The aim of our study was to recognize coagulase-negative staphylococci (CNS) in the air of operating theatres. Out of the identification of 449 isolates, the most frequent species were Staphylococcus epidermidis and Staphylococcus haemolyticus. The strains were adherent to glass in 52.3%. Most of the S. epidermidis showed adherent growth, while the majority of the S. haemolyticus failed to adhere. The disk-diffusion antibiotic sensitivity tests showed great differences in sensitivity to penicillin, tetracycline and erythromycin between adherence-positive and negative isolates. On the whole, the species S. haemolyticus proved to be much more resistant than S. epidermidis. Staphylococcus warneri was the most, while S. haemolyticus was the least sensitive to phages.
Several COVID-19 vaccines have been approved. The mRNA vaccine from Pfizer/BioNTech (Comirnaty, BNT162b2; BNT) and the vector vaccine from AstraZeneca (Vaxzevria, ChAdOx1; AZ) have been widely used. mRNA vaccines induce high antibody and T cell responses, also to SARS-CoV-2 variants, but are costlier and less stable than the slightly less effective vector vaccines. For vector vaccines, heterologous vaccination schedules have generally proven more effective than homologous schedules.
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causing coronavirus disease 2019 (COVID-19) is currently finally determined in laboratory settings by real-time reverse-transcription polymerase-chain-reaction (rt-PCR). However, simple testing with immediately available results are crucial to gain control over COVID-19. The aim was to evaluate such a point-of-care antigen rapid test (AG-rt) device in its performance compared to laboratory-based rt-PCR testing in COVID-19 suspected, symptomatic patients.For this prospective study, two specimens each of 541 symptomatic female (54.7%) and male (45.3%) patients aged between 18 and 95 years tested at five emergency departments (ED, n = 296) and four primary healthcare centres (PHC, n = 245), were compared, using AG-rt (positive/negative/invalid) and rt-PCR (positive/negative and cycle threshold, Ct) to diagnose SARS-CoV-2. Diagnostic accuracy, sensitivity, specificity, positive predictive values (PPV), negative predictive value (NPV), and likelihood ratios (LR+/-) of the AG-rt were assessed.Differences between ED and PHC were detected regarding gender, age, symptoms, disease prevalence, and diagnostic performance. Overall, 174 (32.2%) were tested positive on AG-rt and 213 (39.4%) on rt-PCR. AG correctly classified 91.7% of all rt-PCR positive cases with a sensitivity of 80.3%, specificity of 99.1%, PPV of 98.3, NPV of 88.6%, LR(+) of 87.8, and LR(-) of 0.20. The highest sensitivities and specificities of AG-rt were detected in PHC (sensitivity: 84.4%, specificity: 100.0%), when using Ct of 30 as cut-off (sensitivity: 92.5%, specificity: 97.8%), and when symptom onset was within the first three days (sensitivity: 82.9%, specificity: 99.6%).The highest sensitivity was detected with a high viral load. Our findings suggest that AG-rt are comparable to rt-PCR to diagnose SARS-CoV-2 in COVID-19 suspected symptomatic patients presenting both at emergency departments and primary health care centres.
