Serum Complement C3 and C4 and COVID-19 Severity and Mortality: A Systematic Review and Meta-Analysis With Meta-Regression
Bibliographic record
Abstract
Activation of the complement system has been observed in coronavirus disease 19 (COVID-19). We conducted a systematic review and meta-analysis with meta-regression to investigate possible differences in the serum concentrations of two routinely measured complement components, C3 and C4, in COVID-19 patients with different severity and survival status. We searched PubMed, Web of Science and Scopus, between January 2020 and February 2021, for studies reporting serum complement C3 and C4, measures of COVID-19 severity, and survival. Eligibility criteria were a) reporting continuous data on serum C3 and C4 concentrations in COVID-19 patients, -b) investigating COVID-19 patients with different disease severity and/or survival status, c) adult patients, d) English language, e) ≥10 patients, and f) full-text available. Using a random-effects model, standardized mean differences (SMD) with 95% confidence intervals (CIs) were calculated to evaluate differences in serum C3 and C4 concentrations between COVID-19 patients with low vs. high severity or survivor vs. non-survivor status. Risk of bias was assessed using the Newcastle-Ottawa scale whereas publication bias was assessed with the Begg’s and Egger’s tests. Certainty of evidence was assessed using GRADE. Nineteen studies in 3,764 COVID-19 patients were included in the meta-analysis. Both C3 and C4 concentrations were significantly lower in patients with high disease severity or non-survivor status than patients with low severity or survivor status (C3 SMD=-0.40, 95% CI -0.60 to -0.21, p<0.001; C4 SMD=-0.29, 95% CI -0.49 to -0.09, p=0.005; moderate certainty of evidence). Extreme between-study heterogeneity was observed (C3, I 2 = 82.1%; C4, I 2 = 84.4%). Sensitivity analysis, performed by sequentially removing each study and re-assessing the pooled estimates, showed that the magnitude and direction of the effect size was not modified. There was no publication bias. In meta-regression, the SMD of C3 was significantly associated with white blood cell count, C-reactive protein (CRP), and pro-thrombin time, whereas the SMD of C4 was significantly associated with CRP, pro-thrombin time, D-dimer, and albumin. In conclusion, lower concentrations of C3 and C4, indicating complement activation, were significantly associated with higher COVID-19 severity and mortality. C3 and C4 might be useful to predict adverse clinical consequences in these patients. Systematic Review Registration: PROSPERO, Registration number: CRD42021239634.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.016 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".