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

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

The Prognostic Role of Education Attainment Level in Individuals With Ischemic Heart Disease: Analysis From the Endocrine Vascular Approach Study

Introduction: Although adverse outcomes from cardiovascular disease (CVD) have been on a linear decline, the burden remains high. Addressing the social determinants of health in the care of CVD patients is emerging as a strategy for improving outcomes. Educational attainment level (EAL), a proxy of socioeconomic status, has been associated with both cardiovascular risk and patient’s ability to self-manage the complex cardiovascular treatment. Objective: To assess the impact of EAL on major adverse cardiovascular events (MACE) and all-cause death in patients with ischemic heart disease (IHD). Methods: Endocrine Vascular disease Approach (EVA) is a prospective observational study recruiting hospitalized patients with IHD undergoing coronary angiography and/or percutaneous coronary interventions. Socio-demographics and clinical data, including the level of multimorbidity defined by a Charlson Comorbidity Index≥ 4, were collected. A low-EAL, assessed through a self-reported questionnaire, was defined if at least elementary/middle school education was completed. The primary outcome was the occurrence of MACE (i.e. cardiovascular death, non-fatal myocardial infarction, non-fatal stroke) and a secondary composite endpoint (i.e. all-cause death, non-fatal myocardial infarction, non-fatal stroke) was also analyzed. Results: Among 460 individuals (mean age 67±11, 30% women) with IHD, 252 (55%) had a low-EAL. Individuals with low-EAL were younger and more likely to have heart failure, vascular encephalopathy, and high multimorbidity. A low-EAL was associated with a higher risk of MACE compared with higher EAL (Log-rank=12.29, p<0.001) with similar results for the secondary outcome (Log-rank=9.45, p=0.002). In the adjusted multivariate regression analysis, low EAL was independently associated with MACE [Hazard Ration (HR): 2.31, 95% Confidence Interval (CI): 1.23-4.34, p=0.010] and secondary outcome [HR: 1.73, 95%CI 1.02-2.92, p=0.042] compared to high-EAL. Conclusion: Individuals with IHD and low-EAL had a higher risk of MACE and all-cause death. Interventions that specifically address health literacy and cognition should be tested among these high-risk patients to improve outcomes