Personalized Treatment of Patients with Coronary Artery Disease: The Value and Limitations of Predictive Models

Risk prediction models are increasingly used in the management of coronary artery disease (CAD), with applications ranging from diagnostic stratification to prognostic assessment and therapeutic guidance. In the context of CAD and percutaneous coronary intervention, clinical decision-making often relies on risk scores to estimate the likelihood of ischemic and bleeding events and to tailor antithrombotic strategies accordingly. Traditional scores are derived from clinical, anatomical, procedural, and laboratory variables, and their performance is evaluated based on discrimination and calibration metrics. While many established models are simple, interpretable, and externally validated, their predictive ability is often moderate and may be limited by outdated derivation cohorts, overfitting, or lack of generalizability. Recent advances have introduced artificial intelligence and machine learning models that can process large, high-dimensional datasets and identify patterns not apparent through conventional methods, with the aim to incorporate complex data; however, they are not exempt from limitations and struggle with integration into clinical practice. Notably, ethical issues, such as equity in model application, over-stratification, and real-world implementation, are of critical importance. The ideal predictive model should be accurate, generalizable, and clinically actionable. This review aims at providing an overview of the main predictive models used in the field of CAD and to discuss methodological challenges, with a focus on strengths, limitations and areas of applicability of predictive models.