This copy is for personal use only. To order printed copies, contact reprints@rsna.org I n P r e s s Summary StatementVisual and software-based quantification of well aerated lung parenchyma on admission chest CT were predictors of intensive care unit (ICU) admission or death in patients with pneumonia. Key Results� Patients with COVID-19 pneumonia at baseline chest CT who had ICU admission or who died had 4 or more lobes of the lung affected compared to patients without ICU admission or death (16% versus 6% of patients, p<.04).� After adjustment for patient demographics and clinical parameters, visually assessed well aerated lung parenchyma on admission on chest CT less than 73% was associated with ICU admission or death (OR 5.4, p<.001); software methods for lung quantification showed similar results. List of abbreviations SARS-CoV-2 = severe acute respiratory syndrome coronavirus 2; COVID-19 = coronavirus disease 19; RT-PCR = reverse-transcription polymerase chain reaction; WOG = worse outcome group; N-WOG = not-worse outcome group; %V-WAL = visual assessment of well aerated lung percentage; %S-WAL = software-based assessment of well aerated lung percentage; VOL-WAL = open-source software assessment of well aerated lung absolute volume; AT = adipose tissue. I n P r e s sAbstract Background: Computed tomography (CT) of patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) disease depicts the extent of lung involvement in COVID-19 pneumonia.Purpose: The aim of the study was to determine the value of quantification of the well-aerated lung obtained at baseline chest CT for determining prognosis in patients with COVID-19 pneumonia. Materials and Methods: Patients who underwent chest CT suspected for COVID-19 pneumonia at the emergency department admission between February 17 to March 10, 2020 were retrospectively analyzed. Patients with negative reverse-transcription polymerase chain reaction (RT-PCR) for SARS-CoV-2 in nasalpharyngeal swabs, negative chest CT, and incomplete clinical data were excluded. CT was analyzed for quantification of well aerated lung visually (%V-WAL) and by open-source software (%S-WAL and absolute volume, VOL-WAL). Clinical parameters included demographics, comorbidities, symptoms and symptom duration, oxygen saturation and laboratory values. Logistic regression was used to evaluate relationship between clinical parameters and CT metrics versus patient outcome (ICU admission/death vs. no ICU admission/ death). The area under the receiver operating characteristic curve (AUC) was calculated to determine model performance.Results: The study included 236 patients (females 59/123, 25%; median age, 68 years). A %V-WAL<73% (OR, 5.4; 95% CI, 2.7-10.8; P<0.001), %S-WAL<71% (OR, 3.8; 95% CI, 1.9-7.5; P<0.001), and VOL-WAL<2.9 L (OR, 2.6; 95% CI, 1.2-5.8; P<0.01) were predictors of ICU admission/death. In comparison with clinical model containing only clinical parameters (AUC, 0.83), all three quantitative models showed higher diagnostic performance (AUC 0.86 for all models). ...
There are no robust data on the real onset of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and spread in the prepandemic period worldwide. We investigated the presence of SARS-CoV-2 receptor-binding domain (RBD)–specific antibodies in blood samples of 959 asymptomatic individuals enrolled in a prospective lung cancer screening trial between September 2019 and March 2020 to track the date of onset, frequency, and temporal and geographic variations across the Italian regions. SARS-CoV-2 RBD-specific antibodies were detected in 111 of 959 (11.6%) individuals, starting from September 2019 (14%), with a cluster of positive cases (>30%) in the second week of February 2020 and the highest number (53.2%) in Lombardy. This study shows an unexpected very early circulation of SARS-CoV-2 among asymptomatic individuals in Italy several months before the first patient was identified, and clarifies the onset and spread of the coronavirus disease 2019 (COVID-19) pandemic. Finding SARS-CoV-2 antibodies in asymptomatic people before the COVID-19 outbreak in Italy may reshape the history of pandemic.
