The impact of coronary artery bypass grafting added to aortic valve replacement on long-term outcomes in octogenarian patients: a reconstructed time-to-event meta-analysis
Bibliographic record
Abstract
The long-term results in studies comparing octogenarian patients who received either isolated surgical aortic valve replacement (i-SAVR) or coronary artery bypass grafting (CABG) in addition to SAVR are still debated. We performed a reconstructed time-to-event data meta-analysis of studies comparing i-SAVR and CABG+SAVR to evaluate the impact of CABG and to analyse the time-varying effects on long-term outcome. We performed a systematic review of the literature from January 2000 through November 2021, including studies comparing i-SAVR and CABG+SAVR, which reported at least 3-year follow-up and that plotted Kaplan-Meier curves of overall survival. The primary endpoint was overall long-term survival; secondary endpoints were in-hospital/30-day mortality and postoperative outcomes. The pooled hazard ratio (HR) and odds ratio) with 95% confidence interval (CI) were calculated for primary and secondary endpoints, respectively. Random-effect model was used in all analyses. Sixteen retrospective studies were included (5382 patients, i-SAVR = 2568 and CABG+SAVR = 2814). I-SAVR showed a lower incidence of in-hospital mortality compared to CABG+SAVR (odds ratio = 0.73; 95% CI= 0.60-0.89; P = 0.002). Landmark analyses showed a significantly higher all-cause mortality within 1 year from surgery in CABG+SAVR (HR = 1.17; 95% CI = 1.01-1.36; P = 0.03); after 1 year, no significant difference was observed (HR = 0.95; 95% CI = 0.87-1.04; P = 0.35). Landmark analysis was confirmed by time-varying trend of HR. Late survival of octogenarians did not differ significantly between the 2 interventions. Interestingly, CABG added to SAVR was associated with both higher in-hospital and within 1-year mortality after surgery, whereas this difference was statistically non-significant at long-term follow-up.
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.042 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".