Tocilizumab in Giant Cell Arteritis: A Real-Life Retrospective Study

This study aims to evaluate (1) the efficacy and safety of tocilizumab (TCZ) as a steroid-sparing agent in patients with giant cell arteritis (GCA) and (2) the usefulness of 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) in the follow-up and to detect disease activity. We retrospectively evaluated 12 patients with GCA treated with TCZ (8 mg/kg/mo). Pre- and posttherapy data about clinical signs and symptoms, laboratory results, FDG-PET imaging study, and the mean glucocorticoid (GC) dose were used to assess disease activity. Tocilizumab achieved complete disease remission in all patients. Mean FDG-PET-detected standard uptake value decreased from 2.05 ± 0.64 to 1.78 ± 0.45 ( P = .005). In 2 patients in whom temporal arteries color Doppler sonography examination was consistent with temporal arteritis, the hypoechoic halo disappeared after TCZ treatment. Mean GC dose was tapered from 26.6 ± 13.4 mg/d to 3.3 ± 3.1 mg/d ( P &lt; .0001). One-half of the patients discontinued GC therapy. Three patients experienced severe adverse reactions and had to stop TCZ therapy. In accordance with previous reports, TCZ is an effective steroid-sparing agent for GCA, although careful monitoring of adverse drug reactions is needed. 18F-fluorodeoxyglucose positron emission tomography could be used to monitor disease activity in TCZ-treated patients, but prospective studies are needed to confirm these data. </jats:p

Scientific research in the Federal Republic of Germany : essays on the constitutional, administrative and financial problems /

With a translation of the Federal framework act for higher edocation and other text

Who Is at Higher Risk of SARS-CoV-2 Reinfection? Results from a Northern Region of Italy

The SARS-CoV-2 pandemic continues to spread worldwide, generating a high impact on healthcare systems. The aim of the study was to examine the epidemiological burden of SARS-CoV-2 reinfections and to identify potential related risk factors. A retrospective observational study was conducted in Liguria Region, combining data from National Vaccines Registry and Regional Chronic Condition Data Warehouse. In the study period (September 2021 to May 2022), 335,117 cases of SARS-CoV-2 infection were recorded in Liguria, of which 15,715 were reinfected once. During the Omicron phase (which predominated from 3 January 2022), the risk of reinfection was 4.89 times higher (p &lt; 0.001) than during the Delta phase. Unvaccinated and vaccinated individuals with at least one dose for more than 120 days were at increased risk of reinfection compared with vaccinated individuals with at least one dose for &le;120 days, respectively (odds ratio (OR) of 1.26, p &lt; 0.001; OR of 1.18, p &lt; 0.001). Healthcare workers were more than twice as likely to be reinfected than non-healthcare workers (OR of 2.38, p &lt; 0.001). Lower ORs were seen among people aged 60 to 79 years. Two doses or more of vaccination were found to be protective against the risk of reinfection rather than a single dose (mRNA vaccines: OR of 0.06, p &lt; 0.0001, and OR of 0.1, p &lt; 0.0001; vector vaccines: OR of 0.05, p &lt; 0.0001). Patients with chronic renal failure, cardiovascular disease, bronchopneumopathy, neuropathy and autoimmune diseases were at increased risk of reinfection (OR of 1.38, p = 0.0003; OR of 1.09, p &lt; 0.0296; OR of 1.14, p = 0.0056; OR of 1.78, p &lt; 0.0001; OR of 1.18, p = 0.0205). Estimating the epidemiological burden of SARS-CoV-2 reinfections and the role played by risk factors in reinfections is relevant for identifying risk-based preventive strategies in a pandemic context characterized by a high circulation of the virus and a high rate of pathogen mutations

External validation of unsupervised COVID-19 clinical phenotypes and their prognostic impact

