Platelet-to-Lymphocyte Ratio at Admission as a Predictor of In-Hospital and Long-Term Outcomes in Patients With ST-Segment Elevation Myocardial Infarction Undergoing Primary Percutaneous Coronary Intervention: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: ST-segment elevation myocardial infarction (STEMI) is the most severe form of acute coronary syndrome (ACS) which is associated with significant adverse outcomes. Platelet-to-lymphocyte ratio (PLR) is a novel inflammatory biomarker that has been used as a predictor of various cardiovascular diseases, including ACS. This meta-analysis aimed to investigate the prognostic value of PLR as a predictor of in-hospital and long-term outcomes in patients with STEMI undergoing primary percutaneous coronary intervention (PCI). Methods: We performed a comprehensive systematic literature search in the databases of PubMed, ScienceDirect, Cochrane Library, and ProQuest for eligible studies. The primary outcomes were major adverse cardiac events (MACEs) and mortality, both in-hospital and long-term follow-up. The outcomes were compared between patients with high and low admission PLR. The quality assessment was conducted using the Newcastle-Ottawa scale. Review Manager 5.3 was used to perform the meta-analysis. Results: Six cohort studies involving 4,289 STEMI patients undergoing primary PCI were included in this meta-analysis. The pooled analysis showed that a high PLR at admission was associated with increased in-hospital MACE (odds ratio (OR) = 1.94, 95% confidence interval (CI) = 1.56 - 2.40, P < 0.00001, I 2 = 45%) and in-hospital mortality (OR = 2.07; 95% CI = 1.53 - 2.80; P < 0.00001; I 2 = 50%), as well as increased long-term MACE (OR = 1.98; 95% CI = 1.31 - 3.00; P = 0.001; I 2 = 72%) and long-term mortality (OR = 2.79; 95% CI = 1.45 - 5.36; P = 0.002; I 2 = 83%). Conclusions: In patients with STEMI undergoing primary PCI, a high PLR at admission predicts in-hospital MACE and mortality along with long-term MACE and mortality. Cardiol Res. 2021;12(2):109-116 doi: https://doi.org/10.14740/cr1219
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.034 |
| Bibliometrics | 0.005 | 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 | 0.002 |
| 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".