D-dimer levels and characteristics of lymphocyte subsets, cytokine profiles in peripheral blood of patients with severe COVID-19: A systematic review and meta-analysis
Bibliographic record
Abstract
Purpose A series of complications caused by severe COVID-19 can significantly affect short-term results. Therefore, early diagnosis is essential for critically COVID-19 patients. we aimed to investigate the correlation among D-dimer levels, lymphocyte subsets, cytokines, and disease severity in COVID-19 patients. Methods Systematic review and meta- analysis of PubMed, Scopus, Web of Science, Cochrane Central Register of Controlled Trials, Embase, clinical trials, and China National Knowledge Infrastructure (CNKI) until 1 August 2022. We considered case-control, and cohort studies that compared laboratory parameters between patients with severe or non-serious diseases or between survivors and non-survivors. Pooled data was assessed by use of a random-effects model and used I2 to test heterogeneity. We assessed the risk of bias using the Newcastle- Ottawa Scale. Results Of the 5,561 identified studies, 32 were eligible and included in our analysis (N = 3,337 participants). Random-effect results indicated that patients with COVID-19 in severe group had higher levels for D-dimer (WMD = 1.217 mg/L, 95%CI=[0.788, 1.646], P < 0.001), neutrophil-to-lymphocyte ratio (NLR) (WMD = 6.939, 95%CI = [4.581, 9.297], P < 0.001), IL-2 (WMD = 0.371 pg/ml, 95%CI = [−0.190, 0.932], P = 0.004), IL-4 (WMD = 0.139 pg/ml, 95%CI = [0.060, 0.219], P = 0.717), IL-6 (WMD = 44.251 pg/ml, 95%CI = [27.010, 61.493], P < 0.001), IL-10 (WMD = 3.718 pg/ml, 95%CI = [2.648, 4.788], P < 0.001) as well as lower levels of lymphocytes (WMD = −0.468( × 109/L), 95%CI = [−0.543, −0.394], P < 0.001), T cells (WMD = −446.746(/μL), 95%CI = [−619.607, −273.885], P < 0.001), B cells (WMD = −60.616(/μL), 95%CI = [−96.452, −24.780], P < 0.001), NK cells (WMD = −68.297(/μL), 95%CI = [−90.600, −45.994], P < 0.001), CD3+T cells (WMD = −487.870(/μL), 95%CI = [−627.248, −348.492], P < 0.001), CD4+T cells (WMD = −290.134(/μL), 95%CI = [−370.834, −209.435], P < 0.001), CD8+T cells (WMD = −188.781(/μL), 95%CI = [−227.806, −149.757], P < 0.001). Conclusions There is a correlation among higher levels of D-dimer, cytokines, lower levels of lymphocyte subsets, and disease severity in COVID-19 patients. These effective biomarkers may help clinicians to evaluate the severity and prognosis of COVID-19. This study is registered with PROSPERO, number CRD42020196659. Systematic review registration https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=196659 ; PROSPERO registration number: CRD42020196659.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.037 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".