Prognostic value of Neutrophil-to-lymphocyte ratio in COVID-19 patients: A systematic review and meta-analysis
Bibliographic record
Abstract
Background: Neutrophil-to-lymphocyte ratio (NLR) is an accessible and widely used biomarker. NLR may be used as an early marker of poor prognosis in patients with COVID-19. Methods: We conducted a systematic review and meta-analysis. Observational studies that reported the association between baseline NLR values (i.e. at hospital admission) and severity or all-cause mortality in COVID-19 patients were included. The quality of the studies was assessed using the Newcastle-Ottawa scale (NOS). Random effects models and inverse variance method were used for meta-analyses. The effects were expressed as odds ratios (OR) and their 95% confidence intervals (CI). Small study effects were assessed with the Egger’s test. Results: Twenty studies, 19 cohorts and one case-control were included. An increase of one unit of NLR was associated with a higher odds of COVID-19 severity (OR 6.6, 95% CI: 4.71 - 7.19; p<0.001) and higher odds of all-cause mortality (OR 12.7, 95% CI: 1.32, 123.36; p=0.025). No differences were found in subgroup analyses by study design. The subgroup analysis of the studies, by country of origin, showed that the strength of the association between NLR and mortality was greater in Chinese studies (OR 31.1; 95%CI 19.57 to 49.3; p<0.0001) with moderate heterogeneity (I2 =43%). In our sensitivity analysis, we found that 7 studies with low risk of bias maintained strong association between both outcomes and the NLR values (severity: OR 4.7; 95% CI 3.5 to 6.34; p < 0.001 vs mortality: OR 31.1; 95% CI 19.57 to 49.3; p <0.0001), with low (I2 = 37%) and moderate (I2 = 43%) heterogeneity for severity and mortality outcomes, respectively. No publication bias was found for studies that evaluated effects for the severity of disease. Conclusions: Higher values of NLR were associated with severity and all-cause mortality in hospitalized COVID-19 patients.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.028 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".