ArticleBMC infectious diseases2024
Modelling the long-term health impact of COVID-19 using Graphical Chain Models brief heading: long COVID prediction by graphical chain models.
Article in BMC infectious diseases, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Correction: Modelling the long-term health impact of COVID-19 using Graphical Chain Models.BMC infectious diseases · 2024Article
Corrections and comments
- Erratum issued
Authors and funding
17 authors.
Funding
Abstract
backgroundLong-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).
methods96 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 status, drug treatment, findings recorded during the in-hospital phase, presence of symptoms and signs of organ damage at follow-up were collected. Static and dynamic cardiac and respiratory parameters were evaluated by resting pulmonary function test, echocardiography, high-resolution chest tomography (HRCT) and cardiopulmonary exercise testing (CPET).
resultsTwelve clinically most relevant factors were identified and partitioned into four ordered blocks in the GCM: block 1 - gender, smoking, age and body mass index (BMI); block 2 - admission to the intensive care unit (ICU) and length of follow-up in days; block 3 - peak oxygen consumption (VO
conclusionsThe biological plausibility of the relationships between variables demonstrated by the GCM validates the efficacy of this approach as a valuable statistical tool for elucidating structural features, such as conditional dependencies and associations. This promising method holds potential for exploring the long-term health repercussions of COVID-19 by identifying predictive factors and establishing suitable therapeutic strategies.
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