Predicting functional results of percutaneous coronary intervention using machine learning modelling

Background: Post-percutaneous coronary intervention (PCI) Murray's law-based quantitative flow ratio (μFR) is associated with long-term clinical outcomes. A tool capable of accurately predicting post-PCI μFR before intervention could support procedural planning, reduce the risk of suboptimal physiological results, and improve prognosis. Objectives: To develop and validate machine-learning models that predict continuous post-PCI μFR using only pre-procedural angiographic, physiological and clinical data, and to assess their ability to classify PCI outcomes as optimal (μFR ≥ 0.91) or sub-optimal (μFR < 0.91). Methods: Four machine learning models were trained using pre-PCI variables. Internal bootstrap validation (1000 iterations) identified the best-performing model based on lowest root mean square error (RMSE) for continuous prediction. Predicted μFR values were subsequently used to classify PCI outcomes. Results: In 343 vessels (291 patients), machine learning achieved high accuracy for continuous post-PCI μFR prediction (RMSE 0.036; 95% CI: 0.033-0.040; mean absolute error 0.030; 95% CI: 0.027-0.032; mean absolute percentage error 3.2%; 95% CI: 2.9-3.5), indicating reliable estimation of post-PCI μFR using only pre-procedural data. When the predicted μFR was used to classify PCI outcomes, performance remained clinically meaningful, with accuracy 0.72 (95% CI: 0.70-0.75), area under the curve 0.72 (95% CI: 0.69-0.74), sensitivity 0.90 (95% CI: 0.88-0.93), and specificity 0.29 (95% CI: 0.23-0.34). The high sensitivity ensures reliable upfront identification of vessels likely to achieve optimal physiology. Conclusions: Machine-learning models accurately predict post-PCI μFR and reliably distinguish optimal from sub-optimal outcomes before intervention. This approach supports personalized PCI planning and improves strategy selection.