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 I 2 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( × 10 9 /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 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 | 0.004 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.021 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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 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".