Association between pulmonary embolism and COVID-19 disease.

OBJECTIVES: The current retrospective study focused on evaluation of the relationship between pulmonary embolism during COVID-19 pandemic and demographic, presenting symptoms, comorbidities and laboratory results in patients who underwent CT angiography of the pulmonary arteries. METHODS: The study enrolled all adult patients with suspected acute pulmonary embolism (PE) who underwent computed tomography pulmonary angiography (CTPA) between March 1, 2020, and April 30, 2022, during the SARS-CoV-2 pandemic. 1698 CTPAs were reviewed and various data were collected. Based on examination results, patients were divided into 4 groups: a group with positive PE and a group with negative PE for both COVID-19 and non-COVID-19 patients. RESULTS: When comparing different predictors of COVID-19 patients and non-COVID-19 patients we noticed lower probability of PE in female gender (OR 0.77, 95% CI: 0.60-1.00, p = 0.052) and in chronic obstructive pulmonary disease (COPD) patients (OR 0.6, 95% CI: 0.38-0.90, p = 0.017). Higher probability of PE was in cases of older age (OR 1.02, 95% CI: 1.01-1.02, p < 0.001), increased heart rate (OR 1.01, 95% CI: 1.01-1.02, p < 0.001) and increased D-dimer levels (OR 1.03, 95% CI: 1.02-1.04, p < 0.001). CONCLUSION: Considering predictors of PE there was a significantly lower risk of PE in the female gender and COPD, and a higher risk with increasing age, heart rate, and D-dimer levels.

Artifact Detection in Lung Ultrasound: An Analytical Approach

Lung ultrasound is used to detect various artifacts in the lungs that support the diagnosis of different conditions. There is ongoing research to support the automatic detection of such artifacts using machine learning. We propose a solution that uses analytical computer vision methods to detect two types of lung artifacts, namely A- and B-lines. We evaluate the proposed approach on the POCUS dataset and data acquired from a hospital. We show that by using the Fourier transform, we can analyze lung ultrasound images in real-time and classify videos with an accuracy above 70%. We also evaluate the method's applicability for segmentation, showcasing its high success rate for B-lines (89% accuracy) and its shortcomings for A-line detection. We then propose a hybrid solution that uses a combination of neural networks and analytical methods to increase accuracy in horizontal line detection, emphasizing the pleura.

Comparison of SARS-CoV-2 Detection by Rapid Antigen and by Three Commercial RT-qPCR Tests: A Study from Martin University Hospital in Slovakia.

The global pandemic of coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is having a tremendous impact on the global economy, health care systems and the lives of almost all people in the world. The Central European country of Slovakia reached one of the highest daily mortality rates per 100,000 inhabitants in the first 3 months of 2021, despite implementing strong prophylactic measures, lockdowns and repeated nationwide antigen testing. The present study reports a comparison of the performance of the Standard Q COVID-19 antigen test (SD Biosensor) with three commercial RT-qPCR kits (vDetect COVID-19-MultiplexDX, gb SARS-CoV-2 Multiplex-GENERI BIOTECH Ltd. and Genvinset COVID-19 [E]-BDR Diagnostics) in the detection of infected individuals among employees of the Martin University Hospital in Slovakia. Health care providers, such as doctors and nurses, are classified as "critical infrastructure", and there is no doubt about the huge impact that incorrect results could have on patients. Out of 1231 samples, 14 were evaluated as positive for SARS-CoV-2 antigen presence, and all of them were confirmed by RT-qPCR kit 1 and kit 2. As another 26 samples had a signal in the E gene, these 40 samples were re-isolated and subsequently re-analysed using the three kits, which detected the virus in 22, 23 and 12 cases, respectively. The results point to a divergence not only between antigen and RT-qPCR tests, but also within the "gold standard" RT-qPCR testing. Performance analysis of the diagnostic antigen test showed the positive predictive value (PPV) to be 100% and negative predictive value (NPV) to be 98.10%, indicating that 1.90% of individuals with a negative result were, in fact, positive. If these data are extrapolated to the national level, where the mean daily number of antigen tests was 250,000 in April 2021, it points to over 4700 people per day being misinterpreted and posing a risk of virus shedding. While mean Ct values of the samples that were both antigen and RT-qPCR positive were about 20 (kit 1: 20.47 and 20.16 for Sarbeco E and RdRP, kit 2: 19.37 and 19.99 for Sarbeco E and RdRP and kit 3: 17.47 for ORF1b/RdRP), mean Ct values of the samples that were antigen-negative but RT-qPCR-positive were about 30 (kit 1: 30.67 and 30.00 for Sarbeco E and RdRP, kit 2: 29.86 and 31.01 for Sarbeco E and RdRP and kit 3: 27.47 for ORF1b/RdRP). It confirms the advantage of antigen test in detecting the most infectious individuals with a higher viral load. However, the reporting of Ct values is still a matter of ongoing debates and should not be conducted without normalisation to standardised controls of known concentration.