Inflammatory and vascular biomarkers in post‐COVID‐19 syndrome: A systematic review and meta‐analysis of over 20 biomarkers
Bibliographic record
Abstract
Severe acute respiratory syndrome coronavirus 2 may inflict a post-viral condition known as post-COVID-19 syndrome (PCS) or long-COVID. Studies measuring levels of inflammatory and vascular biomarkers in blood, serum, or plasma of COVID-19 survivors with PCS versus non-PCS controls have produced mixed findings. Our review sought to meta-analyse those studies. A systematic literature search was performed across five databases until 25 June 2022, with an updated search on 1 November 2022. Data analyses were performed with Review Manager and R Studio statistical software. Twenty-four biomarkers from 23 studies were meta-analysed. Higher levels of C-reactive protein (Standardized mean difference (SMD) = 0.20; 95% CI: 0.02-0.39), D-dimer (SMD = 0.27; 95% CI: 0.09-0.46), lactate dehydrogenase (SMD = 0.30; 95% CI: 0.05-0.54), and leukocytes (SMD = 0.34; 95% CI: 0.02-0.66) were found in COVID-19 survivors with PCS than in those without PCS. After sensitivity analyses, lymphocytes (SMD = 0.30; 95% CI: 0.12-0.48) and interleukin-6 (SMD = 0.30; 95% CI: 0.12-0.49) were also significantly higher in PCS than non-PCS cases. No significant differences were noted in the remaining biomarkers investigated (e.g., ferritin, platelets, troponin, and fibrinogen). Subgroup analyses suggested the biomarker changes were mainly driven by PCS cases diagnosed via manifestation of organ abnormalities rather than symptomatic persistence, as well as PCS cases with duration of <6 than ≥6 months. In conclusion, our review pinpointed certain inflammatory and vascular biomarkers associated with PCS, which may shed light on potential new approaches to understanding, diagnosing, and treating PCS.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.030 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".