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

Prognostication and integration of bedside lung ultrasound and computed tomography imaging findings with clinical features to Predict COVID-19 In-hospital mortality and ICU admission

INTRODUCTION

Bedside lung ultrasound (LUS) and computed tomography (CT) imaging are valuable modalities in screening and diagnosis of pulmonary diseases. This study aims to investigate the prognostic value of integrating LUS and CT imaging findings with clinical features to predict poor outcomes upon ER admission in COVID-19.

METHODS

Patients visiting the study center with clinical presentation and laboratory findings compatible with COVID-19 between April 2020 to January 2022 were considered for this study. Several imaging findings (ground glass opacity, consolidation, atelectatic bands, mosaic attenuation, ARDS pattern, crazy paving, pleural thickening in CT and A-line, comet-tail artifact, confluent B-Line in BLUS, pleural thickening and Consolidation in both modalities) were evaluated, alongside clinical assessments upon admission, to assess their prognostic value. The top radiological, LUS findings, and clinical signs were integrated in a nomogram for predicting mortality.

RESULTS

A total of 1230 patients were included in the analyses. Among the findings, consolidation in BLUS and CT imaging, and absence of A-lines were associated with mortality. In addition to these findings, ground-glass opacities, atelectatic band, mosaic attenuation, crazy paving, and confluent B-line were also associated with ICU hospitalization. Although, the prognostic value of individual markers was poor and comparable (AUC < 0.65), the combined use of top clinical and imaging findings in the associated nomogram led to a high accuracy in predicting mortality (Area under curve: 87.3%).

CONCLUSIONS

BLUS and CT imaging findings alone provide limited utility in stratifying patients for higher mortality and ICU admission risk and should not be used for risk stratification alone outside the context of each patient and their clinical presentations in suspected COVID-19 patients.

Point-of-care lung ultrasound predicts hyperferritinemia and hospitalization, but not elevated troponin in SARS-CoV-2 viral pneumonitis in children

SARS-CoV-2 often causes viral pneumonitis, hyperferritinemia, elevations in D-dimer, lactate dehydrogenase (LDH), transaminases, troponin, CRP, and other inflammatory markers. Lung ultrasound is increasingly used to diagnose and stratify viral pneumonitis severity. We retrospectively reviewed 427 visits in patients aged 14 days to 21 years who had had a point-of-care lung ultrasound in our pediatric emergency department from 30/November/2019 to 14/August/2021. Lung ultrasounds were categorized using a 6-point ordinal scale. Lung ultrasound abnormalities predicted increased hospitalization with a threshold effect. Increasingly abnormal laboratory values were associated with decreased discharge from the ED and increased admission to the ward and ICU. Among patients SARS-CoV-2 positive patients ferritin, LDH, and transaminases, but not CRP or troponin were significantly associated with abnormalities on lung ultrasound and also with threshold effects. This effect was not demonstrated in SARS-CoV-2 negative patients. D-Dimer, CRP, and troponin were sometimes elevated even when the lung ultrasound was normal.

Artificial intelligence and machine learning responses to COVID-19 related inquiries

Researchers and scientists can use computational-based models to turn linked data into useful information, aiding in disease diagnosis, examination, and viral containment due to recent artificial intelligence and machine learning breakthroughs. In this paper, we extensively study the role of artificial intelligence and machine learning in delivering efficient responses to the COVID-19 pandemic almost four years after its start. In this regard, we examine a large number of critical studies conducted by various academic and research communities from multiple disciplines, as well as practical implementations of artificial intelligence algorithms that suggest potential solutions in investigating different COVID-19 decision-making scenarios. We identify numerous areas where artificial intelligence and machine learning can impact this context, including diagnosis (using chest X-ray imaging and CT imaging), severity, tracking, treatment, and the drug industry. Furthermore, we analyse the dilemma's limits, restrictions, and hazards.

Diagnostic accuracy of point-of-care lung ultrasound for COVID-19: a systematic review and meta-analysis

BACKGROUND

Point-of-care (POC) lung ultrasound (LUS) is widely used in the emergency setting and there is an established evidence base across a range of respiratory diseases, including previous viral epidemics. The necessity for rapid testing combined with the limitations of other diagnostic tests has led to the proposal of various potential roles for LUS during the COVID-19 pandemic. This systematic review and meta-analysis focused specifically on the diagnostic accuracy of LUS in adult patients presenting with suspected COVID-19 infection.

METHODS

Traditional and grey-literature searches were performed on 1 June 2021. Two authors independently carried out the searches, selected studies and completed the Quality Assessment Tool for Diagnostic Test Accuracy Studies (QUADAS-2). Meta-analysis was carried out using established open-source packages in R. We report overall sensitivity, specificity, positive and negative predictive values, and the hierarchical summary receiver operating characteristic curve for LUS. Heterogeneity was determined using the I² statistic.

RESULTS

Twenty studies were included, published between October 2020 and April 2021, providing data from a total of 4314 patients. The prevalence and admission rates were generally high across all studies. Overall, LUS was found to be 87.2% sensitive (95% CI 83.6 to 90.2) and 69.5% specific (95% CI 62.2 to 72.5) and demonstrated overall positive and negative likelihood ratios of 3.0 (95% CI 2.3 to 4.1) and 0.16 (95% CI 0.12 to 0.22), respectively. Separate analyses for each reference standard revealed similar sensitivities and specificities for LUS. Heterogeneity was found to be high across the studies. Overall, the quality of studies was low with a high risk of selection bias due to convenience sampling. There were also applicability concerns because all studies were undertaken during a period of high prevalence.

CONCLUSION

During a period of high prevalence, LUS had a sensitivity of 87% for the diagnosis of COVID-19 infection. However, more research is required to confirm these results in more generalisable populations, including those less likely to be admitted to hospital.

PROSPERO REGISTRATION NUMBER

CRD42021250464.

Management of COVID-19 Patients in the Emergency Department

COVID-19 is an emerging disease of global public health concern. As the pandemic overwhelmed emergency departments (EDs), a restructuring of emergency care delivery became necessary in many hospitals. Furthermore, with more than 2000 papers being published each week, keeping up with ever-changing information has proven to be difficult for emergency physicians. The aim of the present review is to provide emergency physician with a summary of the current literature regarding the management of COVID-19 patients in the emergency department.

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

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.

The role of PoCUS in the assessment of COVID-19 patients

The Coronavirus disease 19 (COVID-19) pandemic has increased the burden of stress on the global healthcare system in 2020. Point of care ultrasound (PoCUS) is used effectively in the management of pulmonary, cardiac and vascular pathologies. POCUS is the use of traditional ultrasound imaging techniques in a focused binary manner to answer a specific set of clinical questions. This is an imaging technique that delivers no radiation, is inexpensive, ultraportable and provides results instantaneously to the physician operator at the bedside. In regard to the pandemic, PoCUS has played a significant adjunctive role in the diagnosis and management of co-morbidities associated with COVID-19. PoCUS also offers an alternative method to image obstetric patients and the pediatric population safely in accordance with the ALARA principle. Finally, there have been numerous PoCUS protocols describing the effective use of this technology during the COVID-19 pandemic.