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A machine-learning based bio-psycho-social model for the prediction of non-obstructive and obstructive coronary
Valeria Raparelli1,2,3,4, Giulio Francesco Romiti5,6, Giulia Di Teodoro7
1Department of Experimental Medicine, Sapienza University of Rome, Rome, Italy. valeria.raparelli@unife.it.
Insights
Machine learning accurately predicts obstructive vs. non-obstructive coronary artery disease (CAD) by integrating clinical, biological, and psycho-social data. This model aids in understanding CAD mechanisms and improving patient stratification.
Area of Science:
- Cardiology
- Artificial Intelligence
- Biomedical Engineering
Background:
- Mechanisms of myocardial ischemia in obstructive and non-obstructive coronary artery disease (CAD) remain incompletely understood.
- The interplay of clinical, functional, biological, and psycho-social factors in CAD requires further elucidation.
Purpose of the Study:
- To develop a machine learning (ML) model for supervised prediction of obstructive versus non-obstructive CAD.
- To identify key features differentiating obstructive and non-obstructive CAD using explainable AI.
Main Methods:
- Analysis of adults hospitalized for ischemic heart disease (IHD) from the EVA study undergoing coronary angiography.
- Computation of frailty and gender scores, and measurement of serum inflammatory cytokines.
- Application of an XGBoost classifier with SHAP (SHapley Additive exPlanations) for feature importance analysis.
Main Results:
- An ML model achieved 83% accuracy and 87% precision in discriminating CAD types.
- Obstructive CAD associated with higher frailty, older age, and IL-1β, IL-12p70, IL-33 cytokines.
- Non-obstructive CAD linked to higher gender score and IL-18, IL-8, IL-23 cytokines.
Conclusions:
- An optimized, sex- and gender-unbiased ML model effectively discriminates obstructive and non-obstructive CAD.
- Integration of clinical, biological, and psycho-social data enhances CAD classification.
- Further research is needed to explore the biological plausibility of identified associations.
Background:
Mechanisms of myocardial ischemia in obstructive and non-obstructive coronary artery disease (CAD), and the interplay between clinical, functional, biological and psycho-social features, are still far to be fully elucidated.
Objectives:
To develop a machine-learning (ML) model for the supervised prediction of obstructive versus non-obstructive CAD.
Methods:
From the EVA study, we analysed adults hospitalized for IHD undergoing conventional coronary angiography (CCA). Non-obstructive CAD was defined by a stenosis < 50% in one or more vessels. Baseline clinical and psycho-socio-cultural characteristics were used for computing a Rockwood and Mitnitski frailty index, and a gender score according to GENESIS-PRAXY methodology. Serum concentration of inflammatory cytokines was measured with a multiplex flow cytometry assay. Through an XGBoost classifier combined with an explainable artificial intelligence tool (SHAP), we identified the most influential features in discriminating obstructive versus non-obstructive CAD.
Results:
Among the overall EVA cohort (n = 509), 311 individuals (mean age 67 ± 11 years, 38% females; 67% obstructive CAD) with complete data were analysed. The ML-based model (83% accuracy and 87% precision) showed that while obstructive CAD was associated with higher frailty index, older age and a cytokine signature characterized by IL-1β, IL-12p70 and IL-33, non-obstructive CAD was associated with a higher gender score (i.e., social characteristics traditionally ascribed to women) and with a cytokine signature characterized by IL-18, IL-8, IL-23.
Conclusions:
Integrating clinical, biological, and psycho-social features, we have optimized a sex- and gender-unbiased model that discriminates obstructive and non-obstructive CAD. Further mechanistic studies will shed light on the biological plausibility of these associations.
Clinical Trial Registration:
NCT02737982.
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