0000000000871197
AUTHOR
Alexis Elias Malavazos
The three facets of the SARS-CoV-2 pandemic during the first two waves in the northern, central, and southern Italy
Background: There is a scarcity of information in literature regarding the clinical differences and comorbidities of patients affected by Coronavirus disease 2019 (COVID-19), which could clarify the different prevalence of the outcomes (composite and only death) between several Italian regions. Objective: This study aimed to assess the heterogeneity of clinical features of patients with COVID-19 upon hospital admission and disease outcomes in the northern, central, and southern Italian regions. Methods: An observational cohort multicenter retrospective study including 1210 patients who were admitted for COVID-19 in Infectious diseases, Pulmonology, Endocrinology, Geriatrics and Internal Med…
Machine Learning to Predict In-Hospital Mortality in COVID-19 Patients Using Computed Tomography-Derived Pulmonary and Vascular Features
Pulmonary parenchymal and vascular damage are frequently reported in COVID-19 patients and can be assessed with unenhanced chest computed tomography (CT), widely used as a triaging exam. Integrating clinical data, chest CT features, and CT-derived vascular metrics, we aimed to build a predictive model of in-hospital mortality using univariate analysis (Mann–Whitney U test) and machine learning models (support vectors machines (SVM) and multilayer perceptrons (MLP)). Patients with RT-PCR-confirmed SARS-CoV-2 infection and unenhanced chest CT performed on emergency department admission were included after retrieving their outcome (discharge or death), with an 85/15% training/test dataset spli…