Subcutaneous adipose tissue radiodensity: An emerging risk factor for severe COVID-19.

Background: Adipose tissue radiodensity and metabolic activity may influence COVID-19 outcomes. This study evaluated the association between adipose tissue characteristics and clinical outcomes in COVID-19 patients.

Methods: Two retrospective cohorts of hospitalized COVID-19 patients were analyzed.

Construction of a nomogram for predicting COVID-19 in-hospital mortality: A machine learning analysis.

Background And Objectives: We aim to verify the use of ML algorithms to predict patient outcome using a relatively small dataset and to create a nomogram to assess in-hospital mortality of patients with COVID-19.

Methods: A database of 200 COVID-19 patients admitted to the Clinical Hospital of State University of Campinas (UNICAMP) was used in this analysis. Patient features were divided into three categories: clinical, chest abnormalities, and body composition characteristics acquired by computerized tomography.

Low skeletal muscle radiodensity and neutrophil-to-lymphocyte ratio as predictors of poor outcome in patients with COVID-19.

Inflammatory states and body composition changes are associated with a poor prognosis in many diseases, but their role in coronavirus disease 2019 (COVID-19) is not fully understood. To assess the impact of low skeletal muscle radiodensity (SMD), high neutrophil-to-lymphocyte ratio (NLR) and a composite score based on both variables, on complications, use of ventilatory support, and survival in patients with COVID-19. Medical records of patients hospitalized between May 1, 2020, and July 31, 2020, with a laboratory diagnosis of COVID-19 who underwent computed tomography (CT) were retrospectively reviewed.