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A 33yo man, ex-convict, with a history of IV substance abuse, without previous cardiological history, was admitted at our emergency department in the clinical context of an acute pericarditis, intermittent fever in the last 10 days and non-itchy maculo-papular erythema of palms and thorax. EKG documented a diffuse ST-segment elevation, chest X-rays revealed a bilateral pleural effusion and echocardiography documented a normal biventricular morphology and function, normal valvular function, and a circumferential pericardial effusion (14mm). The patient was admitted at our Cardiology Unit after a negative PCR SARS-CoV-2 test. Blood chemistry showed elevated WBC count with an important neutrophilia (24.000/mm3;90% neutrophils), elevated TnI-HS (236 ng/L;n.v. <18 ng/L), elevated C-reactive protein (340 mg/L;n.v. < 5mg/L). A classic anti-inflammatory therapy was set up with indomethacin + colchicine with little clinical benefit. Blood cultures, bacterial serology (i.e., Tubercolosis, T.gondii, Syphilis, Leptospirosis) and viral serology (i.e., HIV, HCV, HBV, EBV, HSV) resulted negative. However, an empirical antibiotic coverage was set up: sequentially with Piperacillin/Tazobactam, Vancomycin, Linezolid and Ceftriaxone. None of the antibiotics improved patient's clinical status. On the contrary, the patient developed an allergic reaction to Vancomycin and Linezolid. All autoantibodies tested resulted negative. After 10 days of hospitalization, the patient's clinical status continued to deteriorate. The intermittent fever (max 41degreeC) was not responsive to any treatment. The evanescent skin rash had spread to the whole body and was concomitant with the fever peaks. The indexes of inflammation were rising (C-reactive protein 400 mg/ L) and so were the WBC count (36.000/mm3). The patient lost weight (-8kg), developed hand and feet arthralgia, diffuse myalgia, painful retronucal lymph nodes, pharyngodynia and abdominal pain. An abdominal echography and CT were performed with evidence of mild abdominal effusion and splenomegaly. Hepatic cytolysis indices began to rise (AST 100 U/L;ALT 150 U/L;LDH 385 U/L). At that point, on the basis of Yamaguchi's Criteria, we suspected our patient could be affected by Adult-onset Still's disease (AOSD) with an initial stage of hemophagocytic lymphohistiocytosis (HLH). Prednisone (IV 2 mg/kg/die) was administered for 72h with an incomplete clinical and biochemical response (C-reactive Protein 180 mg/L). Subsequently, Anakinra (SC 100 mg/die) was administered with a complete clinical response in less than 72h. AOSD is very uncommon. The annual incidence is 0.16/100.000 with an equal distribution between sexes. HLH have been observed in 15% of patients, meanwhile myopericarditis is a rare complication. Although rare, it is fair to know and consider AOSD in the complicated and tricky diagnostic process of myopericarditis. A noteworthy point of this case report is the extreme efficacy of Anakinra in contexts of systemic inflammation and myopericarditis. A point still to be clarified concerns the duration of the treatment and the down-titration of Anakinra in these complicated contexts..
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Vaccine-associated myocarditis and pericarditis usually develop within 14 days of COVID-19 vaccination, are exceptionally rare, manifest with mild clinical pictures and are commonly characterized by a favorable evolution. Young men inoculated with two doses of an mRNA vaccine are the subgroup at higher risk. Recent epidemiological studies evaluated the incidence and risk of vaccine-associated myocarditis and pericarditis among men and women, in different ranges of age and specific types of vaccines. Long-term population analyses demonstrated that the cardiovascular risk conferred by COVID-19 extends beyond the acute phase, representing the rationale for implementing prevention strategies for SARS-CoV-2 infection, monitoring specific populations at higher risk and pursuing the completion of the vaccination campaign. This document provides an update on the most recent scientific evidence and critical interpretation of available data in constant evolution towards personalized strategies of immunization. © 2022 Il Pensiero Scientifico Editore s.r.l.. All rights reserved.
