Can Machine Learning Aid the Selection of Percutaneous vs Surgical Revascularization?

Kai Ninomiya, Shigetaka Kageyama, SYNTAX Investigators, Hiroki Shiomi, Nozomi Kotoku, Shinichiro Masuda, Pruthvi C. Revaiah, Scot Garg, Neil O'Leary, David van Klaveren, Takeshi Kimura, Yoshinobu Onuma, Patrick W. Serruys*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

4 Citations (Scopus)

Abstract

Background: 

In patients with 3-vessel coronary artery disease (CAD) and/or left main CAD, individual risk prediction plays a key role in deciding between percutaneous coronary intervention (PCI) and coronary artery bypass grafting (CABG). Objectives: The aim of this study was to assess whether these individualized revascularization decisions can be improved by applying machine learning (ML) algorithms and integrating clinical, biological, and anatomical factors. 

Methods: 

In the SYNTAX (Synergy between PCI with Taxus and Cardiac Surgery) study, ML algorithms (Lasso regression, gradient boosting) were used to develop a prognostic index for 5-year death, which was combined, in the second stage, with assigned treatment (PCI or CABG) and prespecified effect-modifiers: disease type (3-vessel or left main CAD) and anatomical SYNTAX score. The model's discriminative ability to predict the risk of 5-year death and treatment benefit between PCI and CABG was cross-validated in the SYNTAX trial (n = 1,800) and externally validated in the CREDO-Kyoto (Coronary REvascularization Demonstrating Outcome Study in Kyoto) registry (n = 7,362), and then compared with the original SYNTAX score II 2020 (SSII-2020). 

Results: 

The hybrid gradient boosting model performed best for predicting 5-year all-cause death with C-indexes of 0.78 (95% CI: 0.75-0.81) in cross-validation and 0.77 (95% CI: 0.76-0.79) in external validation. The ML models discriminated 5-year mortality better than the SSII-2020 in the external validation cohort and identified heterogeneity in the treatment benefit of CABG vs PCI. 

Conclusions: 

An ML-based approach for identifying individuals who benefit from CABG or PCI is feasible and effective. Implementation of this model in health care systems—trained to collect large numbers of parameters—may harmonize decision making globally.

Original languageEnglish
Pages (from-to)2113-2124
Number of pages12
JournalJournal of the American College of Cardiology
Volume82
Issue number22
DOIs
Publication statusPublished - 28 Nov 2023

Bibliographical note

Publisher Copyright: © 2023

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