692 A Machine Learning Approach to Predict the Postoperative Length of Stay After Coronary Artery Bypass Grafting Using Preoperative Characteristics
Bibliographic record
Abstract
Abstract Aim Lengthy hospital length of stay (LOS) has a direct impact on healthcare costs. We aimed to design predictive models of prolonged LOS after coronary artery bypass grafting (CABG) with only preoperative characteristics and machine learning (ML) strategies. Method In a single centre retrospective analysis, 2,082 consecutive patients underwent first-time elective/urgent CABG: 1,262 has a short postoperative LOS (≤ 6 days) while the remaining 820 had a long LOS (> 6 days). 70/30 training/testing ratio and resampling methods were used, and cross-validation was conducted. Results The two groups differ significantly in terms of pre-operative variables: short LOS patients were younger (p < 0.01), more frequently male (p < 0.01) with lower BMI (p < 0.01) and better angina class (p < 0.01) and NYHA class (p < 0.01). Moreover, they had lower incidence of hypertension (p = 0.04), COPD (p < 0.01) and PVD (p < 0.01). The Logistic Euroscore was also better in this group (median 0.02 vs 0.03, p < 0.01). The predictive abilities of the ML models were as follows: logistic regression: Area under the Curve (AUC) = 0.71, accuracy = 0.69; Generalized additive model: AUC= 0.7, Accuracy = 0.68; Random Decision Forest: AUC = 0.7, Accuracy = 0.68; Naïve Bayes AUC = 0.63, Accuracy 0.58. Conclusions Developing a reliable predictive model with only pre-operative variables proved to be difficult, but several preoperative characteristics have a significant impact on the probability of prolonged LOS after CABG. Larger studies are needed to investigate the possibility of developing a reliable predictive model that would help to improve surgical planning.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".