The diagnostic performance of lung ultrasound for detecting COVID-19 in emergency departments: A systematic review and meta-analysis

[Chest ultrasound for imaging of pneumonia]

Diagnosis of pneumonia can be challenging, particularly the differential diagnosis of lower respiratory tract infection and pneumonia, acute respiratory failure, the diagnosis of nosocomial pneumonia and in case of treatment failure. As compared to conventional chest radiography and CT of the scan, sonography of the chest offers advantages. It could be demonstrated that it was even superior to chest radiography in the identification of pneumonic consolidations. Since most pneumonias affect the lower lobes and include the pleura, pneumonic substrates could be identified in up to 90% of cases despite the limited penetration depth of lung ultrasound. Sonography of the chest has become an established method in the diagnosis of both adult as well as in pediatric community-acquired pneumonia. In addition, it is particularly powerful when used within a point of care (POCUS) approach which also includes the evaluation of the heart. Finally, it appears to have significant potential also in the diagnosis of nosomomial pneumonia and in the evaluation of treatment response, both in the ward as in the ICU.

Comparison of lung ultrasound scoring systems for the prognosis of COVID-19 in the emergency department: An international prospective cohort study

Purpose

The purpose of this study was to evaluate whether the lung ultrasound (LUS) scores applied to an international cohort of patients presenting to the emergency department (ED) with suspected COVID-19, and subsequently admitted with proven disease, could prognosticate clinical outcomes.

Methods

This was an international, multicentre, prospective, observational cohort study of patients who received LUS and were followed for the composite primary outcome of intubation, intensive care unit (ICU) admission or death. LUS scores were later applied including two 12-zone protocols ('de Alencar score' and 'CLUE score'), a 12-zone protocol with lung and pleural findings ('Ji score') and an 11-zone protocol ('Tung-Chen score'). The primary analysis comprised logistic regression modelling of the composite primary outcome, with the LUS scores analysed individually as predictor variables.

Results

Between April 2020 to April 2022, 129 patients with COVID-19 had LUS performed according to the protocol and 24 (18.6%) met the composite primary endpoint. No association was seen between the LUS score and the composite primary end point for the de Alencar score [odds ratio (OR) = 1.04; 95% confidence interval (CI): 0.97-1.11; P = 0.29], the CLUE score (OR = 1.03; 95% CI: 0.96-1.10; P = 0.40), the Ji score (OR = 1.02; 95% CI: 0.97-1.07; P = 0.40) or the Tung-Chen score (OR = 1.02; 95% CI: 0.97-1.08).

Discussion

Compared to these earlier studies performed at the start of the pandemic, the negative outcome of our study could reflect the changing scenario of the COVID-19 pandemic, including patient, disease, and system factors. The analysis suggests that the study may have been underpowered to detect a weaker association between a LUS score and the primary outcome.

Conclusion

In an international cohort of adult patients presenting to the ED with suspected COVID-19 disease who had LUS performed and were subsequently admitted to hospital, LUS severity scores did not prognosticate the need for invasive ventilation, ICU admission or death.

Visual Perception and Convolutional Neural Network-Based Robotic Autonomous Lung Ultrasound Scanning Localization System

Under the situation of severe COVID-19 epidemic, lung ultrasound (LUS) has been proved to be an effective and convenient method to diagnose and evaluate the extent of respiratory disease. However, the traditional clinical ultrasound (US) scanning requires doctors not only to be in close contact with patients but also to have rich experience. In order to alleviate the shortage of medical resources and reduce the work stress and risk of infection for doctors, we propose a visual perception and convolutional neural network (CNN)-based robotic autonomous LUS scanning localization system to realize scanned target recognition, probe pose solution and movement, and the acquisition of US images. The LUS scanned targets are identified through the target segmentation and localization algorithm based on the improved CNN, which is using the depth camera to collect the image information; furthermore, the method based on multiscale compensation normal vector is used to solve the attitude of the probe; finally, a position control strategy based on force feedback is designed to optimize the position and attitude of the probe, which can not only obtain high-quality US images but also ensure the safety of patients and the system. The results of human LUS scanning experiment verify the accuracy and feasibility of the system. The positioning accuracy of the scanned targets is 15.63 ± 0.18 mm, and the distance accuracy and rotation angle accuracy of the probe position calculation are 6.38 ± 0.25 mm and 8.60° ±2.29° , respectively. More importantly, the obtained high-quality US images can clearly capture the main pathological features of the lung. The system is expected to be applied in clinical practice.

