White blood cell count and clinical outcomes after left main coronary artery revascularization: insights from the EXCEL trial
Bibliographic record
Abstract
BACKGROUND: Prior studies have reported an association between elevated white blood cell count (WBCc) and worse clinical outcomes after coronary artery bypass grafting (CABG) and percutaneous coronary intervention (PCI). We assessed the prognostic impact of WBCc in patients undergoing revascularization for left main coronary artery disease (LMCAD). METHODS: In Evaluation of XIENCE Versus Coronary Artery Bypass Surgery for Effectiveness of Left Main Revascularization (EXCEL), 1905 patients with LMCAD and low or intermediate SYNTAX scores were randomized to PCI with everolimus-eluting stents versus CABG. The 1895 patients with baseline WBCc available were grouped in tertiles of WBCc (mean 5.6 ± 0.8, 7.5 ± 0.5, and 10.1 ± 1.6 × 109/L). RESULTS: Five-year rates of the primary endpoint (death, myocardial infarction or stroke) were similar across increasing WBCc tertiles (21.2, 18.9, and 21.6%; P = 0.46). Individual components of the primary endpoint, Bleeding Academic Research Consortium (BARC) 3-5 bleeding, stent thrombosis or graft occlusion and ischemia-driven revascularization were all similar across WBCc tertiles. By multivariable analysis, WBCc as a continuous variable was not an independent predictor of adverse events (hazard radio per 1 × 109/L: 1.02; 95% CI, 0.97-1.08; P = 0.43). Results were consistent in the PCI and CABG arms individually. CONCLUSION: There was no association between baseline WBCc and 30-day or 5-year clinical outcomes after PCI or CABG. The absence of a clear incremental increase in events with increasing WBCc in the current analysis indicates that WBCc should not routinely be used as a prognostic marker or to guide revascularization decisions in patients with LMCAD.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".