AbstractBackground Hospitalized patients with coronavirus disease 2019 (COVID-19) can be classified into different clinical phenotypes based on their demographic, clinical, radiology, and laboratory features. We aimed to validate in an external cohort of hospitalized COVID-19 patients the prognostic value of a previously described phenotyping system (FEN-COVID-19) and to assess the reproducibility of phenotypes development as a secondary analysis.Methods Patients were classified in phenotypes A, B or C according to the severity of oxygenation impairment, inflammatory response, hemodynamic and laboratory tests according to the FEN-COVID-19 method.Results Overall, 992 patients were included in the study, and 181 (18%), 757 (76%) and 54 (6%) of them were assigned to the FEN-COVID-19 phenotypes A, B, and C, respectively. An association with mortality was observed for phenotype C vs. A (hazard ratio [HR] 3.10, 95% confidence interval [CI] 1.81–5.30, p < 0.001) and for phenotype C vs. B (HR 2.20, 95% CI 1.50–3.23, p < 0.001). A non-statistically significant trend towards higher mortality was also observed for phenotype B vs. A (HR 1.41; 95% CI 0.92–2.15, p = 0.115). By means of cluster analysis, three different phenotypes were also identified in our cohort, with an overall similar gradient in terms of prognostic impact to that observed when patients were assigned to FEN-COVID-19 phenotypes.Conclusions The prognostic impact of FEN-COVID-19 phenotypes was confirmed in our external cohort, although with less difference in mortality between phenotypes A and B than in the original study

External validation of unsupervised COVID-19 clinical phenotypes and their prognostic impact

Hospitalized patients with coronavirus disease 2019 (COVID-19) can be classified into different clinical phenotypes based on their demographic, clinical, radiology, and laboratory features. We aimed to validate in an external cohort of hospitalized COVID-19 patients the prognostic value of a previously described phenotyping system (FEN-COVID-19) and to assess the reproducibility of phenotypes development as a secondary analysis. Patients were classified in phenotypes A, B or C according to the severity of oxygenation impairment, inflammatory response, hemodynamic and laboratory tests according to the FEN-COVID-19 method. Overall, 992 patients were included in the study, and 181 (18%), 757 (76%) and 54 (6%) of them were assigned to the FEN-COVID-19 phenotypes A, B, and C, respectively. An association with mortality was observed for phenotype C vs. A (hazard ratio [HR] 3.10, 95% confidence interval [CI] 1.81–5.30, p p p = 0.115). By means of cluster analysis, three different phenotypes were also identified in our cohort, with an overall similar gradient in terms of prognostic impact to that observed when patients were assigned to FEN-COVID-19 phenotypes. The prognostic impact of FEN-COVID-19 phenotypes was confirmed in our external cohort, although with less difference in mortality between phenotypes A and B than in the original study. Hospitalized patients with COVID-19 can be classified into different clinical phenotypes based on their demographic, clinical, radiology, and laboratory featuresIn this study, we externally confirmed the prognostic impact of clinical phenotypes previously identified by Gutierrez-Gutierrez and colleagues in a Spanish cohort of hospitalized patients with COVID-19, and the usefulness of their simplified probabilistic model for phenotypes assignmentThis could indirectly support the validity of both phenotype’s development and their extrapolation to other hospitals and countries for management decisions during other possible future viral pandemics Hospitalized patients with COVID-19 can be classified into different clinical phenotypes based on their demographic, clinical, radiology, and laboratory features In this study, we externally confirmed the prognostic impact of clinical phenotypes previously identified by Gutierrez-Gutierrez and colleagues in a Spanish cohort of hospitalized patients with COVID-19, and the usefulness of their simplified probabilistic model for phenotypes assignment This could indirectly support the validity of both phenotype’s development and their extrapolation to other hospitals and countries for management decisions during other possible future viral pandemics</p

Clinical characteristics, management and in-hospital mortality of patients with COVID-19 In Genoa, Italy

To describe clinical characteristics, management and outcome of COVID-19 patients; and to evaluate risk factors for all-cause in-hospital mortality