Accuracy of Bedside Lung Ultrasound as a Rapid Triage Tool for Suspected Covid-19 Cases

Karagöz, Arıf; Sağlam, Caner; Demirbaş, Hakan Bariş; Korkut, Semih; Ünlüer, Erden Erol

doi:10.1097/ruq.0000000000000530

Cited by 12 publications

(13 citation statements)

References 31 publications

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“…Meanwhile, Bosso et al [18] found a marginally higher sensitivity (73%) and specificity (89%). By contrast, other groups [23][24][25][26][27] used alternative scoring methodologies and reported high sensitivities ranging from 90% to 100%. The specificity was a consistent trade-off in most studies [22,24,27,28].…”

Section: Discussionmentioning

confidence: 97%

“…The diagnostic performance discrepancy noticeable in the LUS in COVID-19 literature can be explained by the varying definitions of LUS outcome measure (i.e., how to interpret the images). Some studies have merely used any presence of B-lines or consolidations indicative of COVID-19 [21,25,27,32]. Others developed an arbitrary LUS scoring system based on a 5-point Likert scale [24] or a 6-point system [28].…”

Section: Discussionmentioning

confidence: 99%

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Lung Ultrasound using a Handheld Device to Diagnose COVID-19 in the Emergency Department

Alfuraih¹,

Alshehri²,

Alshehri³

et al. 2021

DSAHMJ

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The Coronavirus Disease 2019 (COVID-19) pandemic has critically struck healthcare systems and burdened emergency services. To date, there is no accurate and rapid point-of-care diagnostic test. This study aimed to investigate Lung Ultrasound (LUS) against Reverse Transcription-Polymerase Chain Reaction (RT-PCR) test in suspected COVID-19 patients presenting to the emergency department. In 20 eligible patients (mean age ± standard deviation, 49 ± 15 years), 12 had a positive RT-PCR test and undergone an LUS examination over 12 lung zones using a handheld ultrasound device. Each zone was semiquantitatively scored according to the Lung Ultrasound Scoring System (LUSS) from 0 to 3 based on the severity of findings (pleural line irregularity, B-lines, consolidations) and documented the presence of light beam artifacts. A second blinded reader scored the images to investigate interreader reproducibility. The LUSS score had a modest diagnostic performance at 66.6% [95% Confidence Interval (CI), 34.9-90.0%] sensitivity and 75.0% (95% CI, 34.9-96.8%) specificity. The light beam artifact was more prevalent and sensitive to COVID-19 patients with 81.8% (95% CI, 48.2-97.7%) sensitivity and 75.0% (95% CI, 34.9-96.8%) specificity. LUS had an almost perfect interreader reproducibility for LUSS (Kendall's W = 0.961; 95% CI, 0.894-0.985) and light beam artifact (Cohen's κ = 0.890; 95% CI, 0.683-1.00). Overall, LUS using handheld devices can offer a safe, reproducible, rapid, and feasible first-line tool for detecting COVID-19 patients in emergency departments. The light beam artifact was more sensitive and specific to COVID-19 patients and can be useful for effectively triaging suspected cases.

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Section: Discussionmentioning

confidence: 97%

Section: Discussionmentioning

confidence: 99%

Lung Ultrasound using a Handheld Device to Diagnose COVID-19 in the Emergency Department

Alfuraih¹,

Alshehri²,

Alshehri³

et al. 2021

DSAHMJ

View full text Add to dashboard Cite

show abstract

“…During the last year, studies have shown that typical chest CT patterns of COVID-19 viral pneumonia include multifocal bilateral peripheral ground-glass areas associated with subsegmental patchy consolidations, mostly subpleural, and predominantly involving lower lung lobes and posterior segments [28][29][30][31][32]. In more detail, chest CT images of COVID-19 patients could be evaluated using the following characteristics [33][34][35][36][37][38][39][40]:…”

Section: Ct Diagnosis Algorithmsmentioning

confidence: 99%

AI Modeling to Combat COVID-19 Using CT Scan Imaging Algorithms and Simulations: A Study

Zaeri¹

2022

Simulation Modeling

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The coronavirus disease 2019 (COVID-19) outbreak has been designated as a worldwide pandemic by World Health Organization (WHO) and raised an international call for global health emergency. In this regard, recent advancements of technologies in the field of artificial intelligence and machine learning provide opportunities for researchers and scientists to step in this battlefield and convert the related data into a meaningful knowledge through computational-based models, for the task of containment the virus, diagnosis and providing treatment. In this study, we will provide recent developments and practical implementations of artificial intelligence modeling and machine learning algorithms proposed by researchers and practitioners during the pandemic period which suggest serious potential in compliant solutions for investigating diagnosis and decision making using computerized tomography (CT) scan imaging. We will review the modern algorithms in CT scan imaging modeling that may be used for detection, quantification, and tracking of Coronavirus and study how they can differentiate Coronavirus patients from those who do not have the disease.

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“…A total of 1896 documents were retrieved. After reading the titles, abstracts, and full text of the documents, nine articles were included in the analysis [8][9][10][11][12][13][14][15][16] (Figure 1).…”

Section: Literature Selection Processmentioning

confidence: 99%

A Comparison of Lung Ultrasound and Computed Tomography in the Diagnosis of Patients with COVID-19: A Systematic Review and Meta-Analysis

et al. 2021

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Background Lung ultrasound (LUS) and computed tomography (CT) can both be used for diagnosis of interstitial pneumonia caused by coronavirus disease 2019 (COVID-19), but the agreement between LUS and CT is unknown. Purpose to compare the agreement of LUS and CT in the diagnosis of interstitial pneumonia caused by COVID-19. Materials and Methods We searched PubMed, Cochrane library, Embase, Chinese Biomedicine Literature, and WHO COVID-19 databases to identify studies that compared LUS with CT in the diagnosis of interstitial pneumonia caused by COVID-19. We calculated the pooled overall, positive and negative percent agreements, diagnostic odds ratio (DOR) and the area under the standard receiver operating curve (SROC) for LUS in the diagnosis of COVID-19 compared with CT. Results We identified 1896 records, of which nine studies involving 531 patients were finally included. The pooled overall, positive and negative percentage agreements of LUS for the diagnosis of interstitial pneumonia caused by COVID-19 compared with CT were 81% (95% confidence interval [CI] 43–99%), 96% (95% CI, 80–99%, I2 = 92.15%) and 80% (95%CI, 60–92%, I2 = 92.85%), respectively. DOR was 37.41 (95% CI, 9.43–148.49, I2 = 63.9%), and the area under the SROC curve was 0.94 (95% CI, 0.92–0.96). The quality of evidence for both specificity and sensitivity was low because of heterogeneity and risk of bias. Conclusion The level of diagnostic agreement between LUS and CT in the diagnosis of interstitial pneumonia caused by COVID-19 is high. LUS can be therefore considered as an equally accurate alternative for CT in situations where molecular tests are not available.

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Accuracy of Bedside Lung Ultrasound as a Rapid Triage Tool for Suspected Covid-19 Cases

Cited by 12 publications

References 31 publications

Lung Ultrasound using a Handheld Device to Diagnose COVID-19 in the Emergency Department

Lung Ultrasound using a Handheld Device to Diagnose COVID-19 in the Emergency Department

AI Modeling to Combat COVID-19 Using CT Scan Imaging Algorithms and Simulations: A Study

A Comparison of Lung Ultrasound and Computed Tomography in the Diagnosis of Patients with COVID-19: A Systematic Review and Meta-Analysis

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