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

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. (Synergy Between PCI With TAXUS and Cardiac Surgery: SYNTAX Extended Survival [SYNTAXES]; NCT03417050; SYNTAX Study: TAXUS Drug-Eluting Stent Versus Coronary Artery Bypass Surgery for the Treatment of Narrowed Arteries; NCT00114972).
Competing Interests: Funding Support and Author Disclosures The SYNTAX trial, during 0 to 5 years follow-up, was funded by Boston Scientific Corporation. The sponsor had no role in the study design, data collection, data analyses, and interpretation of the study data, nor were they involved in the decision to publish the final paper. The principal investigators and authors had complete scientific freedom. Prof Serruys has received consultancy fees from SMT (Sahajanand Medical technological), Novartis, Xeltis, Merillife, and Philips. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.
(Copyright © 2023. Published by Elsevier Inc.)