دورية أكاديمية
Unsupervised Clustering of Patients with Severe Aortic Stenosis: A Myocardial Continuum.
| العنوان: | Unsupervised Clustering of Patients with Severe Aortic Stenosis: A Myocardial Continuum. |
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| المؤلفون: | Bohbot, Yohann, Raitière, Olivier, Guignant, Pierre, Ariza, Matthieu, Diouf, Momar, Rusinaru, Dan, Altes, Alexandre, Gun, Mesut, Di Lena, Chloé, Geneste, Laura, Thellier, Nicolas, Maréchaux, Sylvestre, Bauer, Fabrice, Tribouilloy, Christophe |
| المساهمون: | CHU Amiens-Picardie, Service de Cardiologie CHU Rouen, CHU Rouen, Normandie Université (NU)-Normandie Université (NU)-Université de Rouen Normandie (UNIROUEN), Normandie Université (NU), Sorbonne Université - Faculté de Médecine (SU FM), Sorbonne Université (SU), Institut Pierre Louis d'Epidémiologie et de Santé Publique (iPLESP), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU), Mécanismes physiopathologiques et conséquences des calcifications vasculaires - UR UPJV 7517 (MP3CV), Université de Picardie Jules Verne (UPJV)-CHU Amiens-Picardie, Université catholique de Lille (UCL), Hôpital Saint Philibert Lomme, Groupement des Hôpitaux de l'Institut Catholique de Lille (GHICL), Université catholique de Lille (UCL)-Université catholique de Lille (UCL), Service de Cardiologie Amiens, Université de Picardie Jules Verne (UPJV), Laboratoire d'Informatique et des Systèmes (LIS) (Marseille, Toulon) (LIS), Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), Université de Toulon - UFR Sciences et Techniques (UTLN UFR ScT), Université de Toulon (UTLN), CHU Henri Mondor Créteil |
| المصدر: | ISSN: 1875-2136. |
| بيانات النشر: | HAL CCSD Elsevier/French Society of Cardiology |
| سنة النشر: | 2022 |
| المجموعة: | Université de Toulon: HAL |
| مصطلحات موضوعية: | Aortic stenosis, Artificial intelligence, Clustering, Echocardiography, Mortality, Phenomapping, [SDV.MHEP]Life Sciences [q-bio]/Human health and pathology |
| الوصف: | International audience ; BACKGROUND: Traditional statistics, based on prediction models with a limited number of prespecified variables, are probably not adequate to provide an appropriate classification of a condition that is as heterogeneous as aortic stenosis (AS). AIMS: To investigate a new classification system for severe AS using phenomapping. METHODS: Consecutive patients from a referral centre (training cohort) who met the echocardiographic definition of an aortic valve area (AVA) ≤q~1~cm(2) were included. Clinical, laboratory and imaging continuous variables were entered into an agglomerative hierarchical clustering model to separate patients into phenogroups. Individuals from an external validation cohort were then assigned to these original clusters using the K nearest neighbour (KNN) function and their 5-year survival was compared after adjustment for aortic valve replacement (AVR) as a time-dependent covariable. RESULTS: In total, 613 patients were initially recruited, with a mean±standard deviation AVA of 0.72±0.17~cm(2). Twenty-six variables were entered into the model to generate a specific heatmap. Penalized model-based clustering identified four phenogroups (A, B, C and D), of which phenogroups B and D tended to include smaller, older women and larger, older men, respectively. The application of supervised algorithms to the validation cohort (n=1303) yielded the same clusters, showing incremental cardiac remodelling from phenogroup A to phenogroup D. According to this myocardial continuum, there was a stepwise increase in overall mortality (adjusted hazard ratio for phenogroup D vs A 2.18, 95% confidence interval 1.46-3.26; P<0.001). CONCLUSIONS: Artificial intelligence re-emphasizes the significance of cardiac remodelling in the prognosis of patients with severe AS and highlights AS not only as an isolated valvular condition, but also a global disease. |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| العلاقة: | info:eu-repo/semantics/altIdentifier/pmid/36241549; hal-03823326; https://u-picardie.hal.science/hal-03823326Test; PUBMED: 36241549 |
| DOI: | 10.1016/j.acvd.2022.06.007 |
| الإتاحة: | https://doi.org/10.1016/j.acvd.2022.06.007Test https://u-picardie.hal.science/hal-03823326Test |
| رقم الانضمام: | edsbas.ACCE5F31 |
| قاعدة البيانات: | BASE |
| DOI: | 10.1016/j.acvd.2022.06.007 |
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