The value of lung ultrasound in COVID-19 pneumonia, verified by high resolution computed tomography assessed by artificial intelligence

BACKGROUND

Lung ultrasound (LUS) is an increasingly popular imaging method in clinical practice. It became particularly important during the COVID-19 pandemic due to its mobility and ease of use compared to high-resolution computed tomography (HRCT). The objective of this study was to assess the value of LUS in quantifying the degree of lung involvement and in discrimination of lesion types in the course of COVID-19 pneumonia as compared to HRCT analyzed by the artificial intelligence (AI).

METHODS

This was a prospective observational study including adult patients hospitalized due to COVID-19 in whom initial HRCT and LUS were performed with an interval < 72 h. HRCT assessment was performed automatically by AI. We evaluated the correlations between the inflammation volume assessed both in LUS and HRCT, between LUS results and the HRCT structure of inflammation, and between LUS and the laboratory markers of inflammation. Additionally we compared the LUS results in subgroups depending on the respiratory failure throughout the hospitalization.

RESULTS

Study group comprised 65 patients, median 63 years old. For both lungs, the median LUS score was 19 (IQR-interquartile range 11-24) and the median CT score was 22 (IQR 16-26). Strong correlations were found between LUS and CT scores (for both lungs r = 0.75), and between LUS score and percentage inflammation volume (PIV) (r = 0.69). The correlations remained significant, if weakened, for individual lung lobes. The correlations between LUS score and the value of the percentage consolidation volume (PCV) divided by percentage ground glass volume (PGV), were weak or not significant. We found significant correlation between LUS score and C-reactive protein (r = 0.55), and between LUS score and interleukin 6 (r = 0.39). LUS score was significantly higher in subgroups with more severe respiratory failure.

CONCLUSIONS

LUS can be regarded as an accurate method to evaluate the extent of COVID-19 pneumonia and as a promising tool to estimate its clinical severity. Evaluation of LUS in the assessment of the structure of inflammation, requires further studies in the course of the disease.

TRIAL REGISTRATION

The study has been preregistered 13 Aug 2020 on clinicaltrials.gov with the number NCT04513210.

Seeing is Believing: The Import of Lung Ultrasound!

How to cite this article: Govil D, Pachisia AV. Seeing is Believing: The Import of Lung Ultrasound! Indian J Crit Care Med 2022;26(8):894-895.

Multiorgan Involvement in SARS-CoV-2 Infection: The Role of the Radiologist from Head to Toe

Radiology plays a crucial role for the diagnosis and management of COVID-19 patients during the different stages of the disease, allowing for early detection of manifestations and complications of COVID-19 in the different organs. Lungs are the most common organs involved by SARS-CoV-2 and chest computed tomography (CT) represents a reliable imaging-based tool in acute, subacute, and chronic settings for diagnosis, prognosis, and management of lung disease and the evaluation of acute and chronic complications. Cardiac involvement can be evaluated by using cardiac computed tomography angiography (CCTA), considered as the best choice to solve the differential diagnosis between the most common cardiac conditions: acute coronary syndrome, myocarditis, and cardiac dysrhythmia. By using compressive ultrasound it's possible to study the peripheral arteries and veins and to exclude the deep vein thrombosis, directly linked to the onset of pulmonary embolism. Moreover, CT and especially MRI can help to evaluate the gastrointestinal involvement and assess hepatic function, pancreas involvement, and exclude causes of lymphocytopenia, thrombocytopenia, and leukopenia, typical of COVID-19 patients. Finally, radiology plays a crucial role in the early identification of renal damage in COVID-19 patients, by using both CT and US. This narrative review aims to provide a comprehensive radiological analysis of commonly involved organs in patients with COVID-19 disease.