TY - JOUR
T1 - Machine learning identifies pathophysiologically and prognostically informative phenotypes among patients with mitral regurgitation undergoing transcatheter edge-to-edge repair
AU - Trenkwalder, Teresa
AU - Lachmann, Mark
AU - Stolz, Lukas
AU - Fortmeier, Vera
AU - Covarrubias, Héctor Alfonso Alvarez
AU - Rippen, Elena
AU - Schürmann, Friederike
AU - Presch, Antonia
AU - von Scheidt, Moritz
AU - Ruff, Celine
AU - Hesse, Amelie
AU - Gerçek, Muhammed
AU - Mayr, N. Patrick
AU - Ott, Ilka
AU - Schuster, Tibor
AU - Harmsen, Gerhard
AU - Yuasa, Shinsuke
AU - Kufner, Sebastian
AU - Hoppmann, Petra
AU - Kupatt, Christian
AU - Schunkert, Heribert
AU - Kastrati, Adnan
AU - Laugwitz, Karl Ludwig
AU - Rudolph, Volker
AU - Joner, Michael
AU - Hausleiter, Jörg
AU - Xhepa, Erion
N1 - Publisher Copyright:
© The Author(s) 2023. Published by Oxford University Press on behalf of the European Society of Cardiology. All rights reserved.
PY - 2023/5/1
Y1 - 2023/5/1
N2 - Aims Patients with mitral regurgitation (MR) present with considerable heterogeneity in cardiac damage depending on underlying aetiology, disease progression, and comorbidities. This study aims to capture their cardiopulmonary complexity by employing a machine-learning (ML)-based phenotyping approach. Methods Data were obtained from 1426 patients undergoing mitral valve transcatheter edge-to-edge repair (MV TEER) for MR. The and results ML model was developed using 609 patients (derivation cohort) and validated on 817 patients from two external institutions. Phenotyping was based on echocardiographic data, and ML-derived phenotypes were correlated with 5-year outcomes. Unsupervised agglomerative clustering revealed four phenotypes among the derivation cohort: Cluster 1 showed preserved left ventricular ejection fraction (LVEF; 56.5 ± 7.79%) and regular left ventricular end-systolic diameter (LVESD; 35.2 ± 7.52 mm); 5-year survival in Cluster 1, hereinafter serving as a reference, was 60.9%. Cluster 2 presented with preserved LVEF (55.7 ± 7.82%) but showed the largest mitral valve effective regurgitant orifice area (0.623 ± 0.360 cm2) and highest systolic pulmonary artery pressures (68.4 ± 16.2 mmHg); 5-year survival ranged at 43.7% (P-value: 0.032). Cluster 3 was characterized by impaired LVEF (31.0 ± 10.4%) and enlarged LVESD (53.2 ± 10.9 mm); 5-year survival was reduced to 38.3% (P-value: <0.001). The poorest 5-year survival (23.8%; P-value: <0.001) was observed in Cluster 4 with biatrial dilatation (left atrial volume: 312 ± 113 mL; right atrial area: 46.0 ± 8.83 cm2) although LVEF was only slightly reduced (51.5 ± 11.0%). Importantly, the prognostic significance of ML-derived phenotypes was externally confirmed. Conclusion ML-enabled phenotyping captures the complexity of extra-mitral valve cardiac damage, which does not necessarily occur in a sequential fashion. This novel phenotyping approach can refine risk stratification in patients undergoing MV TEER in the future.
AB - Aims Patients with mitral regurgitation (MR) present with considerable heterogeneity in cardiac damage depending on underlying aetiology, disease progression, and comorbidities. This study aims to capture their cardiopulmonary complexity by employing a machine-learning (ML)-based phenotyping approach. Methods Data were obtained from 1426 patients undergoing mitral valve transcatheter edge-to-edge repair (MV TEER) for MR. The and results ML model was developed using 609 patients (derivation cohort) and validated on 817 patients from two external institutions. Phenotyping was based on echocardiographic data, and ML-derived phenotypes were correlated with 5-year outcomes. Unsupervised agglomerative clustering revealed four phenotypes among the derivation cohort: Cluster 1 showed preserved left ventricular ejection fraction (LVEF; 56.5 ± 7.79%) and regular left ventricular end-systolic diameter (LVESD; 35.2 ± 7.52 mm); 5-year survival in Cluster 1, hereinafter serving as a reference, was 60.9%. Cluster 2 presented with preserved LVEF (55.7 ± 7.82%) but showed the largest mitral valve effective regurgitant orifice area (0.623 ± 0.360 cm2) and highest systolic pulmonary artery pressures (68.4 ± 16.2 mmHg); 5-year survival ranged at 43.7% (P-value: 0.032). Cluster 3 was characterized by impaired LVEF (31.0 ± 10.4%) and enlarged LVESD (53.2 ± 10.9 mm); 5-year survival was reduced to 38.3% (P-value: <0.001). The poorest 5-year survival (23.8%; P-value: <0.001) was observed in Cluster 4 with biatrial dilatation (left atrial volume: 312 ± 113 mL; right atrial area: 46.0 ± 8.83 cm2) although LVEF was only slightly reduced (51.5 ± 11.0%). Importantly, the prognostic significance of ML-derived phenotypes was externally confirmed. Conclusion ML-enabled phenotyping captures the complexity of extra-mitral valve cardiac damage, which does not necessarily occur in a sequential fashion. This novel phenotyping approach can refine risk stratification in patients undergoing MV TEER in the future.
KW - artificial neural network
KW - cardiac damage
KW - machine learning
KW - mitral regurgitation
KW - transcatheter edge-to-edge repair
KW - unsupervised agglomerative clustering
UR - http://www.scopus.com/inward/record.url?scp=85154529299&partnerID=8YFLogxK
U2 - 10.1093/ehjci/jead013
DO - 10.1093/ehjci/jead013
M3 - Article
AN - SCOPUS:85154529299
SN - 2047-2404
VL - 24
SP - 574
EP - 587
JO - European Heart Journal Cardiovascular Imaging
JF - European Heart Journal Cardiovascular Imaging
IS - 5
ER -