Post by Nadica (She/Her) on Sept 3, 2024 1:21:53 GMT
GCM’s Role to Explore Long-term COVID-19 Impacts - Published Sept 2, 2024
The following is a summary of “Modelling the long-term health impact of COVID-19 using Graphical Chain Models brief heading: long COVID prediction by graphical chain mode,” published in the August 2024 issue of Infectious Disease by Gourgoura et al.
About 10% of individuals who survived COVID-19 experienced long-term sequelae known as long COVID syndrome, which consisted of physical symptoms and objective measures of organ dysfunction. This condition resulted from a complex interaction between individual predisposing factors and symptoms of the disease.
Researchers conducted a retrospective study to investigate the complexity of the relationship between long COVID symptoms and their predictors in a population of survivors of severe COVID-19-related pneumonia by a Graphical Chain Model (GCM).
They monitored 96 patients with severe COVID-19 who were hospitalized in a non-intensive ward at the “Santa Maria” University Hospital, Terni, Italy, for 3-6 months. Data on drug treatment, in-hospital recorded findings, symptoms, and signs of damaged organs and current and previous clinical status were collected at follow-up. Resting pulmonary function tests, echocardiography, high-resolution chest tomography (HRCT), and cardiopulmonary exercise testing (CPET) were conducted to assess static and dynamic cardiac and respiratory parameters.
The results showed 12 clinically relevant factors, which were categorized into 4 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 (VO2), forced expiratory volume at first second (FEV1), D-dimer levels, depression score and presence of fatigue; block 4 – HRCT pathological findings. Block 2 ICU admission had quite a high BMI and smoking. VO2 was dependent on the length of follow-up, whereas FEV1 was associated with fatigue, further linked to depression score. No fatigue or depression on variables in block 2 was observed.
They concluded that the role of GCM in validating the relationship between variables is a tool for uncovering structural features like dependencies and associations. This optimistic method has the potential to examine the long-term impacts of COVID-19 by determining factors and therapeutic strategies.
Source: bmcinfectdis.biomedcentral.com/articles/10.1186/s12879-024-09777-0
The following is a summary of “Modelling the long-term health impact of COVID-19 using Graphical Chain Models brief heading: long COVID prediction by graphical chain mode,” published in the August 2024 issue of Infectious Disease by Gourgoura et al.
About 10% of individuals who survived COVID-19 experienced long-term sequelae known as long COVID syndrome, which consisted of physical symptoms and objective measures of organ dysfunction. This condition resulted from a complex interaction between individual predisposing factors and symptoms of the disease.
Researchers conducted a retrospective study to investigate the complexity of the relationship between long COVID symptoms and their predictors in a population of survivors of severe COVID-19-related pneumonia by a Graphical Chain Model (GCM).
They monitored 96 patients with severe COVID-19 who were hospitalized in a non-intensive ward at the “Santa Maria” University Hospital, Terni, Italy, for 3-6 months. Data on drug treatment, in-hospital recorded findings, symptoms, and signs of damaged organs and current and previous clinical status were collected at follow-up. Resting pulmonary function tests, echocardiography, high-resolution chest tomography (HRCT), and cardiopulmonary exercise testing (CPET) were conducted to assess static and dynamic cardiac and respiratory parameters.
The results showed 12 clinically relevant factors, which were categorized into 4 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 (VO2), forced expiratory volume at first second (FEV1), D-dimer levels, depression score and presence of fatigue; block 4 – HRCT pathological findings. Block 2 ICU admission had quite a high BMI and smoking. VO2 was dependent on the length of follow-up, whereas FEV1 was associated with fatigue, further linked to depression score. No fatigue or depression on variables in block 2 was observed.
They concluded that the role of GCM in validating the relationship between variables is a tool for uncovering structural features like dependencies and associations. This optimistic method has the potential to examine the long-term impacts of COVID-19 by determining factors and therapeutic strategies.
Source: bmcinfectdis.biomedcentral.com/articles/10.1186/s12879-024-09777-0