A machine-learning based bio-psycho-social model for the prediction of non-obstructive and obstructive coronary artery disease
Authors: 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 ,   EVA Investigators, Tiberti Claudio , Panimolle Federica , Isidori Andrea , Giannetta Elisa , Venneri Mary Anna , Napoleone Laura , Novo Marta , Quattrino Silvia , Ceccarelli Simona , Anastasiadou Eleni , Megiorni Francesca , Marchese Cinzia , Mangieri Enrico , Tanzilli Gaetano , Viceconte Nicola , Barillà Francesco , Gaudio Carlo , Paravati Vincenzo , Tellan Guglielmo , Ettorre Evaristo , Servello Adriana , Miraldi Fabio , Moretti Andrea , Tanzilli Alessandra , Mazzonna Piergiovanni , Al Kindy Suleyman , Iorio Riccardo , Di Iorio Martina , Petriello Gennaro , Gioffrè Laura , Indolfi Eleonora , Pero Gaetano , Cocco Nino , Iannetta Loredana , Giannuzzi Sara , Centaro Emilio , Sergi Sonia Cristina , Pignatelli Pasquale , Amoroso Daria , Bartimoccia Simona , Minisola Salvatore , Morelli Sergio , Fraioli Antonio , Nocchi Silvia , Fontana Mario , Toriello Filippo , Ruscio Eleonora , Todisco Tommaso , Sperduti Nicolò , Santangelo Giuseppe , Visioli Giacomo , Vano Marco , Borgi Marco , Antonini Ludovica Maria , Robuffo Silvia , Tucci Claudia , Rossoni Agostino , Spugnardi Valeria , Vernile Annarita , Santoliquido Mariateresa , Santori Verdiana , Tosti Giulia , Recchia Fabrizio , Morricone Francesco , Scacciavillani Roberto , Lipari Alice , Zito Andrea , Testa Floriana , Ricci Giulia , Vellucci Ilaria , Vincenti Marianna , Pietropaolo Silvia , Scala Camilla , Rubini Nicolò , Tomassi Marta , Rozzi Gloria , Santomenna Floriana , Cantelmi Claudio , Costanzo Giacomo , Rumbolà Lucas , Giarrizzo Salvatore , Sapia Carlotta , Scotti Biagio , Talerico Giovanni , Toni Danilo , Falcou Anne , Pilote Louise , Kaur Amanpreet , Behlouli Hassan , Vestri Anna Rita , Ferroni Patrizia , Crescioli Clara , Antinozzi Cristina , Pignataro Francesca Serena , Bellini Tiziana , Zuliani Giovanni , Passaro Angelina , Gloria Brombo , Cutini Andrea , Capatti Eleonora , Dalla Nora Edoardo , Di Vece Francesca , D’Amuri Andrea , Romagnoli Tommaso , Polastri Michele , Violi Alessandra , Fortunato Valeria , Bella Alessandro , Greco Salvatore , Spaggiari Riccardo , Scaglione Gerarda , Di Vincenzo Alessandra , Manfredini Roberto , De Giorgi Alfredo , Carnevale Roberto , Nocella Cristina , Catalano Carlo , Carbone Iacopo , Galea Nicola , Suppa Marianna , Rosa Antonello , Galardo Gioacchino , Alessandroni Maria , Coppola Alessandro , Palladino Mariangela , Illuminati Giulio , Consorti Fabrizio , Mariani Paola , Neri Fabrizio , Salis Paolo , Segatori Antonio , Tellini Laurent , Costabile Gianluca 
Published: April 2023

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