Association Between Elevated Neutrophil-to-Lymphocyte Ratio and Mortality Risk in Community-Acquired Pneumonia: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
The neutrophil-to-lymphocyte ratio (NLR), derived from routine complete blood count parameters, has emerged as a potential prognostic biomarker reflecting systemic inflammation and immune dysfunction. This systematic review and meta-analysis evaluated the association between elevated NLR and mortality risk in community-acquired pneumonia (CAP) patients. A comprehensive literature search was conducted across multiple databases from January 2011 to August 2025, identifying studies that measured NLR in adult CAP patients with mortality as an endpoint. Twelve observational studies were included, comprising diverse patient populations across different healthcare settings. Random-effects meta-analysis was performed using R software (R Foundation for Statistical Computing, Vienna, Austria), with relative risk as the primary effect measure. Quality assessment was conducted using the Newcastle-Ottawa Scale. The pooled analysis demonstrated that elevated NLR was significantly associated with increased all-cause mortality risk in CAP patients (RR: 2.02, 95% CI: 1.18-3.47, p<0.05). Patients with high NLR values had approximately twice the risk of death compared to those with lower ratios. Pooled analysis showed that high NLR was associated with increased risk of ICU admission (RR: 1.30, 95% CI: 1.11-1.53). Substantial heterogeneity was observed across studies (I² = 99% for mortality, 85% for ICU admission), likely reflecting variations in patient populations, NLR cutoff values, and clinical settings. Sensitivity analysis confirmed the robustness of findings. This meta-analysis supports NLR as a valuable, cost-effective prognostic biomarker for mortality prediction in CAP patients, potentially enhancing clinical decision-making when integrated with existing severity assessment tools.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.035 |
| Bibliometrics | 0.006 | 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".