Point-of-care ultrasound in the COVID-19 era: A scoping review

Sequence and directivity in cardiac muscle injury of COVID-19 patients: an observational study

Objective

To compare cardiac function indicators between mild and moderate to severe COVID-19 patients and to try to identify the sequence and directivity in cardiac muscle injury of COVID-19 patients.

Methods

From December 2022 to January 2023, all patients with laboratory-confirmed SARS-CoV-2 infection in Shanghai General Hospital Jiading Branch were enrolled. The clinical classification was stratified into mild, moderate, or severe groups. We collected the clinical and laboratory information, transthoracic echocardiographic and speckle-tracking echocardiographic parameters of patients and compared the differences among different groups.

Results

The values of echocardiographic parameters in mild group were lower than that in moderate or severe group (P < 0.05) except LVEF. The values of LVEF of mild and moderate group were higher than severe group (P < 0.05). There were no significant differences between moderate and severe group. Positive correlations were observed between left ventricular global longitudinal strain (LVGLS) and myoglobin (r = 0.72), E/e' and age (r = 0.79), E/e' and BNP (r = 0.67). The multivariate analysis shows that SpO2 (OR = 0.360, P = 0.02), LVGLS (OR = 3.196, P = 0.003) and E/e' (OR = 1.307, P = 0.036) were the independent risk factors for mild cases progressing to moderate or severe. According to the receiver operating characteristic (ROC) curves, when all the COVID-19 patients was taken as the sample size, the area under the curve (AUC) of the LVGLS was the highest (AUC = 0.861). The AUC of the LVGLS was higher than LVGCS (AUC = 0.565, P < 0.001).

Conclusion

When mild COVID-19 progresses to moderate or severe, both systolic and diastolic functions of the heart are impaired. LVGLS was the independent risk factor for mild cases progressing to moderate or severe cases. Longitudinal changes may manifest earlier than circumferential changes as myocardial disease progresses in COVID-19.

Prediction of the COVID disease using lung CT images by Deep Learning algorithm: DETS-optimized Resnet 101 classifier

As a result of the COVID-19 (coronavirus) disease due to SARS-CoV2 becoming a pandemic, it has spread over the globe. It takes time to evaluate the results of the laboratory tests because of the rising number of cases each day. Therefore, there are restrictions in terms of both therapy and findings. A clinical decision-making system with predictive algorithms is needed to alleviate the pressure on healthcare systems via Deep Learning (DL) algorithms. With the use of DL and chest scans, this research intends to determine COVID-19 patients by utilizing the Transfer Learning (TL)-based Generative Adversarial Network (Pix 2 Pix-GAN). Moreover, the COVID-19 images are then classified as either positive or negative using a Duffing Equation Tuna Swarm (DETS)-optimized Resnet 101 classifier trained on synthetic and real images from the Kaggle lung CT Covid dataset. Implementation of the proposed technique is done using MATLAB simulations. Besides, is evaluated via accuracy, precision, F1-score, recall, and AUC. Experimental findings show that the proposed prediction model identifies COVID-19 patients with 97.2% accuracy, a recall of 95.9%, and a specificity of 95.5%, which suggests the proposed predictive model can be utilized to forecast COVID-19 infection by medical specialists for clinical prediction research and can be beneficial to them.

Integrating Telehealth for Strengthening Health Systems in the Context of the COVID-19 Pandemic: A Perspective from Peru

The COVID-19 pandemic forced the government to rapidly modify its legal framework to adopt telemedicine and promote the implementation of telehealth services to meet the healthcare needs of patients in Peru. In this paper, we aim to review the main changes to the regulatory framework and describe selected initiatives to promote the telehealth framework that emerged in Peru during the COVID-19 pandemic. In addition, we discuss the challenges to integrate telehealth services for strengthening health systems in Peru. The Peruvian telehealth regulatory framework began in 2005, and in subsequent years, laws and regulations were established that sought to progressively implement a national telehealth network. However, mainly local initiatives were deployed. In this sense, significant challenges remain to be addressed, such as infrastructure in healthcare centers, including high-speed Internet connectivity; infostructure of health-information systems, including interoperability with electronic medical records; monitoring and evaluation of the national agenda for the health sector in 2020-2025; expanding the healthcare workforce in terms of digital health; and developing the capacities of healthcare users on health literacy, including digital aspects. In addition, there is enormous potential for telemedicine as a key strategy to deal with the COVID-19 pandemic and to improve access to rural and hard-to-reach areas and populations. There is thus an urgent need to effectively implement an integrated national telehealth system to address sociocultural issues and strengthen the competencies of human resources in telehealth and digital health in Peru.

Ultrasound during the COVID-19 Pandemic: A Global Approach

SARS-CoV-2 (severe acute respiratory syndrome Coronavirus-2) rapidly spread worldwide as COVID-19 (Coronavirus disease 2019), causing a costly and deadly pandemic. Different pulmonary manifestations represent this syndrome's most common clinical manifestations, together with the cardiovascular complications frequently observed in these patients. Ultrasound (US) evaluations of the lungs, heart, and lower limbs may be helpful in the diagnosis, follow-up, and prognosis of patients with COVID-19. Moreover, POCUS (point-of-care ultrasound) protocols are particularly useful for patients admitted to intensive care units. The present review aimed to highlight the clinical conditions during the SARS-CoV-2 pandemic in which the US represents a crucial diagnostic tool.

