Does diabetes mellitus increase the mortality risk in coronary artery disease patients undergoing coronary artery bypass grafting surgery at the National Heart Institute of Kuala Lumpur?
Bibliographic record
Abstract
Background: Multiple studies had shown that coronary artery disease (CAD) has been the principal cause of mortality in patients with diabetes mellitus (DM).Furthermore, DM has always been a major risk predictor for unfavorable outcomes in patients undergoing cardiac revascularization either percutaneous coronary intervention (PCI) or coronary artery bypass grafting (CABG) surgery.Objective: To investigate whether the presence of DM increase mortality risk in patients undergoing CABG.Methods: A retrospective single-center study was performed.A special database was created to include all EuroSCORE II variables, EuroSCORE II predicted mortality and actual mortality of 1718 patients undergoing Coronary Artery Bypass (CABG) surgery in Malaysia from 1st January 2016 till 31st December 2016.Univariate and multivariate logistic regressions were done to identify significant predictors of in-hospital mortality among this group of patients.Results: More than half of the patients undergoing CABG surgery are diabetic (56.3%) while 20.3% are on long-term insulin.In terms of mortality, a significantly higher proportion of in-hospital mortality was observed among patients with DM (5.7%) compared to those without DM (3.4%).On univariate logistic regression analysis, both non-insulin dependent DM (OR:1.737,95% CI 1.072-2.815,p=0.025) and insulin-dependent DM (OR: 1.960, 95% CI: 1.209-3.179,p=0.006) are significant predictors of in-hospital mortality in this group of patients undergoing CABG surgery.However, in multivariate logistic regression, which took into consideration of other related variables in the EuroSCORE II, only female gender, age more than or equal to 65 years old, serum creatinine more than 120 mol/litre and longer ICU stays are significant predictors of in-hospital post-CABG mortality.Conclusion: In conclusion, a significant proportion of patients undergoing CABG surgery in IJN are actually diabetics while a higher in-hospital mortality risk post-CABG was observed in patients with DM.However, insulin-dependent diabetes mellitus was not a significant risk factor for in-hospital mortality in this group of patients.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".