| Institution: | 1. Department of Cardiology, Amiens University Hospital, Amiens, France;2. UR UPJV 7517, Jules Verne University of Picardie, Amiens, France;3. Rouen University Hospital, Department of Cardiac and Cardio-Vascular Surgery, 76000 Rouen, France;4. Department of Cardiology, Elbeuf General Hospital, Saint-Aubin-lès-Elbeuf, France;5. Department of General Medicine, Jules Verne University of Picardie, Amiens, France;6. Department of Clinical Research, Amiens University Hospital, Amiens, France;7. Groupement des Hôpitaux de l’Institut Catholique de Lille Faculté Libre de Médecine, Université Lille Nord de France, Lille, France;1. Institut cardiovasculaire Paris Sud, hôpital Privé Jacques-Cartier, Ramsay Santé, 6 avenue du Noyer Lamber, 91300 Massy, France;2. GE Healthcare, 78530 Buc, France;1. Department of Cardiology, Amiens University Hospital, 80054 Amiens, France;2. EA 7517 MP3CV, Jules-Verne University of Picardie, 80054 Amiens, France;1. Department of Cardiology, Saint Antoine and Tenon Hospitals, AP–HP, Sorbonne Université, Paris, France;2. INSERM UMRS 1166, Institute of Cardiometabolism and Nutrition (ICAN), Sorbonne Université, Paris, France;3. Department of Cardiology, centre hospitalier universitaire Dijon Bourgogne, 21079 Dijon, France;4. Service de neurologie, hôpital Bichat-Claude-Bernard, AP–HP, 75018 Paris, France;1. Department of Cardiology, Rangueil University Hospital, Toulouse University School of Medicine, TSA 50032, 31059 Toulouse, France;2. Department of Cardiology, Rangueil University Hospital, Toulouse University School of Medicine, 31400 Toulouse, France;3. INSERM UMR 1295, Toulouse Paul Sabatier University, 31000 Toulouse, France;1. Department of Cardiology, University Hospital Álvaro Cunqueiro, Vigo, Spain;2. Health Research Institute Galicia Sur, Vigo, Spain;1. State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100037, China;2. School of Medicine, Shandong University, Jinan 250000, China |
| Abstract: | BackgroundTraditional 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).AimsTo investigate a new classification system for severe AS using phenomapping.MethodsConsecutive patients from a referral centre (training cohort) who met the echocardiographic definition of an aortic valve area (AVA) ≤ 1 cm2 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.ResultsIn total, 613 patients were initially recruited, with a mean ± standard deviation AVA of 0.72 ± 0.17 cm2. 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).ConclusionsArtificial 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. |
