Lung ultrasonography versus chest CT in COVID-19 pneumonia: a two-centered retrospective comparison study from China.
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ABSTRACT:
SUBMITTER: Yang Y
PROVIDER: S-EPMC7246293 | biostudies-literature | 2020 Sep
REPOSITORIES: biostudies-literature
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Lung ultrasonography versus chest CT in COVID-19 pneumonia: a two-centered retrospective comparison study from China.
Yang Yong Y Huang Yi Y Gao Feng F Yuan Lijun L Wang Zhen Z
Intensive care medicine 20200525 9
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Project description:ObjectivesTo compare the pulmonary chest CT findings of patients with COVID-19 pneumonia with those with other types of viral pneumonia.MethodsThis retrospective review includes 154 patients with RT-PCR-confirmed COVID-19 pneumonia diagnosed between February 11 and 20, 2020, and 100 patients with other types of viral pneumonia diagnosed between April 2011 and December 2020 from two hospitals. High-resolution CT (HRCT) of the chest was performed. Data on location, distribution, attenuation, maximum lesion range, lobe involvement, number of lesions, air bronchogram signs, Hilar and mediastinal lymph node enlargement, and pleural effusion were collected. Associations between imaging characteristics and COVID-19 pneumonia were analyzed with univariate and multivariate logistic regression models.ResultsA peripheral distribution was associated with a 13.04-fold risk of COVID-19 pneumonia, compared with a diffuse distribution. A maximum lesion range > 10 cm was associated with a 9.75-fold risk of COVID-19 pneumonia, compared with a maximum lesion range ≤ 5 cm, and the involvement of 5 lobes was associated with an 8.45-fold risk of COVID-19 pneumonia, compared with a maximum lesion range ≤ 2. No pleural effusion was associated with a 3.58-fold risk of COVID-19 pneumonia compared with the presence of pleural effusion. Hilar and mediastinal lymph node enlargement was associated with a 2.79-fold risk of COVID-19 pneumonia.ConclusionA peripheral distribution, a lesion range > 10 cm, involvement of 5 lobes, presence of hilar and mediastinal lymph node enlargement, and no pleural effusion were significantly associated with 2019-novel coronavirus pneumonia.Key points• A peripheral distribution, a lesion range > 10 cm, involvement of 5 lobes, presence of hilar and mediastinal lymph node enlargement, and no pleural effusion were significantly associated with COVID-19 compared with other types of viral pneumonia.
| S-EPMC7214022 | biostudies-literature
Project description:PurposeThe purpose of our study was to determine the usability of lung ultrasonography (LUS) in the diagnosis of COVID-19, and to match the morphological features of lesions detected on computed tomography (CT) with the findings observed on LUS.MethodsSixty patients with COVID-19 were included in this prospective study. Patients were examined by radiology and anesthesia clinic specialists for a visual CT score. A LUS 12-zone ultrasonography protocol was applied by the investigator blinded to the CT and PCR test results. The characteristics of abnormal findings and the relationship of lesions to the pleura and the distance to the pleura were investigated.ResultsForty-five males and 25 females evaluated within the scope of the study had an average age of 61.2 ± 15.3 years. The total CT score was calculated as 14.3 ± 5.3, and the LUS score was found to be 19.9 ± 7.6. There was a statistically significant positive correlation between the measured LUS and CT scores (r = 0.857, p < 0.001). The mean distance of these lesions to the pleura was 5.2 ± 1.76 cm. LUS findings in 51 areas corresponded to non-pleural lesions on CT. There was a negative correlation between the measured distance to the pleura and the LUS scores (p < 0.001, r = - 0.708).ConclusionThe results of this study showed that the correlation between CT and LUS findings may be used in the diagnosis of COVID-19 pneumonia, although there are some limitations. ClinicalTrials.gov identifier: NCT04719234.
| S-EPMC7908963 | biostudies-literature
Project description:Background Double lumen tube (DLT) and single lumen tube (SLT) are two common endotracheal tube (ETT) types in esophageal cancer surgery. Evidence of the relationship between two ETT types and postoperative pneumonia (PP) remains unclear. We aimed to determine the association between two types of ETT (DLT and SLT) and PP and assess the perioperative risk-related parameters that affect PP. Methods This study included 680 patients who underwent esophageal cancer surgery from January 01, 2010 through December 31, 2020. The primary outcome was PP, and the secondary outcome was perioperative risk-related parameters that affect PP. The independent variable was the type of ETT: DLT or SLT. The dependent variable was PP. To determine the relationship between variables and PP, univariate and multivariate analyses were performed. The covariables included baseline demographic characteristics, comorbidity disease, neoadjuvant chemotherapy, tumor location, laboratory parameters, intraoperative related variables. Results In all patients, the incidence of postoperative pneumonia in esophagectomy was 32.77% (36.90% in DLT group and 26.38% in SLT group). After adjusting for potential risk factors, we found that using an SLT in esophagectomy was associated with lower risk of postoperative pneumonia compared to using a DLT (Odd ratio = 0.41, 95% confidence interval (CI): 0.22, 0.77, p = 0.0057). Besides DLT, smoking history, combined intravenous and inhalation anesthesia (CIIA) and vasoactive drug use were all significant and independent risk factors for postoperative pneumonia in esophagectomy. These results remained stable and reliable after subgroup analysis. Conclusions During esophagectomy, there is significant association between the type of ETT (DLT or SLT) and PP. Patients who were intubated with a single lumen tube may have a lower rate of postoperative pneumonia than those who were intubated with a double lumen tube. This finding requires verification in follow-up studies. Supplementary Information The online version contains supplementary material available at 10.1186/s12871-023-02252-4.
