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Development and cross-site validation of machine-learning models for diagnosis and prognosis of stable angina with
Jiawen Deng1, Shubh K Patel1,2,3, Marinda Fung2,3
1Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.
Insights
This study uses machine learning to differentiate angina with no obstructive coronary artery disease (ANOCA) from obstructive coronary artery disease (CAD) and predict long-term mortality in these patients. The findings aim to improve diagnosis and risk stratification for ANOCA.
Area of Science:
- Cardiology
- Machine Learning
- Health Informatics
Background:
- Angina with no obstructive coronary artery disease (ANOCA) affects millions but is often under-recognized.
- Current diagnostic tools primarily focus on obstructive coronary artery disease (CAD), leading to potential underdiagnosis of ANOCA.
- There is a need for improved methods to differentiate ANOCA and predict outcomes for patients with this condition.
Purpose of the Study:
- To develop and validate machine learning (ML) models for differentiating ANOCA from obstructive CAD.
- To create ML models for predicting long-term (1, 3, and 5-year) mortality in patients with ANOCA and obstructive CAD.
- To deploy these models as a web-based clinical risk calculator for improved patient management.
Main Methods:
- Utilized a multicentre retrospective cohort from Canadian registries, including adults undergoing cardiac catheterisation.
- Developed and validated ML classification models using nested cross-validation and leave-one-site-out cross-validation.
- Employed various algorithms (elastic-net logistic regression, random forest, LightGBM, multilayer perceptron) with hyperparameter optimization and feature selection techniques.
Main Results:
- The study protocol outlines the development of ML models to distinguish ANOCA from obstructive CAD.
- Models will also predict 1, 3, and 5-year mortality separately for ANOCA and obstructive CAD cohorts.
- Performance, threshold tuning, explainability, and subgroup fairness will be rigorously assessed.
Conclusions:
- Machine learning holds significant potential for improving the diagnosis and risk stratification of ANOCA.
- Validated ML models can aid clinicians in better managing patients with chest pain and no obstructive coronary artery disease.
- The development of a web-based risk calculator aims to facilitate clinical decision-making and improve patient outcomes.
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 <50% stenosis) versus obstructive CAD (≥50% stenosis) and (2) 1, 3 and 5-year mortality, modelled separately for ANOCA and obstructive CAD.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. Candidate predictors will be harmonised across sites, filtered for missingness and refined using expert/directed acyclic graph-guided selection plus Boruta and Least Absolute Shrinkage and Selection Operator. Preprocessing includes appropriate encoding, missing-data imputation (multivariate imputation by chained equations) and feature scaling. Algorithms will include elastic-net logistic regression, random forest, LightGBM and multilayer perceptron models; hyperparameters will be optimised via Bayesian optimisation. Performance and threshold tuning will be reported. Explainability and subgroup fairness will be assessed using SHapley Additive exPlanations. Final models will be deployed as a web-based clinical risk calculator.
Ethics And Dissemination:
Ethics approval has been obtained from the University of Calgary and the University Health Network (#24-5916). Analyses will use deidentified data in secure environments; only aggregate results will be reported. Findings will be disseminated via peer-reviewed publications, conferences and a web-based calculator.
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