RESUMO
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causal agent of coronavirus disease 2019 (COVID-19), in which coagulation abnormalities and endothelial dysfunction play a key pathogenic role. Tissue factor (TF) expression is triggered by endothelial dysfunction. Activated factor VII-antithrombin (FVIIa-AT) complex reflects indirectly FVIIa-TF interaction and has been proposed as a potential biomarker of prothrombotic diathesis. FVIIa-AT plasma concentration was measured in 40 patients (30 males and 10 females; 64.8 ± 12.3 years) admitted with SARS-CoV-2 pneumonia during the first pandemic wave in Italy. Two sex- and age-matched cohorts without COVID-19, with or without signs of systemic inflammation, were used to compare FVIIa-AT data. The FVIIa-AT plasma levels in COVID-19 patients were higher than those in non-COVID-19 subjects, either with or without inflammation, while no difference was observed among non-COVID-19 subjects. The association between COVID-19 and FVIIa-AT levels remained significant after adjustment for sex, age, C-reactive protein, renal function, fibrinogen, prothrombin time and activated partial thromboplastin time. Our results indicate that SARS-CoV-2 infection, at least during the first pandemic wave, was characterized by high FVIIa-AT levels, which may suggest an enhanced FVIIa-TF interaction in COVID-19, potentially consistent with SARS-CoV-2-induced endotheliopathy.
RESUMO
Systemic sclerosis (SSc) is a rare autoimmune inflammatory rheumatic disease. The prevalence of SSc ranges from 7 to 700 cases per million worldwide. Due to multiple organ involvement and constant inflammatory state, this group of patients presents an increased risk of infectious diseases. This paper aimed to gather the up-to-date evidence on vaccination strategies for patients with SSc and to be a useful tool for the prevention and management of infectious diseases. The authors conducted a scoping review in which each paragraph presents data on a specific vaccine's safety, immunogenicity, and efficacy. The work deals with the following topics: SARS-CoV-2, seasonal influenza, S. pneumoniae, HAV, HBV, HZV, N. meningitidis, H. influenzae, HPV, and diphtheria-tetanus-pertussis.
RESUMO
INTRODUCTION: The performance of seven cardiovascular (CV) risk algorithms is evaluated in a multicentric cohort of ankylosing spondylitis (AS) patients. Performance and calibration of traditional CV predictors have been compared with the novel paradigm of machine learning (ML). METHODS: A retrospective analysis of prospectively collected data from an AS cohort has been performed. The primary outcome was the first CV event. The discriminatory ability of the algorithms was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC), which is like the concordance-statistic (c-statistic). Three ML techniques were considered to calculate the CV risk: support vector machine (SVM), random forest (RF), and k-nearest neighbor (KNN). RESULTS: Of 133 AS patients enrolled, 18 had a CV event. c-statistic scores of 0.71, 0.61, 0.66, 0.68, 0.66, 0.72, and 0.67 were found, respectively, for SCORE, CUORE, FRS, QRISK2, QRISK3, RRS, and ASSIGN. AUC values for the ML algorithms were: 0.70 for SVM, 0.73 for RF, and 0.64 for KNN. Feature analysis showed that C-reactive protein (CRP) has the highest importance, while SBP and hypertension treatment have lower importance. CONCLUSIONS: All of the evaluated CV risk algorithms exhibit a poor discriminative ability, except for RRS and SCORE, which showed a fair performance. For the first time, we demonstrated that AS patients do not show the traditional ones used by CV scores and that the most important variable is CRP. The present study contributes to a deeper understanding of CV risk in AS, allowing the development of innovative CV risk patient-specific models.
