Prevalence and Long‐Term Survival After Coronary Artery Bypass Grafting in Women and Men With Heart Failure and Preserved Versus Reduced Ejection Fraction
Bibliographic record
Abstract
BACKGROUND: Heart failure (HF) with reduced ejection fraction (rEF) is a widely regarded prognosticator after coronary artery bypass grafting. HF with preserved ejection fraction (pEF) accounts for up to half of all HF cases and is associated with considerable morbidity and mortality in hospitalized cohorts. However, HFpEF outcomes have not been elucidated in cardiac surgical patients. We investigated the prevalence and outcomes of HFpEF and HFrEF in women and men following coronary artery bypass grafting. METHODS AND RESULTS: We conducted a retrospective cohort study in Ontario, Canada, between October 1, 2008, and March 31, 2015, using Cardiac Care Network and Canadian Institute of Health Information data. HF is captured through a validated population-based database of all Ontarians with physician-diagnosed HF. We defined pEF as ejection fraction ≥50% and rEF as ejection fraction <50%. The primary outcome was all-cause mortality. Analyses were stratified by sex. Mortality rates were calculated using Kaplan-Meier method. The relative hazard of death was assessed using multivariable Cox proportional hazard models. Of 40 083 patients (20.6% women), 55.5% had pEF without HF, 25.7% had rEF without HF, 6.9% had HFpEF, and 12.0% had HFrEF. Age-standardized HFpEF mortality rates at 4±2 years of follow-up were similar in women and men. HFrEF standardized HFpEF mortality rates were higher in women than men. CONCLUSIONS: We found a higher prevalence and poorer prognosis of HFpEF in women. A history of HF was a more important prognosticator than ejection fraction. Preoperative screening and extended postoperative follow-up should be focused on women and men with HF rather than on rEF alone.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".