Deep Learning and Medical Image Analysis for COVID-19 Diagnosis and Prediction

The coronavirus disease 2019 (COVID-19) pandemic has imposed dramatic challenges to health-care organizations worldwide. To combat the global crisis, the use of thoracic imaging has played a major role in diagnosis, prediction, and management for COVID-19 patients with moderate to severe symptoms or with evidence of worsening respiratory status. In response, the medical image analysis community acted quickly to develop and disseminate deep learning models and tools to meet the urgent need of managing and interpreting large amounts of COVID-19 imaging data. This review aims to not only summarize existing deep learning and medical image analysis methods but also offer in-depth discussions and recommendations for future investigations. We believe that the wide availability of high-quality, curated, and benchmarked COVID-19 imaging data sets offers the great promise of a transformative test bed to develop, validate, and disseminate novel deep learning methods in the frontiers of data science and artificial intelligence. Expected final online publication date for the Annual Review of Biomedical Engineering, Volume 24 is June 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Progressive left and right heart dysfunction in coronavirus disease-19: Prospective echocardiographic evaluation

Aims

Cardiac dysfunction in coronavirus disease-19 (COVID-19) has been reported during acute phase but serial changes have not been well studied. To determine serial changes in type and severity of echocardiographic left and right heart functions we performed a prospective study.

Methods and results

Successive COVID-19 patients at discharge from the hospital from June to December 2020 were enrolled. Clinical details were obtained and echocardiography was performed using Philips IE33X-Matrix. Follow-up evaluation was performed after 3 months. In total, 1789 COVID-19 patients were evaluated. Baseline echocardiography was performed in 1000 eligible patients (men 611, women 389). Mean age was 50.2 ± 15 years, hypertension was in 44.0%, diabetes in 49.4%, and coronary disease in 10.8%. COVID-19 was mild in 47.0%, moderate in 39.5%, and severe in 13.5%. Baseline cardiac parameters were more impaired in severe vs. moderate or mild COVID-19. At 3 months, in 632 patients where baseline and follow-up data were available, decline was observed in select left [left ventricular internal diameter in diastole +0.9 ± 0.2 mm, left atrial volume +7.6 ± 0.1 mL/m², mitral E/e′ +4.8 ± 0.1, and left ventricular ejection fraction (LVEF) −3.7 ± 0.2%] and right [right ventricular internal diameter in diastole +2.1 ± 0.1 mm, right atrial internal dimension +1.6 ± 0.1 mm, tricuspid V_max +1.0 ± 0.1 cm, and tricuspid annulus plane systolic excursion (TAPSE) −2.7 ± 0.2 mm] heart variables (P < 0.001). Compared to mild COVID-19, decline was significantly greater in moderate/severe disease, LVEF −1.1 ± 0.3 vs. −3.8 ± 0.3%; mitral E/e′ +3.2 ± 0.1 vs. +4.8 ± 0.1, tricuspid V_max +0.3 ± 0.1 vs. +1.0 ± 0.1 cm, and TAPSE −0.7 ± 0.2 vs. −2.7 ± 0.2 mm (P < 0.001).

Conclusion

This study shows impaired cardiac functions in severe and moderate COVID-19 compared to mild at hospital discharge and progressive decline in left and right heart functions at 3 months. Impairment is significantly greater in patients with moderate to severe disease.

The role of chest imaging in the diagnosis, management, and monitoring of coronavirus disease 2019 (COVID-19)

Coronavirus disease 2019 (COVID-19) pandemic has posed a major public health crisis all over the world. The role of chest imaging, especially computed tomography (CT), has evolved during the pandemic paralleling the accumulation of scientific evidence. In the early stage of the pandemic, the performance of chest imaging for COVID-19 has widely been debated especially in the context of comparison to real-time reverse transcription polymerase chain reaction. Current evidence is against the use of chest imaging for routine screening of COVID-19 contrary to the initial expectations. It still has an integral role to play, however, in its work up and staging, especially when assessing complications or disease progression. Chest CT is gold standard imaging modality for COVID-19 pneumonia; in some situations, chest X-ray or ultrasound may be an effective alternative. The most important role of radiologists in this context is to be able to identify those patients at greatest risk of imminent clinical decompensation by learning to stratify cases of COVID-19 on the basis of radiologic imaging in the most efficient and timely fashion possible. The present availability of multiple and more refined CT grading systems and classification is now making this task easier and thereby contributing to the recent improvements achieved in COVID-19 treatment and outcomes. In this article, evidence of chest imaging regarding diagnosis, management and monitoring of COVID-19 will be chronologically reviewed.

COVID-19 imaging: Diagnostic approaches, challenges, and evolving advances

The role of radiology and the radiologist have evolved throughout the coronavirus disease-2019 (COVID-19) pandemic. Early on, chest computed tomography was used for screening and diagnosis of COVID-19; however, it is now indicated for high-risk patients, those with severe disease, or in areas where polymerase chain reaction testing is sparsely available. Chest radiography is now utilized mainly for monitoring disease progression in hospitalized patients showing signs of worsening clinical status. Additionally, many challenges at the operational level have been overcome within the field of radiology throughout the COVID-19 pandemic. The use of teleradiology and virtual care clinics greatly enhanced our ability to socially distance and both are likely to remain important mediums for diagnostic imaging delivery and patient care. Opportunities to better utilize of imaging for detection of extrapulmonary manifestations and complications of COVID-19 disease will continue to arise as a more detailed understanding of the pathophysiology of the virus continues to be uncovered and identification of predisposing risk factors for complication development continue to be better understood. Furthermore, unidentified advancements in areas such as standardized imaging reporting, point-of-care ultrasound, and artificial intelligence offer exciting discovery pathways that will inevitably lead to improved care for patients with COVID-19.