Clinical and imaging characteristics of patients with COVID-19 predicting hospital readmission after emergency department discharge: a single-centre cohort study in Italy

Elevated NET, Calprotectin, and Neopterin Levels Discriminate between Disease Activity in COVID-19, as Evidenced by Need for Hospitalization among Patients in Northern Italy

Coronavirus disease 2019 (COVID-19) displays clinical heterogeneity, but little information is available for patients with mild or very early disease. We aimed to characterize biomarkers that are useful for discriminating the hospitalization risk in a COVID-19 cohort from Northern Italy during the first pandemic wave. We enrolled and followed for four weeks 76 symptomatic SARS-CoV-2 positive patients and age/sex-matched healthy controls. Patients with mild disease were discharged (n.42), and the remaining patients were hospitalized (n.34). Blood was collected before any anti-inflammatory/immunosuppressive therapy and assessed for soluble C5b-9/C5a, H3-neutrophil extracellular traps (NETs), calprotectin, and DNase plasma levels via ELISA and a panel of proinflammatory cytokines via ELLA. Calprotectin and NET levels discriminate between hospitalized and non-hospitalized patients, while DNase negatively correlates with NET levels; there are positive correlations between calprotectin and both NET and neopterin levels. Neopterin levels increase in patients at the beginning of the disease and do so more in hospitalized than non-hospitalized patients. C5a and sC5b-9, and other acute phase proteins, correlate with neopterin, calprotectin, and DNase. Both NET and neopterin levels negatively correlate with platelet count. We show that calprotectin, NETs, and neopterin are important proinflammatory parameters potentially useful for discriminating between COVID-19 patients at risk of hospitalization.

Point-of-care ultrasound use in COVID-19: a narrative review

Background and Objective

The coronavirus disease 2019 (COVID-19) pandemic that began in early 2020 resulted in significant mortality from respiratory tract infections. Existing imaging modalities such as chest X-ray (CXR) lacks sensitivity in its diagnosis while computed tomography (CT) scan carries risks of radiation and contamination. Point-of-care ultrasound (POCUS) has the advantage of bedside testing with higher diagnostic accuracy. We aim to describe the various applications of POCUS for patients with suspected severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection in the emergency department (ED) and intensive care unit (ICU).

Methods

We performed literature search on the use of POCUS in the diagnosis and management of COVID-19 in MEDLINE, Embase and Scopus databases using the following search terms: "ultrasonography", "ultrasound", "COVID-19", "SARS-CoV-2", "SARS-CoV-2 variants", "emergency services", "emergency department" and "intensive care units". Search was performed independently by two reviewers with any discrepancy adjudicated by a third member.

Key Content and Findings

Lung POCUS in patients with COVID-19 shows different ultrasonographic features from pulmonary oedema, bacterial pneumonia, and other viral pneumonia, thus useful in differentiating between these conditions. It is more sensitive than CXR, and more accessible and widely available than CT scan. POCUS can be used to diagnose COVID-19 pneumonia, screen for COVID-19-related pulmonary and extrapulmonary complications, and guide management of ICU patients, such as timing of ventilator weaning based on lung POCUS findings.

Conclusions

POCUS is a useful and rapid point-of-care modality that can be used to aid in diagnosis, management, and risk stratification of COVID-19 patients in different healthcare settings.

Complement activation predicts negative outcomes in COVID-19: The experience from Northen Italian patients

Coronavirus disease 19 (COVID-19) may present as a multi-organ disease with a hyperinflammatory and prothrombotic response (immunothrombosis) in addition to upper and lower airway involvement. Previous data showed that complement activation plays a role in immunothrombosis mainly in severe forms. The study aimed to investigate whether complement involvement is present in the early phases of the disease and can be predictive of a negative outcome. We enrolled 97 symptomatic patients with a positive RT-PCR for SARS-CoV-2 presenting to the emergency room. The patients with mild symptoms/lung involvement at CT-scan were discharged and the remaining were hospitalized. All the patients were evaluated after a 4-week follow-up and classified as mild (n. 54), moderate (n. 17) or severe COVID-19 (n. 26). Blood samples collected before starting any anti-inflammatory/immunosuppressive therapy were assessed for soluble C5b-9 (sC5b-9) and C5a plasma levels by ELISA, and for the following serum mediators by ELLA: IL-1β, IL-6, IL-8, TNFα, IL-4, IL-10, IL-12p70, IFNγ, IFNα, VEGF-A, VEGF-B, GM-CSF, IL-2, IL-17A, VEGFR2, BLyS. Additional routine laboratory parameters were measured (fibrin fragment D-dimer, C-reactive protein, ferritin, white blood cells, neutrophils, lymphocytes, monocytes, platelets, prothrombin time, activated partial thromboplastin time, and fibrinogen). Fifty age and sex-matched healthy controls were also evaluated. SC5b-9 and C5a plasma levels were significantly increased in the hospitalized patients (moderate and severe) in comparison with the non-hospitalized mild group. SC5b9 and C5a plasma levels were predictive of the disease severity evaluated one month later. IL-6, IL-8, TNFα, IL-10 and complement split products were higher in moderate/severe versus non-hospitalized mild COVID-19 patients and healthy controls but with a huge heterogeneity. SC5b-9 and C5a plasma levels correlated positively with CRP, ferritin values and the neutrophil/lymphocyte ratio. Complement can be activated in the very early phases of the disease, even in mild non-hospitalized patients. Complement activation can be observed even when pro-inflammatory cytokines are not increased, and predicts a negative outcome.

