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Ninomiya K, Serruys PW, Garg S, Kageyama S, Kotoku N, Masuda S, Revaiah PC, O'leary N, Kappetein AP, Mack MJ, Holmes DR, Davierwala PM, Mohr FW, Thuijs DJFM, Onuma Y. γ-Glutamyl Transferase and Long-Term Survival in the SYNTAXES Trial: Is It Just the Liver? J Am Heart Assoc 2024; 13:e032276. [PMID: 38563386 PMCID: PMC11262495 DOI: 10.1161/jaha.123.032276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 12/27/2023] [Indexed: 04/04/2024]
Abstract
BACKGROUND Recently, machine learning algorithms have identified preprocedural γ-glutamyl transferase (GGT) as a significant predictor of long-term mortality after coronary revascularization in the SYNTAX (Synergy Between PCI [Percutaneous Coronary Intervention] With Taxus and Cardiac Surgery) trial. The aim of the present study is to investigate the impact of preprocedural GGT on 10-year all-cause mortality in patients with complex coronary artery disease after revascularization. METHODS AND RESULTS The SYNTAX trial was a randomized trial comparing PCI with coronary artery bypass grafting in 1800 patients with complex coronary artery disease. The present report is a post hoc subanalysis of the SYNTAXES (Synergy Between PCI With Taxus and Cardiac Surgery Extended Survival) trial, an investigator-driven extended 10-year follow-up of the SYNTAX trial. The association between preprocedural GGT and 10-year all-cause mortality was investigated. The mean values of GGT for men and women were 43.5 (SD, 48.5) and 36.4 (SD, 46.1) U/L, respectively. In multivariable Cox regression models adjusted by traditional risk factors, GGT was an independent predictor for all-cause death at 10-year follow-up, and each SD increase in log-GGT was associated with a 1.24-fold risk of all cause death at 10-year follow-up (95% CI, 1.10-1.40). According to previously reported sex-related GGT thresholds, patients with higher GGT level had a 1.74-fold risk of all-cause death at 10-year follow-up (95% CI, 1.32-2.29) compared with patients with lower GGT level. CONCLUSIONS Preprocedural GGT is an independent predictor of 10-year mortality after coronary revascularization in patients with complex coronary artery disease. In patients with elevated GGT, strong secondary prevention may be required after revascularization and must be studied prospectively. REGISTRATION URL: https://clinicaltrials.gov/study/NCT03417050.
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Affiliation(s)
- Kai Ninomiya
- Department of CardiologyUniversity of GalwayIreland
| | - Patrick W. Serruys
- Department of CardiologyUniversity of GalwayIreland
- National Heart and Lung Institute, Imperial College LondonLondonUnited Kingdom
| | - Scot Garg
- Department of CardiologyRoyal Blackburn HospitalBlackburnUnited Kingdom
| | | | | | | | | | - Neil O'leary
- Department of CardiologyUniversity of GalwayIreland
| | - Arie Pieter Kappetein
- Department of Cardiothoracic SurgeryErasmus University Medical CentreRotterdamthe Netherlands
| | - Michael J. Mack
- Department of Cardiothoracic SurgeryBaylor University Medical CenterDallasTX
| | - David R. Holmes
- Department of Cardiovascular Diseases and Internal MedicineMayo ClinicRochesterMN
| | - Piroze M. Davierwala
- University Department of Cardiac Surgery, Heart Centre LeipzigLeipzigGermany
- Division of Cardiovascular Surgery, Peter Munk Cardiac CentreToronto General Hospital, University Health NetworkTorontoOntarioCanada
- Department of SurgeryUniversity of TorontoCanada
| | - Friedrich W. Mohr
- University Department of Cardiac Surgery, Heart Centre LeipzigLeipzigGermany
| | - Daniel J. F. M. Thuijs
- Department of Cardiothoracic SurgeryErasmus University Medical CentreRotterdamthe Netherlands
| | - Yoshinobu Onuma
- Department of CardiologyUniversity of GalwayIreland
- Galway University HospitalGalwayIreland
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Serruys PW, Kageyama S, Onuma Y. Cardiology's new crystal ball: machine learning for outcome prediction. Eur Heart J 2024; 45:610-612. [PMID: 38243801 DOI: 10.1093/eurheartj/ehad847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/22/2024] Open
Affiliation(s)
- Patrick W Serruys
- CORRIB Research Centre for Advanced Imaging and Core Laboratory, University of Galway, University Road, Galway H91 TK33, Ireland
| | - Shigetaka Kageyama
- CORRIB Research Centre for Advanced Imaging and Core Laboratory, University of Galway, University Road, Galway H91 TK33, Ireland
| | - Yoshinobu Onuma
- CORRIB Research Centre for Advanced Imaging and Core Laboratory, University of Galway, University Road, Galway H91 TK33, Ireland
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Ninomiya K, Kageyama S, Shiomi H, Kotoku N, Masuda S, Revaiah PC, Garg S, O'Leary N, van Klaveren D, Kimura T, Onuma Y, Serruys PW. Can Machine Learning Aid the Selection of Percutaneous vs Surgical Revascularization? J Am Coll Cardiol 2023; 82:2113-2124. [PMID: 37993203 DOI: 10.1016/j.jacc.2023.09.818] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 09/15/2023] [Accepted: 09/20/2023] [Indexed: 11/24/2023]
