Development and cross-site validation of machine-learning models for diagnosis and prognosis of stable angina with and without obstructive coronary artery disease: a study protocol

Introduction Angina with no obstructive coronary artery disease (ANOCA) affects millions and is frequently under-recognised because diagnostic pathways and risk tools predominantly target obstructive coronary artery disease (CAD). This protocol describes shared methods for two machine-learning (ML) studies: (1) differentiating ANOCA from stable angina with obstructive CAD and (2) predicting long-term mortality among patients with ANOCA and obstructive CAD.

Methods and analysis We will develop and cross-site validate ML classification models using a multicentre retrospective cohort drawn from the Alberta Provincial Project for Outcome Assessment in Coronary Heart Disease registry and institutional datasets from the University of Ottawa Heart Institute and the University Health Network. Eligible participants are adults (≥18 years) undergoing initial cardiac catheterisation for chest pain/anginal equivalents since 1995, excluding prior revascularisation, major structural heart disease and predefined non-anginal indications.

Outcomes are (1) ANOCA (0% to Model development will use nested cross-validation with stratified k-fold inner-loop tuning and leave-one-site-out cross-validation for repeated external validation.