Abstract 3239: Risk of Stroke after New-Onset Atrial Fibrillation versus Chronic AF in Patients Undergoing Coronary Artery Bypass Surgery
Bibliographic record
Abstract
Background: Among patients undergoing isolated coronary artery bypass graft surgery (CABG), the risk of post-operative stroke has not been compared between patients with chronic atrial fibrillation (AF) versus patients with hospital-acquired new-onset AF (new-AF). Methods: Using linked hospital discharge data together with comprehensive clinical data from the California CABG outcomes reporting program, we conducted a retrospective cohort study of all patients undergoing isolated CABG in California between years 2003–2006. Using hospital discharge data, chronic AF was defined as ICD-9-CM =427.31 present at time of admission and during a previous hospitalization. New-AF was defined as a first-ever code for AF that was not present at the time of admission. The risk of stroke < 30 days after surgery, defined using specific ICD-9-CM codes, was analyzed using logistic regression, with adjustment for 15 clinically important stroke risk factors. As a sensitivity analysis, we developed a propensity model for new-AF, and analyzed the risk of stroke after hospital discharge but within 30 days of surgery. Results: Among 61,031 cases, 2081 (3.4%) had chronic AF; 9858 (17%) had new-AF; the 30-day incidence of stroke was 1222 (2.0%). Compared to patients with no-AF, the adjusted risk of stroke in patients with chronic AF was odds ratio (OR) = 1.2 (CI: 0.9 –1.5), whereas for new-AF, it was OR= 1.7 (CI: 1.5–1.9), c-statistic = 0.73. Using the propensity analysis, the risk of stroke after hospital discharge associated with new-AF versus no-AF was similar across quintiles of the risk score (OR range=1.7–1.8). Conclusion: After adjusting for stroke risk factors, patients who developed AF during hospitalization for CABG had an approximately 70% higher risk of stroke within 30 days compared to patients without AF, whereas the risk of stroke in patients with chronic AF was not significantly increased. Interventions that reduce the incidence of new-AF after CABG surgery may reduce the incidence of subsequent stroke.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".