Machine-Learning Versus Traditional Scores for Predicting Outcomes After Coronary Artery Bypass Graft Surgery: A Systematic Review and Meta-Analysis
Aashray K. Gupta,
Ammar Zaka,
Daksh Tyagi,
Daud Mutahar,
Razeen Parvez,
Benjamin Muston,
Maria Farag,
Alexander Lombardo,
Aditya Eranki,
Ashley Wilson-Smith,
Gihwan Song,
Brandon Stretton,
Joshua G. Kovoor,
Stephen Bacchi,
Fabio Ramponi,
Justin C. Y,
Sarah Zaman,
Clara Chow,
Pramesh Kovoor,
Jayme S. Bennetts,
Guy J. Maddern,
Discipline of Surgery,
University of Adelaide,
South Australia,
Department of Medicine,
Gold Coast University Hospital,
School of Medicine,
University of Newcastle,
New South Wales,
School of Medicine,
Bond University,
Varsity Lakes,
Department of Cardiothoracic Surgery,
Royal Prince Alfred Hospital,
New South Wales,
Department of Surgery,
Princess Alexandra Hospital,
Department of Cardiothoracic Surgery,
Royal Hobart Hospital,
School of Medicine,
Deakin University,
Yale University,
New Haven,
New York University,
New York,
New York,
Westmead Applied Research Centre,
Faculty of Medicine and Health,
University of Sydney,
New South Wales,
Department of Cardiology,
Westmead Hospital,
New South Wales,
School of Medicine,
Monash University,
Department of Cardiothoracic Surgery,
Victorian Heart Hospital,
Australian Safety and Efficacy Register of New Interventional Procedures - Surgical,
Royal Australasian College of Surgeons,
South Australia,
Audit and Academic Surgery,
Royal Australasian College of Surgeons,
South Australia
Surgical Innovation
Published online on August 06, 2026
Surgical Innovation, Ahead of Print.
BackgroundCoronary artery bypass grafting (CABG) is associated with significant morbidity and mortality. Traditional risk scores, such as the Society of Thoracic Surgery (STS) and EuroSCORE II, have limitations in predicting outcomes, particularly in high-...