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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Affiliation(s)
- Kai Ninomiya
- Department of Cardiology, University of Galway, Galway, Ireland
| | | | - Hiroki Shiomi
- Department of Cardiovascular Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Nozomi Kotoku
- Department of Cardiology, University of Galway, Galway, Ireland
| | | | | | - Scot Garg
- Department of Cardiology, Royal Blackburn Hospital, Blackburn, United Kingdom
| | - Neil O'Leary
- Department of Cardiology, University of Galway, Galway, Ireland
| | - David van Klaveren
- Department of Public Health, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Takeshi Kimura
- Department of Cardiovascular Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Yoshinobu Onuma
- Department of Cardiology, University of Galway, Galway, Ireland
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Serruys PW, Revaiah PC, Ninomiya K, Masuda S, Kotoku N, Kageyama S, Onuma Y, Morel MA, Garg S, Feldman T, Kappetein AP, Holmes DR, Mack MJ, Mohr FW. 10 Years of SYNTAX: Closing an Era of Clinical Research After Identifying New Outcome Determinants. JACC. ASIA 2023; 3:409-430. [PMID: 37396431 PMCID: PMC10308124 DOI: 10.1016/j.jacasi.2023.03.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 02/21/2023] [Accepted: 03/24/2023] [Indexed: 07/04/2023]
Abstract
The SYNTAX trial randomized patients equally eligible for coronary artery bypass grafting or percutaneous coronary intervention using the Heart Team approach. The SYNTAXES study achieved a follow-up rate of 93.8% and reported the 10-year vital status. Factors associated with increased mortality at 10 years were pharmacologically treated diabetes mellitus, increased waist circumference, reduced left ventricular function, prior cerebrovascular and peripheral vascular disease, western Europe and North American descent, current smoking, chronic obstructive pulmonary disease, elevated C-reactive protein, anemia, and an increase in HbA1c. Procedural factors associated with higher 10 years mortality include periprocedural myocardial infarction, extensive stenting, small stents, ≥1 heavily calcified lesion, ≥1 bifurcation lesion, residual SYNTAX score >8, and staged percutaneous coronary intervention. Optimal medical therapy at 5 years, use of statins, on-pump coronary artery bypass grafting, multiple arterial grafts, and higher physical component score and mental component score were associated with lower mortality at 10 years. Numerous scores and prediction models were developed to help individualize risk assessment. Machine learning has emerged as a novel approach for developing risk models.
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Affiliation(s)
- Patrick W. Serruys
- Department of Cardiology, National University of Ireland, Galway (NUIG), and CORRIB Research Centre for Advanced Imaging and Core Laboratory, Galway, Ireland
| | - Pruthvi C. Revaiah
- Department of Cardiology, National University of Ireland, Galway (NUIG), and CORRIB Research Centre for Advanced Imaging and Core Laboratory, Galway, Ireland
| | - Kai Ninomiya
- Department of Cardiology, National University of Ireland, Galway (NUIG), and CORRIB Research Centre for Advanced Imaging and Core Laboratory, Galway, Ireland
| | - Shinichiro Masuda
- Department of Cardiology, National University of Ireland, Galway (NUIG), and CORRIB Research Centre for Advanced Imaging and Core Laboratory, Galway, Ireland
| | - Nozomi Kotoku
- Department of Cardiology, National University of Ireland, Galway (NUIG), and CORRIB Research Centre for Advanced Imaging and Core Laboratory, Galway, Ireland
| | - Shigetaka Kageyama
- Department of Cardiology, National University of Ireland, Galway (NUIG), and CORRIB Research Centre for Advanced Imaging and Core Laboratory, Galway, Ireland
| | - Yoshinobu Onuma
- Department of Cardiology, National University of Ireland, Galway (NUIG), and CORRIB Research Centre for Advanced Imaging and Core Laboratory, Galway, Ireland
| | - Marie Angele Morel
- Department of Cardiology, National University of Ireland, Galway (NUIG), and CORRIB Research Centre for Advanced Imaging and Core Laboratory, Galway, Ireland
| | - Scot Garg
- Department of Cardiology, Royal Blackburn Hospital, Blackburn, United Kingdom
| | - Ted Feldman
- Edwards Lifesciences, Irvine, California, USA
| | - Arie Pieter Kappetein
- Department of Cardiothoracic Surgery, Erasmus University Medical Centre, Rotterdam, the Netherlands
| | - David R. Holmes
- Department of Cardiovascular Diseases and Internal Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Michael J. Mack
- Department of Cardiothoracic Surgery, Baylor Scott & White Health, Dallas, Texas, USA
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