Abstract 8884: Predictors of Adverse Effects on Coronary Artery Bypass Surgery for Patients with Previous History of Cerebrovascular Accident
Bibliographic record
Abstract
Background: Past history of cerebrovascular accident (CVA) is a serious risk factor for increased mortality and morbidity, such as CVA recurrence, after coronary artery bypass grafting (CABG). Purpose of this study is to elucidate the independent predictors of adverse outcomes after CABG among the population with CVA history using a national database. Methods and Results: Patient data were acquired and analyzed from Japan Cardiovascular Surgery Database (JCVSD) registry. 13109 patients undergoing isolated CABG between January 2008 and December 2009 were enrolled in the JCVSD registry. Of these, all the patients with CVA history were included in the analysis as study group (SG; n = 1695). Postoperative CVA occurred in 6.0% (102/1695) of SG and 2.8% (318/11414) of patients without CVA history (Control) (P<.001). Operative mortality of SG and Control were 2.9% and 1.9%, respectively (P=.008). Major morbidity and mortality of SG and control were 17.4% and 12.2%, respectively (P<.001). Multivariate analysis of SG identified five independent preoperative predictors for the CVA recurrence, eight for the operative mortality and 12 for the major morbidity and mortality. The analysis also identified two independent operative predictors for the CVA recurrence, four for the operative mortality and six for the major morbidity and mortality. The table shows the predictors of these adverse effects. Conclusions: Independent predictors of CVA recurrence, major morbidity and mortality were identified through subgroup analysis of the JCVSD registry. Patients who suffered CVA recurrence or other adverse effects had a greater prevalence of concomitant medical illnesses. In conclusion, use of internal mammary artery, off-pump CABG, otherwise shorter cardiopulmonary bypass time, shorter operation time and optimal numbers of distal anastomoses are effective in reducing the risk of CVA recurrence or other adverse effects on CABG for patients with CVA history.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 |
| 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".