Abstract Number: PB0002
Meeting: ISTH 2021 Congress
Background: Although cardiovascular disease is the leading cause of mortality in both females and males, women are more likely to have non-obstructive ischemic heart disease (IHD) than men. However, the underlying sex- and gender-specific mechanisms and differences in IHD manifestations are still not fully understood.
Aims: To develop an interpretable machine learning (ML) model to gain insight on the clinical, functional, biological and psychosocial features playing a major role in the supervised prediction of non-obstructive versus obstructive coronary artery disease (CAD).
Methods: From the EVA study, we analysed a consecutive unselected cohort of adults hospitalised for IHD undergoing coronary angiography. Non-obstructive CAD was defined by a coronary stenosis at the angiogram <50%. Baseline clinical and psycho-socio-cultural characteristics were used for computing a frailty index based on Rockwood and Mitnitsky model, and gender score according to GENESIS-PRAXY methodology. The serum concentration of inflammatory cytokines was measured with a multiplex flow cytometry assay. An XGBoost classifier combined to an explainable artificial intelligence tool (SHAP) was employed to identify 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) revealed that while obstructive CAD associated with a lower frailty index (i.e., lower physiological reserve), older age and a cytokine signature characterised by IL-1β, IL-12p70 and IL-33, non-obstructive CAD is more likely associated with higher gender score (i.e., social characteristics traditionally ascribed to women, regardless of biological sex) and with a cytokine signature characterised by IL-18, IL-8, IL-23.
Conclusions: Integrating clinical, biological and psychosocial features, we have optimised a sex- and gender-unbiased model that discriminates obstructive and non-obstructive CAD. Further mechanistic studies will shed light on the biological plausibility of the observed associations.
To cite this abstract in AMA style:Raparelli V, Proietti M, Romiti GF, Seccia R, Di Teodoro G, Tanzilli G, Marrapodi R, Corica B, Flego D, Cangemi R, Palagi L, Basili S, Stefanini L. A Machine-learning-Based Bio-psycho-Social Model for the Prediction of Non-obstructive and Obstructive Coronary Artery Disease [abstract]. Res Pract Thromb Haemost. 2021; 5 (Suppl 1). https://abstracts.isth.org/abstract/a-machine-learning-based-bio-psycho-social-model-for-the-prediction-of-non-obstructive-and-obstructive-coronary-artery-disease/. Accessed September 24, 2021.
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