Lymphopenia and neutrophilia at admission predicts severity and mortality in patients with COVID-19: a meta-analysis.
Bibliographic record
Abstract
BACKGROUND: There is a compelling need to identify clinical and laboratory predictors of unfavorable clinical course and death in patients with coronavirus disease (COVID-19). A trend towards low lymphocyte count and high neutrophil counts in patients with poor outcomes has been reported by earlier studies. We aim to synthesize existing data evaluating the relationship between clinical outcomes and abnormal neutrophil and lymphocyte counts at admission in COVID-19 patients. METHODS: An electronic search was carried out in PubMed, China National Knowledge Infrastructure (CNKI) and Cochrane Central Register of Controlled Trials (CENTRAL) to identify eligible studies reporting frequency data on neutrophilia and lymphopenia at admission in hospitalization in COVID-19 patients. Pooled odds ratios of clinical outcomes for each parameter were calculated using Comprehensive Meta-Analysis. RESULTS: A total of 22 studies (4,969 patients) were included in this meta-analysis. Lymphopenia at admission was found to be significantly associated with increased odd of progression to severe disease (odds ratio [OR], 4.20; 95% confidence interval [95CI%], 3.46-5.09) and death (OR, 3.71; 95%CI, 1.63-8.44). Neutrophilia at admission was also found to be significantly associated with increased odd of progression to severe disease (OR, 7.99; 95%CI, 1.77-36.14) and death (OR, 7.87; 95%CI, 1.75-35.35). Subgroup analysis revealed that COVID-19 patients with severe lymphopenia (<0.5 x10×9/L) had 12-fold increased odds of in-hospital mortality. CONCLUSION: Admission lymphopenia and neutrophilia are associated with poor outcomes in patients with COVID-19. Regular monitoring and early and even more aggressive intervention shall hence be advisable in patients with low lymphocyte and high neutrophil counts. These variables may be useful in risk stratification models.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.058 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".