Different Lung Parenchyma Quantification Using Dissimilar Segmentation Software: A Multi-Center Study for COVID-19 Patients

Background: Chest Computed Tomography (CT) imaging has played a central role in the diagnosis of interstitial pneumonia in patients affected by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and can be used to obtain the extent of lung involvement in COVID-19 pneumonia patients either qualitatively, via visual inspection, or quantitatively, via AI-based software. This study aims to compare the qualitative/quantitative pathological lung extension data on COVID-19 patients. Secondly, the quantitative data obtained were compared to verify their concordance since they were derived from three different lung segmentation software. Methods: This double-center study includes a total of 120 COVID-19 patients (60 from each center) with positive reverse-transcription polymerase chain reaction (RT-PCR) who underwent a chest CT scan from November 2020 to February 2021. CT scans were analyzed retrospectively and independently in each center. Specifically, CT images were examined manually by two different and experienced radiologists for each center, providing the qualitative extent score of lung involvement, whereas the quantitative analysis was performed by one trained radiographer for each center using three different software: 3DSlicer, CT Lung Density Analysis, and CT Pulmo 3D. Results: The agreement between radiologists for visual estimation of pneumonia at CT can be defined as good (ICC 0.79, 95% CI 0.73–0.84). The statistical tests show that 3DSlicer overestimates the measures assessed; however, ICC index returns a value of 0.92 (CI 0.90–0.94), indicating excellent reliability within the three software employed. ICC was also performed between each single software and the median of the visual score provided by the radiologists. This statistical analysis underlines that the best agreement is between 3D Slicer “LungCTAnalyzer” and the median of the visual score (0.75 with a CI 0.67–82 and with a median value of 22% of disease extension for the software and 25% for the visual values). Conclusions: This study provides for the first time a direct comparison between the actual gold standard, which is represented by the qualitative information described by radiologists, and novel quantitative AI-based techniques, here represented by three different commonly used lung segmentation software, underlying the importance of these specific values that in the future could be implemented as consistent prognostic and clinical course parameters.

Imaging-based indices combining disease severity and time from disease onset to predict COVID-19 mortality: A cohort study

Background

COVID-19 prognostic factors include age, sex, comorbidities, laboratory and imaging findings, and time from symptom onset to seeking care.

Purpose

The study aim was to evaluate indices combining disease severity measures and time from disease onset to predict mortality of COVID-19 patients admitted to the emergency department (ED).

Materials and methods

All consecutive COVID-19 patients who underwent both computed tomography (CT) and chest X-ray (CXR) at ED presentation between 27/02/2020 and 13/03/2020 were included. CT visual score of disease extension and CXR Radiographic Assessment of Lung Edema (RALE) score were collected. The CT- and CXR-based scores, C-reactive protein (CRP), and oxygen saturation levels (sO₂) were separately combined with time from symptom onset to ED presentation to obtain severity/time indices. Multivariable regression age- and sex-adjusted models without and with severity/time indices were compared. For CXR-RALE, the models were tested in a validation cohort.

Results

Of the 308 included patients, 55 (17.9%) died. In multivariable logistic age- and sex-adjusted models for death at 30 days, severity/time indices showed good discrimination ability, higher for imaging than for laboratory measures (AUC_CT = 0.92, AUC_CXR = 0.90, AUC_CRP = 0.88, AUC_sO2 = 0.88). AUC_CXR was lower in the validation cohort (0.79). The models including severity/time indices performed slightly better than models including measures of disease severity not combined with time and those including the Charlson Comorbidity Index, except for CRP-based models.

Conclusion

Time from symptom onset to ED admission is a strong prognostic factor and provides added value to the interpretation of imaging and laboratory findings at ED presentation.