Neutrophil-to-lymphocyte ratio for predictor of in-hospital mortality in ST-segment elevation myocardial infarction: a meta-analysis
Bibliographic record
Abstract
BACKGROUND ST-segment elevation myocardial infarction (STEMI) is the most life-threatening condition of acute coronary syndrome that carries a poor prognosis of in-hospital mortality. Multiple scoring systems have been developed to predict in-hospital mortality and other cardiovascular events. Neutrophil-to-lymphocyte ratio (NLR) is hardly used as a predictor of in-hospital mortality. This study was aimed to determine the predictive value of NLR concerning in-hospital mortality in STEMI patients. METHODS Literature search and pooled analysis related to studies on MEDLINE/PubMed, EBSCO, Science Direct, Cochrane, and ProQuest were retrieved. Inclusion criteria were met if they were cohort studies, the subjects were STEMI patient, contained pretreatment NLR cut-off, and considered in-hospital mortality, which is defined as cardiac or all-cause mortality. Quality assessment was conducted using Newcastle-Ottawa scale. Review Manager version 5.3 (The Nordic Cochrane Centre, Copenhagen) was used for meta-analysis. RESULTS We found 12 studies with a total of 7,251 STEMI subjects with median NLR cut-off value of 5.6. Elevated NLR on admission carries a high risk of in-hospital mortality (odds ratio [OR] = 3.00, 95% confidence interval [CI] = 2.46–3.67). A slightly higher risk of all-cause mortality (OR = 2.74, 95% CI = 1.99–3.77) was observed compared with cardiac-related mortality (OR = 3.20, 95% CI = 2.47–4.14). No significant heterogeneity was observed between these studies (p = 0.46, I2 = 0%). CONCLUSIONS Elevated NLR predicts a higher in-hospital mortality rate of STEMI patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".