CI 1.01-3.67/1.06-3.9, P = 0.04/P = 0.03) were significantly associated with shorter OS. Models including CT parameters (AUC 0.911-0.913, 95% CI 0.873-0.95/0.875-0.952) were better predictors of death as compared to clinical model (AUC 0.869, 95% CI 0.816-0.922; P = 0.04 for both models). CONCLUSIONS: In COVID-19 patients, qualitative and quantitative chest CT parameters obtained visually or by software are predictors of mortality. Predictive models including CT metrics were better predictors of death in comparison to clinical model.

Comparison of admission chest computed tomography and lung ultrasound performance for diagnosis of COVID-19 pneumonia in populations with different disease prevalence.

PURPOSE: Chest computed tomography (CT) is considered a reliable imaging tool for COVID-19 pneumonia diagnosis, while lung ultrasound (LUS) has emerged as a potential alternative to characterize lung involvement. The aim of the study was to compare diagnostic performance of admission chest CT and LUS for the diagnosis of COVID-19. METHODS: We included patients admitted to emergency department between February 21-March 6, 2020 (high prevalence group, HP) and between March 30-April 13, 2020 (moderate prevalence group, MP) undergoing LUS and chest CT within 12 h. Chest CT was considered positive in case of "indeterminate"/"typical" pattern for COVID-19 by RSNA classification system. At LUS, thickened pleural line with ≥ three B-lines at least in one zone of the 12 explored was considered positive. Sensitivity, specificity, PPV, NPV, and AUC were calculated for CT and LUS against real-time reverse transcriptase polymerase chain reaction (RT-PCR) and serology as reference standard. RESULTS: The study included 486 patients (males 61 %; median age, 70 years): 247 patients in HP (COVID-19 prevalence 94 %) and 239 patients in MP (COVID-19 prevalence 45 %). In HP and MP respectively, sensitivity, specificity, PPV, and NPV were 90-95 %, 43-69 %, 96-72 %, 20-95 % for CT and 94-93 %, 7-31 %, 94-52 %, 7-83 % for LUS. CT demonstrated better performance than LUS in diagnosis of COVID-19, both in HP (AUC 0.75 vs 0.51; P < 0.001) and MP (AUC 0.85 vs 0.62; P < 0.001). CONCLUSIONS: Admission chest CT shows better performance than LUS for COVID-19 diagnosis, at varying disease prevalence. LUS is highly sensitive, but not specific for COVID-19.

COVID-19 outbreak in Italy: Clinical-radiological presentation and outcome in three oncologic patients.

We present three patients affected by pulmonary squamous cell carcinoma, metastatic esophageal cancer and advanced non-Hodgkin lymphoma, who incurred in coronavirus 2019 (COVID-19) infection during the early phase of epidemic wave in Italy. All patients presented with fever. Social contact with subject positive for COVID-19 was declared in only one of the three cases. In all cases, laboratory findings showed lymphopenia and elevated C-reactive protein (CRP). Chest x-ray and computed tomography showed bilateral ground-glass opacities, shadowing, interstitial abnormalities, and "crazy paving" pattern which evolved with superimposition of consolidations in one patient. All patients received antiviral therapy based on ritonavir and lopinavir, associated with hydroxychloroquine. Despite treatment, two patients with advanced cancers died after 39 and 17 days of hospitalization, while the patient with lung cancer was dismissed at home, in good conditions.

Well-aerated Lung on Admitting Chest CT to Predict Adverse Outcome in COVID-19 Pneumonia.

Background CT of patients with severe acute respiratory syndrome coronavirus 2 disease depicts the extent of lung involvement in coronavirus disease 2019 (COVID-19) pneumonia. Purpose To determine the value of quantification of the well-aerated lung (WAL) obtained at admission chest CT to determine prognosis in patients with COVID-19 pneumonia. Materials and Methods Imaging of patients admitted at the emergency department between February 17 and March 10, 2020 who underwent chest CT were retrospectively analyzed. Patients with negative results of reverse-transcription polymerase chain reaction for severe acute respiratory syndrome coronavirus 2 at nasal-pharyngeal swabbing, negative chest CT findings, and incomplete clinical data were excluded. CT images were analyzed for quantification of WAL visually (%V-WAL), with open-source software (%S-WAL), and with absolute volume (VOL-WAL). Clinical parameters included patient characteristics, comorbidities, symptom type and duration, oxygen saturation, and laboratory values. Logistic regression was used to evaluate the relationship between clinical parameters and CT metrics versus patient outcome (intensive care unit [ICU] admission or death vs no ICU admission or death). The area under the receiver operating characteristic curve (AUC) was calculated to determine model performance. Results The study included 236 patients (59 of 123 [25%] were female; median age, 68 years). A %V-WAL less than 73% (odds ratio [OR], 5.4; 95% confidence interval [CI]: 2.7, 10.8; P < .001), %S-WAL less than 71% (OR, 3.8; 95% CI: 1.9, 7.5; P < .001), and VOL-WAL less than 2.9 L (OR, 2.6; 95% CI: 1.2, 5.8; P < .01) were predictors of ICU admission or death. In comparison with clinical models containing only clinical parameters (AUC = 0.83), all three quantitative models showed better diagnostic performance (AUC = 0.86 for all models). The models containing %V-WAL less than 73% and VOL-WAL less than 2.9 L were superior in terms of performance as compared with the models containing only clinical parameters (P = .04 for both models). Conclusion In patients with confirmed coronavirus disease 2019 pneumonia, visual or software quantification of the extent of CT lung abnormality were predictors of intensive care unit admission or death. © RSNA, 2020 Online supplemental material is available for this article.