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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".