Long-Term Outcomes after Coronary Artery Bypass Grafting with Risk Stratification
Bibliographic record
Abstract
Background: Risk stratification of long-term outcomes for patients undergoing Coronary artery bypass grafting has enormous potential clinical importance. Aim: To develop risk stratification models for predicting long-term outcomes following coronary artery bypass graft (CABG) surgery. Methods: We retrospectively revised the electronic medical records of 2330 patients who underwent adult Cardiac surgery between August 2016 and December 2022 at Madinah Cardiac Center, Saudi Arabia. Three hundred patients fulfilled the eligibility criteria of CABG operations with a complete follow-up period of at least 24 months, and data reporting. The collected data included patient demographics, comorbidities, laboratory data, pharmacotherapy, echocardiographic parameters, procedural details, postoperative data, in-hospital outcomes, and follow-up data. Our follow-up was depending on the clinical status (NYHA class), chest pain recurrence, medication dependence and echo follow-up. A univariate analysis was performed between each patient risk factor and the long-term outcome to determine the preoperative, operative, and postoperative factors significantly associated with each long-term outcome. Then a multivariable logistic regression analysis was performed with a stepwise, forward selection procedure. Significant (p Results: The incidence of all-cause mortality during hospital admission or follow-up period was 2.3%. Other long-term outcomes included all-cause recurrent hospitalization (9.8%), recurrent chest pain (2.4%), and the need for revascularization by using a stent in 5 (3.0%) patients. Thirteen (4.4%) patients suffered heart failure and they were on the maximum anti-failure medications. The model for predicting all-cause mortality included the preoperative EF ≤ 35% (AOR: 30.757, p = 0.061), the bypass time (AOR: 1.029, p = 0.003), and the duration of ventilation following the operation (AOR: 1.237, p = 0.021). The model for risk stratification of recurrent hospitalization comprised the preoperative EF ≤ 35% (AOR: 6.198, p p = 0.023), low postoperative cardiac output (AOR: 3.622, p = 0.007), and the development of postoperative atrial fibrillation (AOR: 2.787, p = 0.038). Low postoperative cardiac output was the only predictor that significantly contributed to recurrent chest pain (AOR: 11.66, p = 0.004). Finally, the model consisted of low postoperative cardiac output (AOR: 5.976, p p = 0.019) was significantly associated with an increased likelihood of the future need for revascularization using a stent. Conclusions: A risk prediction model was developed in a Saudi cohort for predicting all-cause mortality risk during both hospital admission and the follow-up period of at least 24 months after isolated CABG surgery. A set of models were also developed for predicting long-term risks of all-cause recurrent hospitalization, recurrent chest pain, heart failure, and the need for revascularization by using stents.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| 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".