ObjectivesThe real-world effectiveness of the oral antivirals nirmatrelvir-ritonavir and molnupiravir against the SARS-CoV-2 Omicron variant remains uncertain. We aimed to estimate their effectiveness in non-hospitalised adults with Covid-19.MethodsThis retrospective cohort study used data from the Municipal Department for Public Health Services of Vienna, Austria, to identify non-hospitalised adults with confirmed SARS-CoV-2 infection between Jan-2022–May-2023. Nirmatrelvir-ritonavir users were compared with untreated controls and molnupiravir users with untreated controls by calculating adjusted risk differences (aRDs) using a covariate-adjusted logistic regression model with inverse probability weighting. Outcomes were hospitalisation and all-cause death within 28 days.ResultsWe identified 113,399 eligible cases (90,481 untreated controls, 12,166 nirmatrelvir-ritonavir users, and 10,752 molnupiravir users). Over 96% of the patients were immunised by previous infection or vaccination. In the nirmatrelvir-ritonavir analysis, the estimated risk of hospitalisation was 0.57% (95%CI, 0.35–0.78) in nirmatrelvir-ritonavir users and 1.09% (95%CI, 0.86–1.32) in untreated controls (aRD -0.53%; 95%CI, -0.77–-0.28). The estimated risk of death was 0.0% (95%CI, 0.0–0.0) in nirmatrelvir-ritonavir users and 0.13% (95%CI, 0.08–0.18) in untreated controls (aRD -0.13%, 95%CI, -0.18–-0.08). The number needed to treat to prevent hospitalisation and death was 190 (95%CI, 130–356) and 792 (95%CI, 571–1289), respectively. These statistically significant aRDs were restricted to the subgroup of patients ≥60 years. In the molnupiravir analysis, the estimated risk of hospitalisation was 1.36% (95%CI, 0.95–1.77) in molnupiravir users and 1.16% (95%CI, 0.93–1.39) in untreated controls (aRD 0.2%; 95%CI, -0.08–0.49). The estimated risk of death was 0.12% (95%CI, 0.01–0.23) in molnupiravir users and 0.14% (95%CI, 0.06–0.21) in untreated controls (aRD, -0.01%; 95%CI, -0.08–-0.06).ConclusionsAmong outpatients aged ≥60 years with Covid-19 in an Omicron-dominated era, treatment with nirmatrelvir-ritonavir was associated with a lower risk of hospitalisation and all-cause death within 28 days, albeit with wide confidence intervals and high numbers needed to treat. This finding was not observed in molnupiravir users and younger nirmatrelvir-ritonavir users.
COVID-19 infections are accompanied by adverse changes in inflammatory pathways that are also partly influenced by increased oxidative stress and might result in elevated DNA damage. The aim of this case-control study was to examine whether COVID-19 patients show differences in oxidative stress-related markers, unconjugated bilirubin (UCB), an inflammation panel and DNA damage compared to healthy, age-and sex-matched controls. The Comet assay with and without the treatment of formamidopyrimidine DNA glycosylase (FPG) and H2O2 challenge was used to detect DNA damage in whole blood. qPCR was applied for gene expression, UCB was analyzed via HPLC, targeted proteomics were applied using Olink® inflammation panel and various oxidative stress as well as clinical biochemistry markers were analyzed in plasma. Hospitalized COVID-19 patients (n = 48) demonstrated higher serum levels of 55 inflammatory proteins (p < 0.001), including hs-C-reactive protein levels (p < 0.05), compared to healthy controls (n = 48). Interestingly, significantly increased age-related DNA damage (%-DNA in tail) after formamidopyrimidine DNA glycosylase (FPG) treatment was measured in younger (n = 24, average age 55.7 years; p < 0.05) but not in older COVID-19 patients (n = 24, average age 83.5 years; p > 0.05). Although various oxidative stress markers were not altered (e.g., FRAP, malondialdehyde, p > 0.05), a significant increased ratio of oxidized to reduced glutathione was detected in COVID-19 patients compared to healthy controls (p < 0.05). UCB levels were significantly lower in individuals with COVID-19, especially in younger COVID-19 patients (p < 0.05). These results suggest that COVID-19 infections exert effects on DNA damage related to age in hospitalized COVID-19 patients that might be driven by changes in inflammatory pathways but are not altered by oxidative stress parameters.
Abstract Standard blood laboratory parameters may have diagnostic potential, if polymerase-chain-reaction (PCR) tests are not available on time. We evaluated standard blood laboratory parameters of 655 COVID-19 patients suspected to be infected with SARS-CoV-2, who underwent PCR testing in one of five hospitals in Vienna, Austria. We compared laboratory parameters, clinical characteristics, and outcomes between positive and negative PCR-tested patients and evaluated the ability of those parameters to distinguish between groups. Of the 590 patients (20-100years, 276 females and 314 males), 208 were PCR-positive. Positive compared to negative PCR-tested patients had significantly lower levels of leukocytes, neutrophils, basophils, eosinophils, lymphocytes, neutrophil-to-lymphocyte ratio, monocytes, and thrombocytes; while significantly higher levels were detected with erythrocytes, hemoglobin, hematocrit, C-reactive-protein, ferritin, activated-partial-thromboplastin-time, alanine-aminotransferase, aspartate-aminotransferase, lipase, creatine-kinase, and lactate-dehydrogenase. From all blood parameters, eosinophils, ferritin, leukocytes, and erythrocytes showed the highest ability to distinguish between COVID-19 positive and negative patients (area-under-curve: 72.3-79.4%). Leukopenia, eosinopenia, elevated erythrocytes, and hemoglobin were among the strongest markers regarding accuracy, sensitivity, specificity, positive and negative predictive value, positive and negative likelihood ratio, and post-test probabilities. Our findings suggest that especially leukopenia, eosinopenia, as well as elevated erythrocytes, hemoglobin, and ferritin are helpful to distinguish between COVID-19 positive and negative tested patients.
