Insights from comparison of the clinical presentation and outcomes of patients hospitalized with COVID-19 in an Italian internal medicine ward during first and third wave.

Background: The reasons of variability of clinical presentation of coronavirus disease-19 (COVID-19) across different pandemic waves are not fully understood, and may include individual risk profile, SARS-CoV-2 lineage and seasonal variations of viral spread. The objective of this retrospective study was to compare the characteristics and outcomes of patients admitted with confirmed coronavirus disease-19 (COVID-19) in the same season during the first (March 2020) and the third pandemic wave (March 2021, dominance of SARS-CoV-2 B.1.1.7 lineage) in an internal medicine ward of a large teaching hospital in Italy. Materials and methods: Data of 769 unvaccinated patients (399 from the first and 370 from the third wave) were collected from clinical records, including symptom type and duration, extension of lung abnormalities on chest computed tomography (CT) and PaO2/FiO2 ratio on admission arterial blood gas analysis. Results: Third wave patients were in average younger (median 65, interquartile range [IQR] 55-75, vs. 72, IQR 61-81 years old, p < 0.001), with less comorbidities and better pulmonary (CT visual score median 25, IQR 15-40, vs. 30, IQR 15-50, age- and sex-adjusted p = 0.017) and respiratory involvement (PaO2/FiO2 median 288, IQR 237-338, vs. 233, IQR 121-326 mmHg, age- and sex-adjusted p < 0.001) than first wave patients. Hospital mortality was lower (19% vs. 36%, p < 0.001), but not for subjects over 75 years old (46 vs. 49%). Age, number of chronic illnesses, PCT levels, CT visual score [Odds Ratio (OR) 1.022, 95% confidence interval (CI) 1.009-1.036, p < 0.001] and PaO2/FiO2 (OR 0.991, 95% CI 0.988-0.994, p < 0.001), but not the pandemic wave, were associated with mortality on stepwise multivariate logistic regression analysis. Conclusion: Despite the higher virulence of B.1.1.7 lineage, we detected milder clinical presentation and improved mortality in patients hospitalized during the third COVID-19 wave, with involvement of younger subjects. The reasons of this discrepancy are unclear, but could involve the population effect of vaccination campaigns, that were being conducted primarily in older frail subjects during the third wave.

Safety of surgical masks during physical activity evaluated with graded cycle ergometry test.

BACKGROUND: At the time of the coronavirus disease 2019 (COVID-19) pandemic wearing surgical mask (SM) is recommended for the prevention of contracting or exposing others to airborne transmission of COVID-19. It is somewhat controversial whether wearing SM during exercise affects performance and health status and/or may influence the results. In order to give an answer we planned a prospective, randomized, crossover study to evaluate the effects of wearing a SM or no-SM in 33 (17 male) physically active healthy subjects during a graded exercise cycle ergometry test. METHODS: The two tests were performed in random order in the same subjects. The participants were all tested the same day, after a recovery time of at least of one hour, in order to avoid interferences on physical performances. Arterial oxygen saturation, heart rate and arterial blood pressure were assessed throughout the exercise tests every step of two minutes, at the end of exercise, performed at the same time with and without mask. RESULTS: Wearing SM had no effect on performance, since the duration of cycle ergometry test with SM and without SM median, respectively, was 14.2 (lower-upper quartile 13.9-14.8) versus 14.3 (13.9-15.5) minutes (P=0.094), and median peak power was 150 W (150-180) versus 150 W (120-180) (P=0.754). CONCLUSIONS: When expressed relative to peak exercise performance, no differences were found between wearing or not wearing SM regarding arterial oxygen saturation, or heart rate at any time during the exercise tests. Wearing SM during vigorous exercise had no detrimental effect on cardiovascular parameters, as well as on exercise metrics in all participants. No ventricular repolarization abnormalities and no arrhythmias were reported on the electrocardiograms.

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.

Beyond the lung involvement in COVID-19 patients.

Since COVID-19 spread all over the world becoming a pandemic illness, researchers have better characterized route of virus transmissibility and clinical signs and symptoms of the disease. Since viral transmission occurs through the droplets emitted during coughing or sneezing, the lungs are primarily affected. However, SARS-CoV-2 can affect several human organs due to high expressions of ACE2 receptor which is the main viral target, and the virus may affect not only higher and lower respiratory tracts, but also heart, kidney, gastro enteric tract, liver, pancreas, nervous system and skin. This review focuses on extra pulmonary involvement underlying atypical presentation of COVID-19. There is a great body of evidence concerning several human organ abnormalities associated to the SARS-CoV-2, enough to consider COVID-19 as a multisystemic and polyhedral disease.

Co-Administration of Remdesivir and Azithromycin May Protect against Intensive Care Unit Admission in COVID-19 Pneumonia Requiring Hospitalization: A Real-Life Observational Study.

