Long-Term Survival After Surgical or Percutaneous Revascularization in Patients With Diabetes and Multivessel Coronary Disease
Bibliographic record
Abstract
BACKGROUND: There remains a paucity of real-world observational evidence comparing percutaneous coronary intervention (PCI) with coronary artery bypass grafting (CABG) in patients with diabetes and multivessel coronary artery disease (CAD). OBJECTIVES: This study compared early and long-term outcomes of PCI versus CABG in patients with diabetes. METHODS: Clinical and administrative databases in Ontario, Canada were linked to obtain records of all patients with diabetes with angiographic evidence of 2- or 3-vessel CAD who were treated with either PCI or isolated CABG from 2008 to 2017. A 1:1 propensity score match was performed to account for baseline differences. All-cause mortality and the composite of myocardial infarction, repeat revascularization, stroke, or death (termed major cardiovascular and cerebrovascular events [MACCEs]) were compared between the matched groups using a stratified log-rank test and Cox proportional hazards model. RESULTS: A total of 4,519 and 9,716 patients underwent PCI and CABG, respectively. Before matching, patients who underwent CABG were significantly younger (age 65.7 years vs. 68.3 years), were more likely to be men (78% vs. 73%) and had more severe CAD. Propensity score matching based on 23 baseline covariates yielded 4,301 well-balanced pairs. There was no difference in early mortality between PCI and CABG (2.4% vs. 2.3%; p = 0.721) after matching. The median and maximum follow-ups were 5.5 and 11.5 years, respectively. All-cause mortality (hazard ratio [HR]: 1.39; 95% CI: 1.28 to 1.51) and overall MACCEs (HR: 1.99; 95% CI: 1.86 to 2.12) were significantly higher with PCI compared with CABG. CONCLUSIONS: In patients with multivessel CAD and diabetes, CABG was associated with improved long-term mortality and freedom from MACCEs compared with PCI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".