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Can Machine Learning Aid the Selection of Percutaneous vs Surgical Revascularization?
Ninomiya, Kai; Kageyama, Shigetaka; Shiomi, Hiroki; Kotoku, Nozomi; Masuda, Shinichiro; Revaiah, Pruthvi C; Garg, Scot; O'Leary, Neil; van Klaveren, David; Kimura, Takeshi; Onuma, Yoshinobu; Serruys, Patrick W.
Afiliação
  • Ninomiya K; Department of Cardiology, University of Galway, Galway, Ireland.
  • Kageyama S; Department of Cardiology, University of Galway, Galway, Ireland.
  • Shiomi H; Department of Cardiovascular Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
  • Kotoku N; Department of Cardiology, University of Galway, Galway, Ireland.
  • Masuda S; Department of Cardiology, University of Galway, Galway, Ireland.
  • Revaiah PC; Department of Cardiology, University of Galway, Galway, Ireland.
  • Garg S; Department of Cardiology, Royal Blackburn Hospital, Blackburn, United Kingdom.
  • O'Leary N; Department of Cardiology, University of Galway, Galway, Ireland.
  • van Klaveren D; Department of Public Health, Erasmus University Medical Center, Rotterdam, the Netherlands.
  • Kimura T; Department of Cardiovascular Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
  • Onuma Y; Department of Cardiology, University of Galway, Galway, Ireland.
  • Serruys PW; Department of Cardiology, University of Galway, Galway, Ireland. Electronic address: patrick.w.j.c.serruys@gmail.com.
J Am Coll Cardiol ; 82(22): 2113-2124, 2023 11 28.
Article em En | MEDLINE | ID: mdl-37993203
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. (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).
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Doença da Artéria Coronariana / Stents Farmacológicos / Intervenção Coronária Percutânea Limite: Humans Idioma: En Revista: J Am Coll Cardiol Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Irlanda

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Doença da Artéria Coronariana / Stents Farmacológicos / Intervenção Coronária Percutânea Limite: Humans Idioma: En Revista: J Am Coll Cardiol Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Irlanda