Publication date: Jul 06, 2025
Post-COVID-19 condition (PCC) has gained traction currently in the post-pandemic era. To address this, we utilized a Bayesian directed acyclic graphic (DAG) model to develop a personalized composite risk score (CRS) for PCC, based on the tabular data derived from a comprehensive meta-analysis. Our risk assessment model incorporates 215 combinations of risk factors, including personal demographic and health-related profiles, across 41 studies involving over 860,000 COVID-19 cases. The CRS ranges from 0 to 500, categorizing patients into risk quartiles and estimating PCC probability across SARS-CoV-2 variants of concerns, including Wild/D614G/Alpha, Delta, and Omicron BA. 1/BA. 2. External validation demonstrated accurate predictions, though higher risk scores showed slight deviations, particularly in BA. 5 Omicron subset. The risk assessment model is not only adaptable for incorporating new evidence as SARS-CoV-2 subvariants emerge but also very valuable in facilitating the optimal individualized medical care for PCC patients and prioritizing a spectrum of risk groups for early PCC diagnosis. Notably, the adaptability of Bayesian DAG model enhances PCC risk prediction, enabling data integration for evolving SARS-CoV-2 contexts and informing healthcare resource allocation for high-risk groups.
| Concepts | Keywords |
|---|---|
| Adaptable | Bayesian |
| Covid | post‐COVID‐19 condition |
| D614g | risk assessment |
| Healthcare | |
| Pandemic |
Semantics
| Type | Source | Name |
|---|---|---|
| disease | MESH | COVID-19 |
| drug | DRUGBANK | Factor IX Complex (Human) |