Details

Autor(en) / Beteiligte

• Raparelli, Valeria
• Romiti, Giulio Francesco
• Di Teodoro, Giulia
• Seccia, Ruggiero
• Tanzilli, Gaetano
• Viceconte, Nicola
• Marrapodi, Ramona
• Flego, Davide
• Corica, Bernadette
• Cangemi, Roberto
• Pilote, Louise
• Basili, Stefania
• Proietti, Marco
• Palagi, Laura
• Stefanini, Lucia

Titel

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

Ist Teil von

• Clinical research in cardiology, 2023-09, Vol.112 (9), p.1263-1277

Ort / Verlag

Berlin/Heidelberg: Springer Berlin Heidelberg

Erscheinungsjahr

2023

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• 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. Graphical abstract