Impact of periprocedural major adverse events on 10-year mortality after revascularisation.
Bibliographic record
Abstract
BACKGROUND: The long-term prognostic impact of a composite of periprocedural major adverse events (PMAE) following revascularisation for patients with complex coronary artery disease (CAD) has not yet been established. AIMS: This study aimed to assess the impact on 10-year mortality of non-fatal PMAE following percutaneous coronary intervention (PCI) or coronary artery bypass grafting (CABG). Other objectives were to evaluate 1) whether PMAE affect mortality predicted by the SYNTAX score II 2020 (SSII-2020) and 2) whether optimal medical therapy (OMT) positively affects the prognosis of patients with non-fatal PMAE. METHODS: The association between 10-year mortality and non-fatal PMAE occurring within 30 days of PCI or CABG in patients with three-vessel disease and/or left main disease enrolled in the SYNTAXES study was investigated. RESULTS: The main findings are that non-fatal PMAE occurred less frequently following PCI than CABG (11.2% vs 28.2%; p<0.001) and that non-fatal PMAE were an independent predictor of all-cause mortality in the first year post-procedure, but not at 5 or 10 years, in both treatment modalities. PMAE substantially alter the individual predictions of 10-year mortality by the SSII-2020. In patients with non-fatal PMAE, OMT may provide survival benefits during the first year post-procedure as well as in the long term. CONCLUSIONS: In patients with complex CAD, non-fatal PMAE were more common following CABG than PCI, but their prognostic impact was similar, being significant in the first year and then diminishing out to 10 years. Patients with non-fatal PMAE may therefore require more careful follow-up and additional preventive treatment in the first year post-procedure.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".