Modelling the long-term health impact of COVID-19 using Graphical Chain Models brief heading: long COVID prediction by graphical chain models.

ABSTRACT:

Background

Long-term sequelae of SARS-CoV-2 infection, namely long COVID syndrome, affect about 10% of severe COVID-19 survivors. This condition includes several physical symptoms and objective measures of organ dysfunction resulting from a complex interaction between individual predisposing factors and the acute manifestation of disease. We aimed at describing the complexity of the relationship between long COVID symptoms and their predictors in a population of survivors of hospitalization for severe COVID-19-related pneumonia using a Graphical Chain Model (GCM).

Methods

96 patients with severe COVID-19 hospitalized in a non-intensive ward at the "Santa Maria" University Hospital, Terni, Italy, were followed up at 3-6 months. Data regarding present and previous clinical sta

SUBMITTER: Gourgoura K 

PROVIDER: S-EPMC11360819 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature