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.

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

Raparelli, Valeria;Basili, Stefania;Giuseppe Santangelo;Amanpreet Kaur;Tiziana Bellini;Giovanni Zuliani;Angelina Passaro;Brombo Gloria;Andrea Cutini;Eleonora Capatti;Edoardo Dalla Nora;Francesca Di Vece;Andrea D’Amuri;Tommaso Romagnoli;Michele Polastri;Alessandra Violi;Valeria Fortunato;Alessandro Bella;Salvatore Greco;Riccardo Spaggiari;Gerarda Scaglione;Alfredo De Giorgi;Giulio Illuminati;
2023

Abstract

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.
2023
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; Claudio, Tiberti; Federica, Panimolle; Andrea, Isidori; Elisa, Giannetta; Mary Anna Venneri, ; Laura, Napoleone; Marta, Novo; Silvia, Quattrino; Simona, Ceccarelli; Eleni, Anastasiadou; Francesca, Megiorni; Cinzia, Marchese; Enrico, Mangieri; Gaetano, Tanzilli; Nicola, Viceconte; Francesco, Barillà; Carlo, Gaudio; Vincenzo, Paravati; Guglielmo, Tellan; Evaristo, Ettorre; Adriana, Servello; Fabio, Miraldi; Andrea, Moretti; Alessandra, Tanzilli; Piergiovanni, Mazzonna; Suleyman Al Kindy, ; Riccardo, Iorio; Martina Di Iorio, ; Gennaro, Petriello; Laura, Gioffrè; Eleonora, Indolfi; Gaetano, Pero; Nino, Cocco; Loredana, Iannetta; Sara, Giannuzzi; Emilio, Centaro; Sonia Cristina Sergi, ; Pasquale, Pignatelli; Daria, Amoroso; Simona, Bartimoccia; Salvatore, Minisola; Sergio, Morelli; Antonio, Fraioli; Silvia, Nocchi; Mario, Fontana; Filippo, Toriello; Eleonora, Ruscio; Tommaso, Todisco; Nicolò, Sperduti; Santangelo, Giuseppe Camillo; Giacomo, Visioli; Marco, Vano; Marco, Borgi; Ludovica Maria Antonini, ; Silvia, Robuffo; Claudia, Tucci; Agostino, Rossoni; Valeria, Spugnardi; Annarita, Vernile; Mariateresa, Santoliquido; Verdiana, Santori; Giulia, Tosti; Fabrizio, Recchia; Francesco, Morricone; Roberto, Scacciavillani; Alice, Lipari; Andrea, Zito; Floriana, Testa; Giulia, Ricci; Ilaria, Vellucci; Marianna, Vincenti; Silvia, Pietropaolo; Camilla, Scala; Nicolò, Rubini; Marta, Tomassi; Gloria, Rozzi; Floriana, Santomenna; Claudio, Cantelmi; Giacomo, Costanzo; Lucas, Rumbolà; Salvatore, Giarrizzo; Carlotta, Sapia; Biagio, Scotti; Giovanni, Talerico; Danilo, Toni; Anne, Falcou; Amanpreet, Kaur; Hassan, Behlouli; Anna Rita Vestri, ; Patrizia, Ferroni; Clara, Crescioli; Cristina, Antinozzi; Francesca Serena Pignataro, ; Bellini, Tiziana; Zuliani, Giovanni; Passaro, Angelina; Brombo, Gloria; 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; Alessandra Di Vincenzo Roberto Manfredini, ; DE GIORGI, Alfredo; Roberto, Carnevale; Carlo Catalano, Cristina Nocella.; Iacopo, Carbone; Nicola, Galea; Marianna, Suppa; Antonello, Rosa; Gioacchino, Galardo; Maria, Alessandroni; Alessandro, Coppola; Mariangela, Palladino; Illuminati, Giulio; Fabrizio, Consorti; Paola, Mariani; Fabrizio, Neri; Paolo, Salis; Antonio, Segatori; Laurent, Tellini; Gianluca, Costabile
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11392/2507671
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