Subphenotyping of Patients With Aortic Stenosis by Unsupervised Agglomerative Clustering of Echocardiographic and Hemodynamic Data |
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Institution: | 1. First Department of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany;2. Department of Family Medicine, McGill University, Montreal, Quebec, Canada;3. Department of Cardiology, Deutsches Herzzentrum München, Technical University of Munich, Munich, Germany;4. DZHK (German Centre for Cardiovascular Research), partner site Munich Heart Alliance, Munich, Germany;5. Department of Physics, University of Johannesburg, Auckland Park, South Africa;6. Department of Cardiology, Keio University School of Medicine, Tokyo, Japan |
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Abstract: | ObjectivesThe aim of this retrospective analysis was to categorize patients with severe aortic stenosis (AS) according to clinical presentation by applying unsupervised machine learning.BackgroundPatients with severe AS present with heterogeneous clinical phenotypes, depending on disease progression and comorbidities.MethodsUnsupervised agglomerative clustering was applied to preprocedural data from echocardiography and right heart catheterization from 366 consecutively enrolled patients undergoing transcatheter aortic valve replacement for severe AS.ResultsCluster analysis revealed 4 distinct phenotypes. Patients in cluster 1 (n = 164 44.8%]), serving as a reference, presented with regular cardiac function and without pulmonary hypertension (PH). Accordingly, estimated 2-year survival was 90.6% (95% CI: 85.8%-95.6%). Clusters 2 (n = 66 18.0%]) and 4 (n = 91 24.9%]) both comprised patients with postcapillary PH. Yet patients in cluster 2 with preserved left and right ventricular structure and function showed a similar survival as those in cluster 1 (2-year survival 85.8%; 95% CI: 76.9%-95.6%), whereas patients in cluster 4 with dilatation of all heart chambers and a high prevalence of mitral and tricuspid regurgitation (12.5% and 14.8%, respectively) died more often (2-year survival 74.9% 95% CI: 65.9%-85.2%]; HR for 2-year mortality: 2.8 95% CI: 1.4-5.5]). Patients in cluster 3, the smallest (n = 45 12.3%]), displayed the most extensive disease characteristics (ie, left and right heart dysfunction together with combined pre- and postcapillary PH), and 2-year survival was accordingly reduced (77.3% 95% CI: 65.2%-91.6%]; HR for 2-year mortality: 2.6 95% CI: 1.1-6.2]).ConclusionsUnsupervised machine learning aids in capturing complex clinical presentations as observed in patients with severe AS. Importantly, structural alterations in left and right heart morphology, possibly due to genetic predisposition, constitute an equally sensitive indicator of poor prognosis compared with high-grade PH. |
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Keywords: | artificial neural network machine learning severe aortic stenosis transcatheter aortic valve replacement unsupervised agglomerative clustering ANN"} {"#name":"keyword" "$":{"id":"kwrd0040"} "$$":[{"#name":"text" "_":"artificial neural network AS"} {"#name":"keyword" "$":{"id":"kwrd0050"} "$$":[{"#name":"text" "_":"aortic stenosis AVG"} {"#name":"keyword" "$":{"id":"kwrd0060"} "$$":[{"#name":"text" "_":"aortic valve gradient EuroSCORE"} {"#name":"keyword" "$":{"id":"kwrd0080"} "$$":[{"#name":"text" "_":"European System for Cardiac Operative Risk Evaluation LVEF"} {"#name":"keyword" "$":{"id":"kwrd0090"} "$$":[{"#name":"text" "_":"left ventricular ejection fraction mPAP"} {"#name":"keyword" "$":{"id":"kwrd0100"} "$$":[{"#name":"text" "_":"mean pulmonary artery pressure PH"} {"#name":"keyword" "$":{"id":"kwrd0110"} "$$":[{"#name":"text" "_":"pulmonary hypertension PVR"} {"#name":"keyword" "$":{"id":"kwrd0120"} "$$":[{"#name":"text" "_":"pulmonary vascular resistance RHC"} {"#name":"keyword" "$":{"id":"kwrd0130"} "$$":[{"#name":"text" "_":"right heart catheterization TAPSE"} {"#name":"keyword" "$":{"id":"kwrd0140"} "$$":[{"#name":"text" "_":"tricuspid annular plane systolic excursion TAVR"} {"#name":"keyword" "$":{"id":"kwrd0150"} "$$":[{"#name":"text" "_":"transcatheter aortic valve replacement |
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