Impact of Periprocedural Risk Predictors on Long-Term Outcomes in Patients with Diabetes Undergoing Coronary Artery Bypass Grafting
Bibliographic record
Abstract
Background and Objectives: In this study, we aim to analyze the impact of risk predictors on long-term outcomes in patients with diabetes undergoing isolated coronary artery bypass grafting (CABG). Materials and Methods: All consecutive patients undergoing isolated CABG between May 2005 and June 2021 were included in the study. Patients with and without diabetes were compared for baseline demographics and pre-operative characteristics. A propensity-matched analysis was used to compare the two groups. The primary outcome was long-term incidence of all-cause death. Results: Of a total of 4871 patients, propensity matching identified 1589 pairs of patients with and without diabetes that were included in the current study. Median follow-up was 5.8 years. All-cause death was recorded in 215/1589 (13.5%) vs. 169/1589 (10.6%) patients with and without diabetes, respectively (HR 1.3, p = 0.013). MACCE was also significantly higher in diabetic patients (HR 1.3, p = 0.049). Diabetes mellitus was identified as one of the independent predictors for all-cause mortality (HR 1.4, CI 1.2, 1.7) and MACCE (HR 1.2, CI 1.0, 1.3). Chronic obstructive pulmonary disease, peripheral vascular disease, and serum creatinine levels >2.0 mg/dL were found to be the only predictors of all-cause mortality in both diabetic and non-diabetic patient groups, when individually analyzed. Conclusions: Patients with diabetes undergoing isolated CABG had a significantly higher incidence of late all-cause death and MACCE compared to those without diabetes. The presence of diabetes mellitus predicts poorer long-term outcomes following CABG.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".