Association between white blood cell count and coronary artery bypass graft failure: an individual patient data analysis of clinical trials
Bibliographic record
Abstract
Baseline systemic inflammation is associated with worse long-term outcomes after coronary artery bypass grafting [CABG], but the mechanisms of this association are unclear. This study aims to explore the association between pre-operative white blood cell [WBC] count and CABG graft failure. We pooled individual patient data from two randomized clinical trials with systematic CABG graft imaging. The primary analysis was the association between pre-operative WBC count and graft failure, as a continuous variable, at the time of imaging after CABG, using mixed-effects multivariable logistic regression models. Overall, 910 patients and 2,036 grafts were included in the analysis [1,120 saphenous vein grafts, 828 left internal thoracic arteries, 76 right internal thoracic arteries, and 12 radial arteries]. The median time to imaging was 1.01 [interquartile range (IQR), 0.99;1.03] years and the median pre-operative WBC count was 7.1 [IQR, 6.0;8.4] x 10 9 /L. There was no association between WBC count and graft failure at both the patient and the individual graft level [adjusted odds ratio (aOR) 1.07 (95% confidence interval (CI), 0.98;1.17), p = 0.11 and aOR 1.09 (95% CI, 0.91;1.30), p = 0.37], respectively. When evaluated as a dichotomous variable [≥ 11 vs. < 11 × 10 9 /L] and by quartile, WBC count was not associated with graft failure at the patient and individual graft levels. In this pooled analysis of individual patient data from two randomized clinical trials, WBC count was not associated with graft failure after CABG. The reported association between inflammation and CABG is likely mediated through other mechanisms, such as native coronary artery disease progression. The lack of a clear association between WBC count and graft failure suggests that pre-operative WBC count should not be routinely used as a predictor of graft failure after CABG.
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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 | 0.032 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 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".