Neutrophil–lymphocyte ratio as predictor of mortality and morbidity in cardiovascular surgery: a systematic review
Bibliographic record
Abstract
BACKGROUND: Neutrophil-lymphocyte ratio (NLR) is an emerging biomarker of inflammation and predicts poorer outcome in cancer surgery. The prognostic value of NLR in cardiovascular surgery is unclear. METHODS: Systematic review and meta-analysis of studies of in cardiovascular surgical patients were conducted to assess the role of perioperative NLR in predicting post-operative mortality and morbidity. Electronic searches were conducted on Ovid Medline, EMBASE, Cochrane Central Register of Controlled Trials and Cochrane Database of Systemic Reviews for all prospective clinical studies reporting on NLR and post-operative morbidity and mortality in cardiovascular surgical patient population. Our primary end point was all-cause post-operative mortality and the secondary end point was post-operative morbidity. Mortality outcome from prospective studies were pooled for a meta-analysis using a random-effect model. RESULTS: Of the 999 citations identified, five studies with 3487 patients met the inclusion criteria. In a pooled analysis of three prospective studies of 3108 patients, a preoperative increase in NLR (>3.3 in cardiac surgery, >5 in vascular surgery) was associated with increased mortality at a mean follow-up of 34.8 months (hazard ratio 1.85, 95% confidence interval 1.46-2.36; P < 0.00001). Raised NLR value was also associated with increased cardiac mortality, amputation in vascular operations and raised risk of post-operative re-intubation. CONCLUSIONS: Elevated NLR were associated with increased long-term mortality and morbidity after major cardiac and vascular surgery. NLR may guide perioperative management and risk-stratification of 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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.013 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 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".