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Aims: Several risk factors have been identified to predict worse outcomes in patients affected by SARS-CoV-2 infection. Prediction models are needed to optimize clinical management and to early stratify patients at a higher mortality risk. Machine learning (ML) algorithms represent a novel approach to identify a prediction model with a good discriminatory capacity to be easily used in clinical practice. Methods and results: The Cardio-COVID is a multicentre observational study that involved a cohort of consecutive adult Caucasian patients with laboratory-confirmed COVID-19 [by real time reverse transcriptase-polymerase chain reaction (RT-PCR)] who were hospitalized in 13 Italian cardiology units from 1 March to 9 April 2020. Patients were followed-up after the COVID-19 diagnosis and all causes in-hospital mortality or discharge were ascertained until 23 April 2020. Variables with more than 20% of missing values were excluded. The Lasso procedure was used with a λ=0.07 for reducing the covariates number. Mortality was estimated by means of a Random Forest (RF). The dataset was randomly divided in two subsamples with the same percentage of death/alive people of the entire sample: training set contained 80% of the data and test set the remaining 20%. The training set was used in the calibration procedure where a RF models in-hospital mortality with the covariates selected by Lasso. Its accuracy was measured by means of the ROC curve, obtaining AUC, sensitivity, specificity, and related 95% confidence interval (CI) computed with 10 000 stratified bootstrap replicates. From the RF the relative Variable Importance Measure (relVIM) was extracted to understand which of the selected variables had the greatest impact on outcome, providing a ranking from the most (relVIM=100) to the less important variable. The model obtained was compared with the Gradient Boosting Machine (GBM) and with the logistic regression, where the predictions were cross validated. Finally, to understand if each model has the same performance in sample (training) and out of sample (test), the two AUCs were compared by means of the DeLong's test. Among 701 patients enrolled (mean age 67.2±13.2 years, 69.5% males), 165 (23.5%) died during a median hospitalization of 15 (IQR, 9-24) days. Variables selected by the Lasso were: age, Oxygen saturation, PaO2/FiO2, Creatinine Clearance and elevated Troponin. Compared with those who survived, deceased patients were older, had a lower blood oxygenation, a lower creatinine clearance levels and higher prevalence of elevated Troponin (all P<0.001). Training set included 561 patients and test set 140 patients. The best performance out of sample was provided by the RF with an AUC of 0.78 (95% CI: 0.68-0.88) and a sensitivity of 0.88 (95% CI: 0.58-1.00). Moreover, RF is the unique methodology that provided similar performance in sample and out of sample (DeLong test P=0.78). On the contrary, prediction model was less accurate by using GBM and logistic regression. The relVIM ranked the variables from the most to the less important in predicting the outcome as follows: clearance creatinine, PaO2/FiO2, age, oxygen saturation, and elevated Troponin. Conclusions: In a large COVID-19 population, we showed that a customizable MLbased score derived from clinical variables, is feasible and effective for the prediction of in-hospital mortality.
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The coronavirus disease (COVID-19) pandemic has caused 2.69 million deaths and 122 million infections. Great efforts have been made worldwide to promptly develop effective vaccines and reduce morbidity and mortality rates from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Available vaccines have proven highly effective at preventing symptomatic disease in clinical trials and real-world reports and are playing an essential role in flattening the epidemiology curve and, mostly, in reducing COVID-19 hospitalizations. Some concerns have been raised after very rare cases of myocarditis and peri-carditis recently reported by the Centers for Disease Control and Prevention (CDC) as potentially associ-ated with COVID-19 mRNA vaccinations, namely the Pfizer-BioNTech mRNA vaccine (BNT162b2) and the Moderna mRNA vaccine (mRNA-1273). Therefore, the aim of this document is to explore the possible link between COVID-19 mRNA vaccination and the development of myocarditis and/or pericarditis by per-forming a critical analysis of available data and to provide indications for specific subgroups of individuals.