Association of neutrophil-to-lymphocyte ratio with clinical outcomes after percutaneous coronary intervention
Bibliographic record
Abstract
AIMS: Inflammation contributes significantly to coronary artery disease (CAD). The neutrophil-to-lymphocyte ratio (NLR) has emerged as a readily available biomarker reflecting both inflammatory and immune cells' activity, potentially enhancing risk stratification of patients with CAD. This study evaluates the clinical impact of baseline NLR in patients undergoing percutaneous coronary intervention (PCI) for both chronic coronary syndrome and acute coronary syndrome (ACS). METHODS AND RESULTS: We conducted a retrospective analysis of patients undergoing PCI at Mount Sinai Hospital between 2012 and 2022. Patients were stratified into NLR quartiles and outcomes were analysed using Cox regression models. The primary endpoint was major adverse cardiovascular events (MACEs) at 1-year follow-up, including all-cause death, myocardial infarction (MI), and stroke. A total of 7287 patients were included in the study. Age, male sex, comorbidities, high-sensitivity C-reactive protein, and complexity of PCI tended to be higher in the highest NLR quartiles. At 1 year, MACE incidence increased across NLR quartiles, from 5.1% (1st quartile) to 9.3% (4th quartile) (P for trend = 0.004). Compared with the 1st quartile, the 4th NLR quartile (NLR > 5.0) was associated with increased adjusted risks of MACE [adjusted hazard ratio (adjHR) 1.52, 95% CI 1.12-2.05], all-cause death (adjHR 1.71, 95% CI 1.10-2.65), MI (adjHR 1.53, 95% CI 1.00-2.35), and bleeding (adjHR 2.01, 95% CI 1.50-2.70). Ischaemic risk associated with high NLR was more pronounced in patients presenting with ACS and chronic kidney disease (CKD). CONCLUSION: Baseline NLR is associated with adverse cardiovascular outcomes in CAD patients undergoing PCI. Assessment of NLR could enhance risk stratification particularly in patients with ACS and CKD.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".