Abstract 15189: Impact of Revascularization Strategy on the Prognosis of Young and Middle-aged Patients With Coronary Artery Disease
Bibliographic record
Abstract
Introduction: Despite increasing incidence and mortality of CAD among young and mid-age patients (age ≤ 65), the optimum revascularization strategy remains unclear. We compared outcomes of PCI and CABG in this patient group. Methods: “Coronary artery bypass” and “percutaneous coronary intervention” were used to identify articles in PubMed and Cochrane database published prior to February 2, 2020. Both RCTs and observational studies (OSs) comparing PCI and CABG with data of patients or subgroups patients ≤ 65 years of age were included. The quality of study data was assessed by RoB2 and Newcastle-Ottawa Scale (NOS). The primary end point was all-cause mortality. Secondary endpoint includes MI, stroke, repeat revascularization (RR), and a composite endpoint of major adverse cardiac cerebral events (MACCE). We calculated odds ratio using Mantel-Haenszel method with random effects. Results: A total of 10 RCTs and 20 OSs with 31226 CAD patients were included in our analysis, of which 1 RCT and 4 OSs focused on population ≤ 65 years old while the rest provided subgroup data. The risk of bias RCTs were low to middle, and quality ratings of OSs were 4-8 by NOS. Compared to CABG, PCI was associated with a higher risk of mortality (OR 1.42, 95% CI 1.24-1.62, P<0.001), MACCE (OR 1.99, P<0.001), MI (OR 2.13, P = 0.011), and RR (OR 3.88, P<0.001). The risk of stroke is similar in both groups (OR 0.883, P= 0.506). However, after stratification, mortality rate is similar in studies with follow-up period ≤ 3 years. (OR 1.27, P=0.255) but remain significant with longer follow-up (OR 1.41, P<0.001). Conclusions: Compared to PCI, CABG is associated with lower all-cause mortality in young and middle-aged CAD patients, especially with long follow-up indicating superior long-term survival. Given the longer life-expectancy in this age group, the advantage of CABG is even more prominent. However, given the retrospective nature of this study, dedicated RCT is needed to further address this question.
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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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".