Out-of-hospital mortality in coronary artery disease (CAD) is particularly high and established adverse event prediction tools are yet to be available. Our study aimed to investigate whether precision phenotyping can be performed using routine laboratory parameters for the prediction of out-of-hospital survival in a CAD population treated by percutaneous coronary intervention (PCI). All patients treated by PCI and discharged alive in a tertiary center between January 2016 - December 2022 that have been included prospectively in the local registry were analyzed.
View Article and Find Full Text PDFInfective endocarditis remains a condition associated with high morbidity and mortality, regardless of advances in diagnosis and therapeutics. The etiology, microbiology, and epidemiology of infective endocarditis have changed in the last years, with healthcare-associated infective endocarditis being responsible for a myriad of cases. is rarely the cause of infective endocarditis.
View Article and Find Full Text PDFBackground And Aims: Machine learning (ML) models have been proposed as a prognostic clinical tool and superiority over clinical risk scores is yet to be established. Our aim was to analyse the performance of predicting 3-year all-cause- and cardiovascular cause mortality using ML techniques and compare it with clinical scores in a percutaneous coronary intervention (PCI) population.
Methods: An all-comers patient population treated by PCI in a tertiary cardiovascular centre that have been included prospectively in the local registry between January 2016-December 2017 was analysed.
Malignant vascular tumors of the liver include rare primary hepatic mesenchymal tumors developed in the background of a normal liver parenchyma. Most of them are detected incidentally by the increased use of performing imaging techniques. Their diagnosis is challenging, involving clinical and imaging criteria, with final confirmation by histology and immunohistochemistry.
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