ABSTRACT Background Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is current pandemic disease. Acute polymerase-chain-reaction is the gold standard test for this disease, is not available everywhere. Standard blood laboratory parameters may have diagnostic potential. Methods We evaluated standard blood laboratory parameters of 655 COVID-19 patients suspected to be infected with SARS-CoV-2, who underwent PCR testing in one of five hospitals in Vienna, Austria. Additionally, clinical characteristics and 28-day outcome were obtained from medical records. We compared standard blood laboratory parameters, clinical characteristics, and outcomes between positive and negative PCR-tested patients and evaluated the ability of those parameters to distinguish between groups. Results Of the 590 study patients including 276 females and 314 males, aged between 20 and 100 years, 208 were tested positive by means of PCR. Patients with positive compared to negative PCR-tests had significantly lower levels of leukocytes, basophils, eosinophils, monocytes, and thrombocytes; while significantly higher levels were detected with hemoglobin, C-reactive-protein (CRP), neutrophil-to-lymphocyte ratio (NLR), activated-partial-thromboplastin-time (aPTT), creatine-kinase (CK), lactate-dehydrogenase (LDH), alanine-aminotransferase (ALT), aspartate-aminotransferase (AST), and lipase. Our multivariate model correctly classified 83.9% of cases with a sensitivity of 78.4%, specificity of 87.3%, positive predictive value of 79.5%, and negative predictive value of 86.6%. Decreasing leucocytes and eosinophils and increasing hemoglobin and CRP were significantly associated with an increased likelihood of being COVID-19 positive tested. Conclusions Our findings suggest that especially leucocytes, eosinophils, hemoglobin, and CRP are helpful to distinguish between COVID-19 positive and negative tested patients and that a certain blood pattern is able to predict PCR-results. Summary Decreasing leucocytes and eosinophils and increasing hemoglobin and CRP were significantly associated with an increased likelihood of being COVID-19 positive tested. Each single parameter showed either a high sensitivity (leucocytes, eosinophils, CRP, monocytes, thrombocytes) or specificity (NLR, CK, ALT, lipase), or a sensitivity and specificity around 60% (Hb, LDH, AST).
Abstract Standard blood laboratory parameters may have diagnostic potential, if polymerase-chain-reaction (PCR) tests are not available on time. We evaluated standard blood laboratory parameters of 655 COVID-19 patients suspected to be infected with SARS-CoV-2, who underwent PCR testing in one of five hospitals in Vienna, Austria. We compared laboratory parameters, clinical characteristics, and outcomes between positive and negative PCR-tested patients and evaluated the ability of those parameters to distinguish between groups. Of the 590 patients (20–100 years, 276 females and 314 males), 208 were PCR-positive. Positive compared to negative PCR-tested patients had significantly lower levels of leukocytes, neutrophils, basophils, eosinophils, lymphocytes, neutrophil-to-lymphocyte ratio, monocytes, and thrombocytes; while significantly higher levels were detected with erythrocytes, hemoglobin, hematocrit, C-reactive-protein, ferritin, activated-partial-thromboplastin-time, alanine-aminotransferase, aspartate-aminotransferase, lipase, creatine-kinase, and lactate-dehydrogenase. From all blood parameters, eosinophils, ferritin, leukocytes, and erythrocytes showed the highest ability to distinguish between COVID-19 positive and negative patients (area-under-curve, AUC: 72.3–79.4%). The AUC of our model was 0.915 (95% confidence intervals, 0.876–0.955). Leukopenia, eosinopenia, elevated erythrocytes, and hemoglobin were among the strongest markers regarding accuracy, sensitivity, specificity, positive and negative predictive value, positive and negative likelihood ratio, and post-test probabilities. Our findings suggest that especially leukopenia, eosinopenia, and elevated hemoglobin are helpful to distinguish between COVID-19 positive and negative tested patients.
