Background: Several risk factors have been identified to predict worse outcomes in patients affected by SARS-CoV-2 infection. Machine learning algorithms represent a novel approach to identifying a prediction model with a good discriminatory capacity to be easily used in clinical practice. The aim of this study was to obtain a risk score for in-hospital mortality in patients with coronavirus disease infection (COVID-19) based on a limited number of features collected at hospital admission.
View Article and Find Full Text PDFIntroduction: The role of sex compared to comorbidities and other prognostic variables in patients with coronavirus disease (COVID-19) is unclear.
Methods: This is a retrospective observational study on patients with COVID-19 infection, referred to 13 cardiology units. The primary objective was to assess the difference in risk of death between the sexes.
Aims: Myocardial injury (MI) in coronavirus disease-19 (COVID-19) is quite prevalent at admission and affects prognosis. Little is known about troponin trajectories and their prognostic role. We aimed to describe the early in-hospital evolution of MI and its prognostic impact.
View Article and Find Full Text PDFAims: To assess the prognostic value of a history of heart failure (HF) in patients with coronavirus disease 2019 (COVID-19).
Methods And Results: We enrolled 692 consecutive patients admitted for COVID-19 in 13 Italian cardiology centres between 1 March and 9 April 2020. Mean age was 67.