The Prognostic Value of Circulating Cytokines and Complete Blood Count-Based Inflammatory Markers in COVID-19 Patients With Atrial Fibrillation
Bibliographic record
Abstract
Background: Atrial fibrillation (AF) is associated with a high burden of cardiovascular disease, which has been worsened during the coronavirus disease 2019 (COVID-19) pandemic. The purpose of this study was to assess the association between clinical markers, especially interleukin-6 (IL-6) and other inflammatory biomarkers, and the severity of COVID-19 in patients with AF. Methods: This retrospective cohort study categorized patients based on clinical presentations and laboratory results to investigate the prognostic significance of inflammatory markers in COVID-19 outcomes among those with AF. The study included 100 hospitalized COVID-19 patients aged between 40 to 80 years and was conducted at the Chapidze Hospital in Tbilisi, Georgia. Patients were then grouped by disease severity according to computed tomography (CT) scores, clinical symptoms, respiratory rate and oxygen saturation. Levels of IL-6 were obtained at three time points during hospitalization. A broad range of laboratory tests, including C-reactive protein (CRP), ferritin, and D-dimer, were also conducted. Results: Patients with AF demonstrated significantly elevated levels of IL-6 (P = 0.024), CRP (P = 0.001), and ferritin (P < 0.001), suggesting a severe inflammatory response. D-dimer levels were also notably higher in the AF group (P < 0.005), indicating an increased risk of thrombotic complications. Oxygen saturation levels were significantly lower (P = 0.004) and CT scores higher in patients with AF. Furthermore, the length of hospitalization was longer among patients with AF (median duration significantly higher, P = 0.032), indicating a more severe disease course. Conclusions: The proinflammatory markers such as IL-6 are independent predictive markers of COVID-19 severity in AF patients. Overall, it highlights urgent treatment approaches, such as available anti-inflammatory drugs, for COVID-19 patients with arrhythmias. Combining these biomarkers into clinical routines helps us better identify patients at risk and how to treat them.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".