| Title: |
Machine-learning phenotyping of patients with functional mitral regurgitation undergoing transcatheter edge-to-edge repair: the MITRA-AI study |
| Authors: |
D'Ascenzo, Fabrizio; Angelini, Filippo; Pancotti, Corrado; Bocchino, Pier Paolo; Giannini, Cristina; Finizio, Filippo; Adamo, Marianna; Camman, Victoria; Morici, Nuccia; Perl, Leor; Muscoli, Saverio; Crimi, Gabriele; Boretto, Paolo; de Filippo, Ovidio; Baldetti, Luca; Biondi-Zoccai, Giuseppe; Conrotto, Federico; Petronio, Sonia; Giordano, Arturo; Estévez-Loureiro, Rodrigo; Stolfo, Davide; Templin, Christian; Chiarito, Mauro; Cavallone, Elena; Dusi, Veronica; Alunni, Gianluca; Oreglia, Jacopo; Iannaccone, Mario; Pocar, Marco; Pagnesi, Matteo; Pidello, Stefano; Kornowski, Ran; Fariselli, Piero; Frea, Simone; La Torre, Michele; Raineri, Claudia; Patti, Giuseppe; Porto, Italo; Montefusco, Antonio; Raposeiras Roubin, Sergio; De Ferrari, Gaetano Maria |
| Contributors: |
F. D'Ascenzo; F. Angelini; C. Pancotti; P.P. Bocchino; C. Giannini; F. Finizio; M. Adamo; V. Camman; N. Morici; L. Perl; S. Muscoli; G. Crimi; P. Boretto; O. De Filippo; L. Baldetti; G. Biondi-Zoccai; F. Conrotto; S. Petronio; A. Giordano; R. Estévez-Loureiro; D. Stolfo; C. Templin; M. Chiarito; E. Cavallone; V. Dusi; G. Alunni; J. Oreglia; M. Iannaccone; M. Pocar; M. Pagnesi; S. Pidello; R. Kornowski; P. Fariselli; S. Frea; M. La Torre; C. Raineri; G. Patti; I. Porto; A. Montefusco; S. Raposeiras Roubin; G.M. De Ferrari |
| Publisher Information: |
Oxford University Press |
| Publication Year: |
2025 |
| Collection: |
The University of Milan: Archivio Istituzionale della Ricerca (AIR) |
| Subject Terms: |
Artificial intelligence; Machine-learning; MitraClip; Mitral regurgitation; Transcatheter mitral valve repair; Settore MEDS-13/C - Chirurgia cardiaca; Settore MEDS-07/B - Malattie dell'apparato cardiovascolare |
| Description: |
Aims Severe functional mitral regurgitation (FMR) may benefit from mitral transcatheter edge-to-edge repair (TEER), but selection of patients remains to be optimized. Objectives The aim of this study was to use machine-learning (ML) approaches to uncover concealed connections between clinical, echocardiographic, and haemodynamic data associated with patients’ outcomes. Methods Consecutive patients undergoing TEER from 2009 to 2020 were included in the MITRA-AI registry. The primary endpoint and results was a composite of cardiovascular death or heart failure (HF) hospitalization at 1 year. External validation was performed on the Mitrascore cohort. 822 patients were included. The composite primary endpoint occurred in 250 (30%) patients. Four clusters with decreasing risk of the primary endpoint were identified (42, 37, 25, and 20% from Cluster 1 to Cluster 4, respectively). Clusters were combined into a high-risk (Clusters 1 and 2) and a low-risk phenotype (Clusters 3 and 4). High-risk phenotype patients had larger left ventriculars (LVs) (>107 mL/m2), lower left ventricular ejection fraction ( |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| Relation: |
info:eu-repo/semantics/altIdentifier/pmid/40395430; info:eu-repo/semantics/altIdentifier/wos/WOS:001439238300001; volume:6; issue:3; firstpage:340; lastpage:349; numberofpages:10; journal:EUROPEAN HEART JOURNAL. DIGITAL HEALTH; https://hdl.handle.net/2434/1169675 |
| DOI: |
10.1093/ehjdh/ztaf006 |
| Availability: |
https://hdl.handle.net/2434/1169675; https://doi.org/10.1093/ehjdh/ztaf006 |
| Rights: |
info:eu-repo/semantics/openAccess ; license:Creative commons ; license uri:http://creativecommons.org/licenses/by/4.0/ |
| Accession Number: |
edsbas.9B82C52 |
| Database: |
BASE |