The benefits of remdesivir treatment, with or without co-administration of antibiotics such as azithromycin, are uncertain in COVID-19 pneumonia. The aim of this retrospective single-center study was to assess the effects of remdesivir, with or without azithromycin, on hospital mortality, intensive care unit (ICU) admission, and need of non-invasive ventilation. The clinical records of the COVID-19 patients hospitalized in an Italian ward in March 2021 were analyzed, and data on comorbidities and clinical, radiological, and laboratory presentation of the disease were collected. Among 394 participants (234 M), 173 received remdesivir (43.9%), including 81 with azithromycin (20.5%). Remdesivir recipients were younger, with less comorbidities, and had better PaO2/FiO2 and clinical outcomes, including reduced mortality, but the differences were not independent of covariates. Rates of ICU transferal were 17%, 9%, and 1% in the no remdesivir, remdesivir without azithromycin, and remdesivir/azithromycin groups, respectively. In a stepwise multivariate logistic regression model, remdesivir/azithromycin co-treatment was independently associated with reduced ICU admission (vs remdesivir alone, OR 0.081, 95% CI 0.008-0.789, p = 0.031; vs no remdesivir, OR 0.060, 95% CI 0.007-0.508, p = 0.010). These data suggest that the therapeutical effect of remdesivir in COVID-19 pneumonia may be potentiated by azithromycin. The association between the two drugs should be further investigated.

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.

Stress Echo 2030: The Novel ABCDE-(FGLPR) Protocol to Define the Future of Imaging.

With stress echo (SE) 2020 study, a new standard of practice in stress imaging was developed and disseminated: the ABCDE protocol for functional testing within and beyond CAD. ABCDE protocol was the fruit of SE 2020, and is the seed of SE 2030, which is articulated in 12 projects: 1-SE in coronary artery disease (SECAD); 2-SE in diastolic heart failure (SEDIA); 3-SE in hypertrophic cardiomyopathy (SEHCA); 4-SE post-chest radiotherapy and chemotherapy (SERA); 5-Artificial intelligence SE evaluation (AI-SEE); 6-Environmental stress echocardiography and air pollution (ESTER); 7-SE in repaired Tetralogy of Fallot (SETOF); 8-SE in post-COVID-19 (SECOV); 9: Recovery by stress echo of conventionally unfit donor good hearts (RESURGE); 10-SE for mitral ischemic regurgitation (SEMIR); 11-SE in valvular heart disease (SEVA); 12-SE for coronary vasospasm (SESPASM). The study aims to recruit in the next 5 years (2021-2025) ≥10,000 patients followed for ≥5 years (up to 2030) from ≥20 quality-controlled laboratories from ≥10 countries. In this COVID-19 era of sustainable health care delivery, SE2030 will provide the evidence to finally recommend SE as the optimal and versatile imaging modality for functional testing anywhere, any time, and in any patient.

Deep vein thrombosis in COVID-19 patients in general wards: prevalence and association with clinical and laboratory variables.

BACKGROUND: Preliminary reports suggest a hypercoagulable state in COVID-19. Deep vein thrombosis (DVT) is perceived as a frequent finding in hospitalized COVID-19 patients, but data describing the prevalence of DVT are lacking. OBJECTIVES: We aimed to report the prevalence of DVT in COVID-19 patients in general wards, blinded to symptoms/signs of disease, using lower extremities duplex ultrasound (LEDUS) in random patients. We tested the association of DVT with clinical, laboratory and inflammatory markers and also reported on the secondary endpoint of in-hospital mortality. PATIENTS/METHODS: n  = 263 COVID-19 patients were screened with LEDUS between March 01, 2020 and April 05, 2020 out of the overall n = 1012 admitted with COVID-19. RESULTS: DVT was detected in n = 67 screened patients (25.5%), n = 41 patients (15.6%) died during the index hospitalization. Multiple logistic regression demonstrated that only C-reactive protein (odds ratio 1.009, 95% CI 1.004-1.013, p < 0.001) was independently associated with the presence of DVT at LEDUS. Both age (odds ratio 1.101, 95% CI 1.054-1.150, p < 0.001) and C-reactive protein (odds ratio 1.012, 95% CI 1.006-1.018, p < 0.001) were instead significantly independently associated with in-hospital mortality. CONCLUSIONS: The main study finding is that DVT prevalence in COVID-19 patients admitted to general wards is 25.5%, suggesting it may be reasonable to screen COVID-19 patients for this potentially severe but treatable complication, and that inflammation, measured with serum C-reactive protein, is the main variable associated with the presence of DVT, where all other clinical or laboratory variables, age or D-dimer included, are instead not independently associated with DVT.