TY - JOUR
T1 - Artificial intelligence-derived risk score for mortality in secondary mitral regurgitation treated by transcatheter edge-to-edge repair
T2 - The EuroSMR risk score
AU - on behalf of the EuroSMR Investigators
AU - Hausleiter, Jörg
AU - Lachmann, Mark
AU - Stolz, Lukas
AU - Bedogni, Francesco
AU - Rubbio, Antonio P.
AU - Estèvez-Loureiro, Rodrigo
AU - Raposeiras-Roubin, Sergio
AU - Boekstegers, Peter
AU - Karam, Nicole
AU - Rudolph, Volker
AU - Stocker, Thomas
AU - Orban, Mathias
AU - Braun, Daniel
AU - Näbauer, Michael
AU - Massberg, Steffen
AU - Popescu, Aniela
AU - Ruf, Tobias
AU - Von Bardeleben, Ralph Stephan
AU - Iliadis, Christos
AU - Pfister, Roman
AU - Baldus, Stephan
AU - Besler, Christian
AU - Kister, Tobias
AU - Kresoja, Karl
AU - Lurz, Philipp
AU - Thiele, Holger
AU - Koell, Benedikt
AU - Schofer, Niklas
AU - Kalbacher, Daniel
AU - Neuss, Michael
AU - Butter, Christian
AU - Laugwitz, Karl Ludwig
AU - Trenkwalder, Teresa
AU - Xhepa, Eroion
AU - Joner, Michael
AU - Omran, Hazem
AU - Fortmeier, Vera
AU - Gerçek, Muhammed
AU - Beucher, Harald
AU - Schmitz, Thomas
AU - Bufe, Alexander
AU - Rothe, Jürgen
AU - Seyfarth, Melchior
AU - Schmidt, Tobias
AU - Frerker, Christian
AU - Rottländer, Dennis
AU - Horn, Patrick
AU - Spieker, Maximilian
AU - Zweck, Elric
AU - Kassar, Mohammad
N1 - Publisher Copyright:
© 2024 The Author(s). Published by Oxford University Press on behalf of the European Society of Cardiology. All rights reserved.
PY - 2024/3/14
Y1 - 2024/3/14
N2 - 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.
AB - 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.
KW - Edge-to-edge-repair
KW - Heart failure
KW - Machine learning
KW - Mortality prediction
KW - Secondary mitral regurgitation
UR - http://www.scopus.com/inward/record.url?scp=85187958582&partnerID=8YFLogxK
U2 - 10.1093/eurheartj/ehad871
DO - 10.1093/eurheartj/ehad871
M3 - Article
C2 - 38243773
AN - SCOPUS:85187958582
SN - 0195-668X
VL - 45
SP - 922
EP - 936
JO - European Heart Journal
JF - European Heart Journal
IS - 11
ER -