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 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 (VO₂), forced expiratory volume at first second (FEV₁), 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. VO₂ showed dependency on length of follow-up. FEV₁ 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.

Fig. 1

The Graphical Chain Model (GCM). In the GCM two variables are joined by an edge. Two kinds of edges are allowed: directed (←), also called arrow, or undirected (-). An arrow is always used to join two variables in two different blocks. Let A be in block 3 and B be in block (1) A←B implies that B is an explanatory variable of A. An undirected edge is always used to join variables in the same block. Let A be in block 3 and C be also in block 3. Then A-C means that the two variables are associated after conditioning on all the variables in block 1 and 2. BMI: body mass index; VO2: peak oxygen consumption; log-DDIMER: Serum D-dimer level after logarithmic transformation; ICU: admission to intensive care unit; HRCT: high resolution computed tomography; FEV₁: Forced expiratory volume in one second