Objectives To evaluate the inter-rater agreement of chest X-ray (CXR) findings in coronavirus disease 2019 (COVID-19) and to determine the value of initial CXR along with demographic, clinical, and laboratory data at emergency department (ED) presentation for predicting mortality and the need for ventilatory support. Methods A total of 340 COVID-19 patients who underwent CXR in the ED setting (March 1–13, 2020) were retrospectively included. Two reviewers independently assessed CXR abnormalities, including ground-glass opacities (GGOs) and consolidation. Two scoring systems (Brixia score and percentage of lung involvement) were applied. Inter-rater agreement was assessed by weighted Cohen’s kappa (κ) or intraclass correlation coefficient (ICC). Predictors of death and respiratory support were identified by logistic or Poisson regression. Results GGO admixed with consolidation (n = 235, 69%) was the most common CXR finding. The inter-rater agreement was almost perfect for type of parenchymal opacity (κ = 0.90), Brixia score (ICC = 0.91), and percentage of lung involvement (ICC = 0.95). The Brixia score (OR: 1.19; 95% CI: 1.06, 1.34; p = 0.003), age (OR: 1.16; 95% CI: 1.11, 1.22; p < 0.001), PaO2/FiO2 ratio (OR: 0.99; 95% CI: 0.98, 1; p = 0.002), and cardiovascular diseases (OR: 3.21; 95% CI: 1.28, 8.39; p = 0.014) predicted death. Percentage of lung involvement (OR: 1.02; 95% CI: 1.01, 1.03; p = 0.001) and PaO2/FiO2 ratio (OR: 0.99; 95% CI: 0.99, 1.00; p < 0.001) were significant predictors of the need for ventilatory support. Conclusions CXR is a reproducible tool for assessing COVID-19 and integrates with patient history, PaO2/FiO2 ratio, and SpO2 values to early predict mortality and the need for ventilatory support. Key Points • Chest X-ray is a reproducible tool for assessing COVID-19 pneumonia. • The Brixia score and percentage of lung involvement on chest X-ray integrate with patient history, PaO2/FIO2ratio, and SpO2values to early predict mortality and the need for ventilatory support in COVID-19 patients presenting to the emergency department.
Background: Lung ultrasound (LUS) is an accurate, safe, and cheap tool assisting in the diagnosis of several acute respiratory diseases. The diagnostic value of LUS in the workup of coronavirus disease-19 (COVID-19) in the hospital setting is still uncertain. Objectives: The aim of this observational study was to explore correlations of the LUS appearance of COVID-19-related pneumonia with CT findings. Methods: Twenty-six patients (14 males, age 64 ± 16 years) urgently hospitalized for COVID-19 pneumonia, who underwent chest CT and bedside LUS on the day of admission, were enrolled in this observational study. CT images were reviewed by expert chest radiologists, who calculated a visual CT score based on extension and distribution of ground-glass opacities and consolidations. LUS was performed by clinicians with certified competency in thoracic ultrasonography, blind to CT findings, following a systematic approach recommended by ultrasound guidelines. LUS score was calculated according to presence, distribution, and severity of abnormalities. Results: All participants had CT findings suggestive of bilateral COVID-19 pneumonia, with an average visual scoring of 43 ± 24%. LUS identified 4 different possible abnormalities, with bilateral distribution (average LUS score 15 ± 5): focal areas of nonconfluent B lines, diffuse confluent B lines, small subpleural microconsolidations with pleural line irregularities, and large parenchymal consolidations with air bronchograms. LUS score was significantly correlated with CT visual scoring (r = 0.65, p < 0.001) and oxygen saturation in room air (r =-0.66, p < 0.001). Conclusion: When integrated with clinical data, LUS could represent a valid diagnostic aid in patients with suspect COVID-19 pneumonia, which reflects CT findings.
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.
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. Supplementary Information The online version contains supplementary material available at 10.1007/s10140-020-01867-1.
Introduction:for the management of patients referred to respiratory triage during the early stages of the SARS-CoV-2 pandemic, either chest radiograph (CXR) or computed tomography (CT) were used as first-line diagnostic tools. The aim of this study was to compare the impact on triage, diagnosis and prognosis of patients with suspected COVID-19 when clinical decisions are derived from reconstructed CXR or from CT.Methods:we reconstructed CXR (r-CXR) from high-resolution CT (HRCT) scan. Five clinical observers independently reviewed clinical charts of 300 subjects with suspected COVID-19 pneumonia, integrated with either r-CXR 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 r-CXR 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 r-CXR-based protocol and good (Kendall's W=0.654; p<0.001) by CT-based protocol. NPV assisted by r-CXR (31.4%) was lower than that of HRCT (77.9%). In case of indeterminate or typical radiological appearence for COVID-19 pneumonia, extent of disease on r-CXR or HRCT were the only two imaging variables that were similarly linked to mortality by adjusted multivariable modelsConclusions:the present findings suggest that clinical triage is safely assisted by CXR. An integrated algorithm using first-line CXR and contingent use of HRCT can help optimise management and prognostication of COVID-19.
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