Highlights
- •Machine learning (ML) can address the limitations of regression-based models.
- •The ML-derived model exhibited better predictions than an established model.
- •The ML-based approach can be used for risk stratification in clinical practice.
Abstract
Background and aims
Risk stratification for three-vessel coronary artery disease (3VD) remains an important
clinical challenge. In this study, we utilized machine learning (ML), which can address
the limitations of traditional regression-based models, to develop a novel model to
assess mortality risk in patients with 3VD.
Methods
This study was based on a prospective cohort of 8943 participants with 3VD consecutively
enrolled between 2004 and 2011. An ML-derived random forest model was trained and
tested to predict 4-year mortality. The predictability of the model was compared with
that of an established model, the Synergy Between Percutaneous Coronary Intervention
With Taxus and Cardiac Surgery score II (SSII), among 3VD patients undergoing percutaneous
coronary intervention (PCI), coronary artery bypass grafting (CABG), and medical therapy
(MT) alone.
Results
The all-cause mortality was 7.5% (667 patients) over the 4-year follow-up period.
The correlation-based feature selection algorithm selected 18 of the 94 features to
develop the ML model. In the testing dataset, the ML-derived model achieved an area
under the curve of 0.81 for 4-year mortality prediction. Its predictability was significantly
better than that of the SSII among patients undergoing PCI (0.80 vs. 0.70, p < 0.001) or CABG (0.80 vs. 0.67, p < 0.001). The model also outperformed the SSII in patients receiving MT alone (ML:
0.75 vs. SSII for PCI: 0.70 or SSII for CABG: 0.66, p < 0.001).
Conclusions
This ML-based approach exhibited better performance in risk stratification for 3VD
compared with the conventional method. Further validation studies are needed to confirm
these findings.
Graphical abstract
Graphical Abstract
Keywords
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Article info
Publication history
Published online: January 12, 2023
Accepted:
January 11,
2023
Received in revised form:
December 20,
2022
Received:
July 3,
2022
Identification
Copyright
© 2023 Elsevier B.V. All rights reserved.
