Man-machine interaction-based phenotyping identifies pathophysiologically and prognostically informative clusters among patients with mitral regurgitation undergoing transcatheter edge-to-edge repair

نویسندگان

چکیده

Abstract Background Depending on etiology, extent of disease progression, and comorbidities, patients with severe mitral regurgitation (MR) typically present considerable heterogeneity. Purpose This study therefore sought to improve diagnostic prognostic resolution in undergoing valve transcatheter edge-to-edge repair (MV TEER) for MR by developing a man-machine interaction-based phenotyping approach. Methods All 609 from this single-center registry underwent MV TEER between 2009 2020. Unsupervised agglomerative clustering was applied preprocedural echocardiography data, an artificial neural network (ANN) subsequently trained future patient-to-cluster assignment. Primary outcome measure postprocedural 5-year survival Results Cluster analysis revealed four pathophysiologically prognostically informative phenotypes: 1 constituted (n=188) presenting preserved left ventricular ejection fraction (LVEF; 56.5±7.79%) regular end-systolic diameter (LVESD; 35.2±7.52 mm). cluster 1, hereinafter serving as reference, 60.9% (95% CI: 53.3–69.7%). Patients 2 (n=102) also presented LVEF (55.7±7.82%) LVESD (34.9±7.68 mm), but showed the largest effective regurgitant orifice area (0.623±0.360 cm2) highest systolic pulmonary artery pressures (68.4±16.2 mmHg). Consequently, their ranged at 43.7% 33.2–57.6%; p-value: 0.032). 3 (n=270) were predominantly characterized impaired function (LVEF: 31.0±10.4%) dilated diameters (LVESD: 53.2±10.9 reduced 38.3% 31.9–46.1%; <0.001). Poorest (23.8% [95% 12.8–44.3%]; <0.001) observed 4 (n=49) biatrial dilatation (left atrial volume: 312±113 mL; right area: 46.0±8.83 although only slightly (51.5±11.0%). diagnosed fibrillation. An ANN could precisely predict assignment (accuracy: 85.2%), detecting high-risk clusters excellent specificity (95.0% 99.4%, respectively). Conclusion Assigning using multiparametric phenotypic approach can facilitate risk stratification clinical practice. Our unsupervised machine learning-based classification system differs previous approaches stratification, because we do neither hypothesize linear sequence accumulated pathologies caused (potentially ignoring aggravating impact comorbidities), nor stratify into low- cohorts accordance single variable's dichotomy (prone oversimplification). Funding Acknowledgement Type funding sources: None.

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ژورنال

عنوان ژورنال: European Heart Journal

سال: 2022

ISSN: ['2634-3916']

DOI: https://doi.org/10.1093/eurheartj/ehac544.1568