Background and Aims Risk stratification for mitral valve transcatheter edge-to-edge repair (M-TEER) is paramount in the decision-making process to appropriately select patients with severe secondary mitral regurgitation (SMR). This study sought to develop and validate an artificial intelligence-derived risk score (EuroSMR score) to predict 1-year outcomes (survival or survival + clinical improvement) in patients with SMR undergoing M-TEER.Methods An artificial intelligence-derived risk score was developed from the EuroSMR cohort (4172 and 428 patients treated with M-TEER in the derivation and validation cohorts, respectively). The EuroSMR score was validated and compared with established risk models.Results The EuroSMR risk score, which is based on 18 clinical, echocardiographic, laboratory, and medication parameters, allowed for an improved discrimination of surviving and non-surviving patients (hazard ratio 4.3, 95% confidence interval 3.7-5.0; P < .001), and outperformed established risk scores in the validation cohort. Prediction for 1-year mortality (area under the curve: 0.789, 95% confidence interval 0.737-0.842) ranged from <5% to >70%, including the identification of an extreme-risk population (2.6% of the entire cohort), which had a very high probability for not surviving beyond 1 year (hazard ratio 6.5, 95% confidence interval 3.0-14; P < .001). The top 5% of patients with the highest EuroSMR risk scores showed event rates of 72.7% for mortality and 83.2% for mortality or lack of clinical improvement at 1-year follow-up.Conclusions The EuroSMR risk score may allow for improved prognostication in heart failure patients with severe SMR, who are considered for a M-TEER procedure. The score is expected to facilitate the shared decision-making process with heart team members and patients.

Artificial intelligence-derived risk score for mortality in secondary mitral regurgitation treated by transcatheter edge-to-edge repair: the EuroSMR risk score / Hausleiter, Jörg; Lachmann, Mark; Stolz, Lukas; Bedogni, Francesco; Rubbio, Antonio P; Estévez-Loureiro, Rodrigo; Raposeiras-Roubin, Sergio; Boekstegers, Peter; Karam, Nicole; Rudolph, Volker; Stocker, Thomas; Orban, Mathias; Braun, Daniel; Näbauer, Michael; Massberg, Steffen; Popescu, Aniela; Ruf, Tobias; von Bardeleben, Ralph Stephan; Iliadis, Christos; Pfister, Roman; Baldus, Stephan; Besler, Christian; Kister, Tobias; Kresoja, Karl; Lurz, Philipp; Thiele, Holger; Koell, Benedikt; Schofer, Niklas; Kalbacher, Daniel; Neuss, Michael; Butter, Christian; Laugwitz, Karl-Ludwig; Trenkwalder, Teresa; Xhepa, Eroion; Joner, Michael; Omran, Hazem; Fortmeier, Vera; Gerçek, Muhammed; Beucher, Harald; Schmitz, Thomas; Bufe, Alexander; Rothe, Jürgen; Seyfarth, Melchior; Schmidt, Tobias; Frerker, Christian; Rottländer, Dennis; Horn, Patrick; Spieker, Maximilian; Zweck, Elric; Kassar, Mohammad; Praz, Fabien; Windecker, Stephan; Puscas, Tania; Adamo, Marianna; Lupi, Laura; Metra, Marco; Villa, Emmanuel; Zoccai, Giuseppe Biondi; Tamburino, Corrado; Grasso, Carmelo; Catriota, Fausto; Testa, Luca; Tusa, Maurizio; Godino, Cosmo; Galasso, Michele; Montorfano, Matteo; Agricola, Eustachio; Denti, Paolo; De Marco, Federico; Tarantini, Giuseppe; Masiero, Giulia; Crimi, Gabriele; Munafò, Andrea Raffaele; Giannini, Christina; Petronio, Anna; Pidello, Stefano; Boretto, Paolo; Montefusco, Antonio; Frea, Simone; Angelini, Filippo; Bocchino, Pier Paolo; De Felice, Francesco; Citro, Rodolfo; Caneiro-Queija, Berenice; Freixa, Xavier; Regueiro, Ander; Sanchís, Laura; Sabaté, Manel; Arzamendi, Dabit; Asmarats, Lluís; Peregrina, Estefanía Fernández; Benito-González, Tomas; Fernández-Vázquez, Felipe; Pascual, Isaac; Avanzas, Pablo; Nombela-Franco, Luis; Tirado-Conte, Gabriela; Pozo, Eduardo; Portolés-Hernández, Antonio; Palomero, Vanessa Moñivas; Sampaio, Francisco; Melica, Bruno; Rodes-Cabau, Josep; Paradis, Jean-Michel; Alperi, Alberto; Shuvy, Mony; Haberman, Dan; Null, Null. - In: EUROPEAN HEART JOURNAL. - ISSN 0195-668X. - 45:11(2024), pp. 922-936. [10.1093/eurheartj/ehad871]

Artificial intelligence-derived risk score for mortality in secondary mitral regurgitation treated by transcatheter edge-to-edge repair: the EuroSMR risk score

Montorfano, Matteo;Agricola, Eustachio;
2024-01-01

Abstract

Background and Aims Risk stratification for mitral valve transcatheter edge-to-edge repair (M-TEER) is paramount in the decision-making process to appropriately select patients with severe secondary mitral regurgitation (SMR). This study sought to develop and validate an artificial intelligence-derived risk score (EuroSMR score) to predict 1-year outcomes (survival or survival + clinical improvement) in patients with SMR undergoing M-TEER.Methods An artificial intelligence-derived risk score was developed from the EuroSMR cohort (4172 and 428 patients treated with M-TEER in the derivation and validation cohorts, respectively). The EuroSMR score was validated and compared with established risk models.Results The EuroSMR risk score, which is based on 18 clinical, echocardiographic, laboratory, and medication parameters, allowed for an improved discrimination of surviving and non-surviving patients (hazard ratio 4.3, 95% confidence interval 3.7-5.0; P < .001), and outperformed established risk scores in the validation cohort. Prediction for 1-year mortality (area under the curve: 0.789, 95% confidence interval 0.737-0.842) ranged from <5% to >70%, including the identification of an extreme-risk population (2.6% of the entire cohort), which had a very high probability for not surviving beyond 1 year (hazard ratio 6.5, 95% confidence interval 3.0-14; P < .001). The top 5% of patients with the highest EuroSMR risk scores showed event rates of 72.7% for mortality and 83.2% for mortality or lack of clinical improvement at 1-year follow-up.Conclusions The EuroSMR risk score may allow for improved prognostication in heart failure patients with severe SMR, who are considered for a M-TEER procedure. The score is expected to facilitate the shared decision-making process with heart team members and patients.
2024
Edge-to-edge-repair
Heart failure
Machine learning
Mortality prediction
Secondary mitral regurgitation
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11768/159656
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