A machine-learning-based bio-psycho-social model for the prediction of non-obstructive and obstructive coronary artery disease

Abstract

Abstract 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. Aim 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 CAD. Methods From the EVA study, we analyzed a consecutive unselected cohort of adults hospitalized 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 cytometric 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 analyzed. The ML-based model (83% accuracy and 87% precision) revealed that while obstructive CAD associated with higher frailty index (i.e., lower physiological reserve), older age and a cytokine signature characterized by IL-1β, IL-12p70 and IL-33, non-obstructive CAD was more likely associated with higher gender score (i.e., social characteristics traditionally ascribed to women, regardless of biological sex) 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 the observed associations. Funding Acknowledgement Type of funding sources: Public Institution(s). Main funding source(s): Italian Ministry of Education, Research and University, Scientific Independence of young Researcher (SIR)