tetano
Editor, Senior Moderator
BMC Infect Dis
. 2024 Aug 29;24(1):885.
doi: 10.1186/s12879-024-09777-0. Modelling the long-term health impact of COVID-19 using Graphical Chain Models brief heading: long COVID prediction by graphical chain models
K Gourgoura[SUP] 1 [/SUP], P Rivadeneyra[SUP] 2 3 [/SUP], E Stanghellini[SUP] 1 [/SUP], C Caroni[SUP] 4 [/SUP], F Bartolucci[SUP] 1 [/SUP], R Curcio[SUP] 5 [/SUP], S Bartoli[SUP] 6 [/SUP], R Ferranti[SUP] 7 [/SUP], I Folletti[SUP] 8 9 [/SUP], M Cavallo[SUP] 5 [/SUP], L Sanesi[SUP] 5 [/SUP], I Dominioni[SUP] 5 8 [/SUP], E Santoni[SUP] 5 8 [/SUP], G Morgana[SUP] 5 8 [/SUP], M B Pasticci[SUP] 8 10 [/SUP], G Pucci[SUP] 11 12 [/SUP], G Vaudo[SUP] 5 8 [/SUP]
Affiliations
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 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).
Results: Twelve 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[SUB]2[/SUB]), forced expiratory volume at first second (FEV[SUB]1[/SUB]), D-dimer levels, depression score and presence of fatigue; block 4 - HRCT pathological findings. Higher BMI and smoking had a significant impact on the probability of a patient's admission to ICU. VO[SUB]2[/SUB] showed dependency on length of follow-up. FEV[SUB]1[/SUB] was related to the self-assessed indicator of fatigue, and, in turn, fatigue was significantly associated with the depression score. Notably, neither fatigue nor depression depended on variables in block 2, including length of follow-up.
Conclusions: The 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.
Keywords: COVID-19; Chain Graph Model; Fatigue; Graphical Chain Model; High resolution computed tomography; Long COVID; Prevention.
. 2024 Aug 29;24(1):885.
doi: 10.1186/s12879-024-09777-0. Modelling the long-term health impact of COVID-19 using Graphical Chain Models brief heading: long COVID prediction by graphical chain models
K Gourgoura[SUP] 1 [/SUP], P Rivadeneyra[SUP] 2 3 [/SUP], E Stanghellini[SUP] 1 [/SUP], C Caroni[SUP] 4 [/SUP], F Bartolucci[SUP] 1 [/SUP], R Curcio[SUP] 5 [/SUP], S Bartoli[SUP] 6 [/SUP], R Ferranti[SUP] 7 [/SUP], I Folletti[SUP] 8 9 [/SUP], M Cavallo[SUP] 5 [/SUP], L Sanesi[SUP] 5 [/SUP], I Dominioni[SUP] 5 8 [/SUP], E Santoni[SUP] 5 8 [/SUP], G Morgana[SUP] 5 8 [/SUP], M B Pasticci[SUP] 8 10 [/SUP], G Pucci[SUP] 11 12 [/SUP], G Vaudo[SUP] 5 8 [/SUP]
Affiliations
- PMID: 39210315
- PMCID: PMC11360819
- DOI: 10.1186/s12879-024-09777-0
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 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).
Results: Twelve 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[SUB]2[/SUB]), forced expiratory volume at first second (FEV[SUB]1[/SUB]), D-dimer levels, depression score and presence of fatigue; block 4 - HRCT pathological findings. Higher BMI and smoking had a significant impact on the probability of a patient's admission to ICU. VO[SUB]2[/SUB] showed dependency on length of follow-up. FEV[SUB]1[/SUB] was related to the self-assessed indicator of fatigue, and, in turn, fatigue was significantly associated with the depression score. Notably, neither fatigue nor depression depended on variables in block 2, including length of follow-up.
Conclusions: The 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.
Keywords: COVID-19; Chain Graph Model; Fatigue; Graphical Chain Model; High resolution computed tomography; Long COVID; Prevention.