| S-EPMC10478392 | biostudies-literature
Project description:BackgroundDark-field chest radiography allows the assessment of the structural integrity of the alveoli by exploiting the wave properties of x-rays.PurposeTo compare the qualitative and quantitative features of dark-field chest radiography in patients with COVID-19 pneumonia with conventional CT imaging.Materials and methodsIn this prospective study conducted from May 2020 to December 2020, patients aged at least 18 years who underwent chest CT for clinically suspected COVID-19 infection were screened for participation. Inclusion criteria were a CO-RADS score ≥4, the ability to consent to the procedure and to stand upright without help. Participants were examined with a clinical dark-field chest radiography prototype. For comparison, a healthy control cohort of 40 subjects was evaluated. Using Spearman's correlation coefficient, correlation was tested between dark-field coefficient and CT-based COVID-19 index and visual total CT score as well as between the visual total dark-field score and the visual total CT score.ResultsA total of 98 participants [mean age 58 ± 14 (standard deviation) years; 59 men] were studied. The areas of signal intensity reduction observed in dark-field images showed a strong correlation with infiltrates identified on CT scans. The dark-field coefficient had a negative correlation with both the quantitative CT-based COVID-19 index (r = -.34, p = .001) and the overall CT score used for visual grading of COVID-19 severity (r = -.44, p < .001). The total visual dark-field score for the presence of COVID-19 was positively correlated to the total CT score for visual COVID-19 severity grading (r = .85, p < .001).ConclusionCOVID-19 pneumonia-induced signal intensity losses in dark-field chest radiographs are consistent with CT-based findings, showing the technique's potential for COVID-19 assessment.
| S-EPMC11772474 | biostudies-literature
Project description:Background:Pediatric community acquired pneumonia (pCAP) is a major public health and economic problem with a considerable impact on morbidity and mortality in children. Recently many studies and meta-analyses have shown promising results on the accuracy of lung ultrasonography (LU) in diagnosing pneumonia and potentially replacing chest radiography (CR) in pediatric population. However, previous studies establishing the accuracy of LU often used CR as reference standard and took into account different clinical settings all together. To make a more objective and specific analysis, we performed a systematic review and meta-analysis to compare the diagnostic accuracy of LU and CR for pCAP in the emergency department (ED) setting. Methods:A literature search of PubMed and Embase databases up to December 2018 was conducted. Pooled sensitivity, specificity, positive and negative likelihood ratio, and diagnostic odds ratio of LU and CR were calculated, and summary receiver operating characteristic (SROC) curves were drawn. Results:A total of six studies, which included 575 pCAPs from 701 patients, were finally analyzed. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio and diagnostic odds ratios of LU were 96.7%, 87.3%, 8.10, 0.05, 256.68, while they were 89.7%, 93.7%, 9.97, 0.12, 175.07 for CR, respectively. The area under the SROC curves in diagnosing pCAP in the ED setting were 0.99 [95% confidence interval (CI), 0.98-1.00] and 0.97 (95% CI, 0.95-1.00) for LU and CR, respectively. Conclusions:Our meta-analysis suggests that LU is an accurate tool in the diagnosis of pCAP in the ED setting with a superior sensitivity over CR.
| S-EPMC6987984 | biostudies-literature
Project description:Online supplemental material is available for this article.
| S-EPMC9131172 | biostudies-literature
Project description:PurposeComparison of deep learning algorithm, radiomics and subjective assessment of chest CT for predicting outcome (death or recovery) and intensive care unit (ICU) admission in patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection.MethodsThe multicenter, ethical committee-approved, retrospective study included non-contrast-enhanced chest CT of 221 SARS-CoV-2 positive patients from Italy (n = 196 patients; mean age 64 ± 16 years) and Denmark (n = 25; mean age 69 ± 13 years). A thoracic radiologist graded presence, type and extent of pulmonary opacities and severity of motion artifacts in each lung lobe on all chest CTs. Thin-section CT images were processed with CT Pneumonia Analysis Prototype (Siemens Healthineers) which yielded segmentation masks from a deep learning (DL) algorithm to derive features of lung abnormalities such as opacity scores, mean HU, as well as volume and percentage of all-attenuation and high-attenuation (opacities >-200 HU) opacities. Separately, whole lung radiomics were obtained for all CT exams. Analysis of variance and multiple logistic regression were performed for data analysis.ResultsModerate to severe respiratory motion artifacts affected nearly one-quarter of chest CTs in patients. Subjective severity assessment, DL-based features and radiomics predicted patient outcome (AUC 0.76 vs AUC 0.88 vs AUC 0.83) and need for ICU admission (AUC 0.77 vs AUC 0.0.80 vs 0.82). Excluding chest CT with motion artifacts, the performance of DL-based and radiomics features improve for predicting ICU admission.ConclusionDL-based and radiomics features of pulmonary opacities from chest CT were superior to subjective assessment for differentiating patients with favorable and adverse outcomes.
