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Published on: March 27, 2018
Machine-Learning Versus Traditional Scores for Predicting Outcomes After Coronary Artery Bypass Graft Surgery: A
Aashray K Gupta1, Ammar Zaka2, Daksh Tyagi3
1Discipline of Surgery, University of Adelaide, Adelaide, South Australia, Australia.
Insights
Machine learning (ML) models show promise in predicting outcomes after coronary artery bypass grafting (CABG), outperforming traditional risk scores. Further validation is needed before routine clinical use.
Area of Science:
- Cardiology
- Medical Informatics
- Data Science
Background:
- Coronary artery bypass grafting (CABG) is linked to significant patient morbidity and mortality.
- Existing risk scores (e.g., STS, EuroSCORE II) have limitations in predicting outcomes, especially for high-risk individuals.
- Machine learning (ML) may offer improved prediction by identifying complex data patterns.
Purpose of the Study:
- To systematically review and compare the predictive efficacy of ML models against traditional risk scores for outcomes following CABG.
- To synthesize evidence on the performance of ML in predicting mortality after CABG.
Main Methods:
- A comprehensive literature search was conducted across major databases (PubMed, Embase, Web of Science, Cochrane Library) up to August 14th, 2025.
- Included studies utilized ML algorithms and traditional risk scores for predicting all-cause mortality post-CABG.
- Meta-analysis employed a linear mixed-effects model, with C-statistics as the primary measure of discrimination.
Main Results:
- Twenty-six studies involving 565,063 participants were analyzed.
- ML models demonstrated a significantly higher pooled C-statistic (0.82) compared to traditional risk scores (0.73) (P < 0.0001).
- The highest-performing ML model achieved a C-statistic of 0.98; calibration data was inconsistently reported.
Conclusions:
- ML approaches exhibit superior pooled discrimination for predicting mortality after CABG compared to traditional risk scores.
- Findings suggest ML's potential but require cautious interpretation due to study limitations (heterogeneity, bias, lack of external validation).
- Prospective, externally validated studies are essential to confirm ML's clinical superiority for pre-operative risk stratification.
Abstract:
BackgroundCoronary artery bypass grafting (CABG) is associated with significant morbidity and mortality. Traditional risk scores, such as the Society of Thoracic Surgery (STS) and EuroSCORE II, have limitations in predicting outcomes, particularly in high-risk patients. Machine learning (ML) models may address these issues by detecting nuanced data patterns not captured by conventional methods. This systematic review and meta-analysis compared the efficacy of ML models with traditional risk scores in predicting outcomes after CABG.MethodsA comprehensive literature search of records up to August 14th 2025, was conducted using PubMed, Embase, Web of Science, and the Cochrane Library. Studies included used ML algorithms and traditional risk scores to predict all-cause mortality (in-hospital, 30-day, or longer term as reported by each study) following CABG. Data extraction and quality assessment were independently performed by two reviewers. Meta-analyses were conducted using a linear mixed-effects model, with C-statistics as the primary measure of discrimination.ResultsTwenty-six studies, comprising 565 063 participants, met the inclusion criteria. The pooled C-statistic for ML models was 0.82 (95% CI 0.79-0.85), significantly higher than the 0.73 (95% CI 0.71-0.76) for traditional risk scores (P < 0.0001). The top-performing ML model achieved a C-statistic of 0.98 (CI 0.95-1.00). Calibration was reported inconsistently across studies and was synthesised narratively rather than quantitatively. Where reported, ML calibration was generally adequate but a robust head-to-head comparison with traditional risk scores was not possible. Subgroup analyses revealed consistent superior performance of ML models across various algorithms and covariate sets.ConclusionsIn this meta-analysis of predominantly internally-validated models, ML approaches showed higher pooled discrimination than traditional risk scores for mortality after CABG. However, given the small number of pooled studies, very high between-study heterogeneity (I2 = 98%), the predominance of high risk-of-bias studies, and the scarcity of external validation, these findings should be interpreted as supporting the promise of ML rather than establishing proof of clinical superiority. Confirmatory prospective, externally validated studies are required before ML can be recommended for routine pre-operative risk stratification.