| S-EPMC8247202 | biostudies-literature
Project description:Lung ultrasonography (LUS) is being increasingly utilized in emergency and critical settings. We performed a systematic review of the current literature to compare the accuracy of LUS and chest radiography (CR) for the diagnosis of adult community-acquired pneumonia (CAP). We searched in Pub Med, EMBASE dealing with both LUS and CR for diagnosis of adult CAP, and conducted a meta-analysis to evaluate the diagnostic accuracy of LUS in comparison with CR. The diagnostic standard that the index test compared was the hospital discharge diagnosis or the result of chest computed tomography scan as a "gold standard". We calculated pooled sensitivity and specificity using the Mantel-Haenszel method and pooled diagnostic odds ratio using the DerSimonian-Laird method. Five articles met our inclusion criteria and were included in the final analysis. Using hospital discharge diagnosis as reference, LUS had a pooled sensitivity of 0.95 (0.93-0.97) and a specificity of 0.90 (0.86 to 0.94), CR had a pooled sensitivity of 0.77 (0.73 to 0.80) and a specificity of 0.91 (0.87 to 0.94). LUS and CR compared with computed tomography scan in 138 patients in total, the Z statistic of the two summary receiver operating characteristic was 3.093 (P = 0.002), the areas under the curve for LUS and CR were 0.901 and 0.590, respectively. Our study indicates that LUS can help to diagnosis adult CAP by clinicians and the accuracy was better compared with CR using chest computed tomography scan as the gold standard.
| S-EPMC4479467 | biostudies-literature
Project description:IntroductionStroke-associated pneumonia (SAP) is a significant cause of morbidity and mortality after stroke. Various factors, including dysphagia and stroke severity, are closely related to SAP risk; however, the contribution of the baseline pulmonary parenchymal status to this interplay is an understudied field. Herein, we evaluated the prognostic performance of admission chest computed tomography (CT) findings in predicting SAP.MethodsWe evaluated admission chest CT images, acquired as part of a COVID-19-related institutional policy, in a consecutive series of acute ischemic stroke patients. The pulmonary opacity load at baseline was quantified using automated volumetry and visual scoring algorithms. The relationship between pulmonary opacities with risk of pneumonia within 7 days of symptom onset (i.e., SAP) was evaluated by bivariate and multivariate analyses.ResultsTwenty-three percent of patients in our cohort (n = 100) were diagnosed with SAP. Patients with SAP were more likely to have atrial fibrillation, COPD, severe neurological deficits, and dysphagia. The visual opacity score on chest CT was significantly higher among patients who developed SAP (p = 0.014), while no such relationship was observed in terms of absolute or relative opacity volume. In multivariate analyses, admission stroke severity, presence of dysphagia and a visual opacity score of ≥ 3 (OR 6.37, 95% CI 1.61-25.16; p = 0.008) remained significantly associated with SAP risk.ConclusionsPulmonary opacity burden, as evaluated on admission chest CT, is significantly associated with development of pneumonia within initial days of stroke. This association is independent of other well-known predisposing factors for SAP, including age, stroke severity, and presence of dysphagia.
| S-EPMC9312318 | biostudies-literature
Project description:ObjectivesAn artificial intelligence model was adopted to identify mild COVID-19 pneumonia from computed tomography (CT) volumes, and its diagnostic performance was then evaluated.MethodsIn this retrospective multicenter study, an atrous convolution-based deep learning model was established for the computer-assisted diagnosis of mild COVID-19 pneumonia. The dataset included 2087 chest CT exams collected from four hospitals between 1 January 2019 and 31 May 2020. The true positive rate, true negative rate, receiver operating characteristic curve, area under the curve (AUC) and convolutional feature map were used to evaluate the model.ResultsThe proposed deep learning model was trained on 1538 patients and tested on an independent testing cohort of 549 patients. The overall sensitivity was 91.5% (195/213; p < 0.001, 95% CI: 89.2-93.9%), the overall specificity was 90.5% (304/336; p < 0.001, 95% CI: 88.0-92.9%) and the general AUC value was 0.955 (p < 0.001).ConclusionsA deep learning model can accurately detect COVID-19 and serve as an important supplement to the COVID-19 reverse transcription-polymerase chain reaction (RT-PCR) test.Key points• The implementation of a deep learning model to identify mild COVID-19 pneumonia was confirmed to be effective and feasible. • The strategy of using a binary code instead of the region of interest label to identify mild COVID-19 pneumonia was verified. • This AI model can assist in the early screening of COVID-19 without interfering with normal clinical examinations.
| S-EPMC7971359 | biostudies-literature