无节律的米特拉突起表型:使用多中心心脏MRI注册表进行无监督机器学习分析
Ralph Kwame Akyea1, Stefano Figliozzi1, Pedro M Lopes1
1From the Primary Care Stratified Medicine Research Group, Centre for Academic Primary Care, Lifespan and Population Health Unit, School of Medicine, University of Nottingham, Nottingham, England (R.K.A.); IRCCS Humanitas Research Hospital, Rozzano, Italy (S.F., L.M., M.F.); School of Biomedical Engineering and Imaging Sciences-Faculty of Life Sciences and Medicine, King's College London, Westminster Bridge Rd, London SE1 7EH, England (S.F., V.S., A.C., G.G., P.G.M.); Department of Cardiology, Hospital de Santa Cruz, Centro Hospitalar de Lisboa Ocidental, Carnaxide, Lisbon, Portugal (P.M.L., A.M.F., J.A.); Department of Cardiology, University Hospital Muenster, Muenster, Germany (K.B.B., A.Y., A.R.F.); Department of Cardiology, Hartcentrum, Jessa Hospital, Hasselt, Belgium (S.M.F.); Faculty of Medicine and Life Sciences, Hasselt University, Hasselt, Belgium (S.M.F.); Multimodality Cardiac Imaging Section, IRCSS Policlinico San Donato, San Donato Milanese, Italy (L.T., M.L.); Department of Radiology, Ca' Granda Ospedale Maggiore Policlinico, University of Milan, Milan, Italy (L.T.); Department of Perioperative Cardiology and Cardiovascular Imaging, Centro Cardiologico Monzino IRCCS, Milan, Italy (S.M., G.P.); GVM Care & Research, Maria Cecilia Hospital, Cotignola, Italy (S.C., A.S.); Center for Cardiac MR, Lausanne University Hospital, CHUV, Lausanne, Switzerland (A.G.P., P.M., J.S.); Cardiologia-4, Dipartimento Cardio-toraco-vascolare A. De Gasperis, ASST Grande Ospedale Metropolitano Niguarda, Milan, Italy (I.B., G.Q., P.P.); Department of Cardiology, Hospital Universitario Vall d'Hebron, Institut de Recerca (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain (L.G.G., J.F.R.P.); Centro de Investigación Biomédica en Red, CIBERCV, Madrid, Spain (L.G.G., J.F.R.P.); Department of Cardiology, Division of Heart and Lungs, University Medical Center Utrecht, Utrecht, the Netherlands (A.J.T., T.L.); Fondazione CNR/Regione Toscana G. Monasterio, Pisa, Italy (F.B., C.D.A.); Department of Clinical, Internal, Anesthesiology and Cardiovascular Sciences, Sapienza University of Rome, Rome, Italy (D.F., V.M., L.A.); Department of Cardiology, Istituto Auxologico Italiano, IRCCS, Milan, Italy (C.T., D.M., L.P.B.); Department of Medicine and Surgery, University of Milano-Bicocca, Milan, Italy (D.M., L.P.B.); Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland (P.M., J.S.); Gasthuisberg University Hospital, Leuven, Belgium (B.V., J.B.); Department of Biomedical, Surgical and Dental Sciences (G.P.) and Department of Clinical Sciences and Community Health, Cardiovascular Section (D.A.), University of Milan, Milan, Italy; and Department of Clinical Therapeutics, National and Kapodistrian University of Athens, Athens, Greece (G.G.).
无监督机器学习识别了两组患有 mitra valve prolapse (MVP) 的患者群. 一组,以心脏MRI发现为特征,面临危险心律失常和突然心脏死亡的风险明显更高.
科学领域:
- 心脏病学 心脏病学
- 医疗成像医学成像
- 机器学习 机器学习
背景情况:
- 密特拉脱落 (MVP) 可能与严重的心律失常事件有关.
- 在MVP患者中预测心律不整的风险,而没有显著的额头肌反或左心室功能障碍仍然具有挑战性.
研究的目的:
- 应用无监督机器学习来识别MVP患者中不同的表型集群.
- 评估这些集群之间的关联和不良心律失常结果的风险.
主要方法:
- 对474名MVP患者进行了以晚期加多增强 (LGE) 进行心脏MRI的回顾性分析.
- 用于无监督集群的等级k-平均算法.
- 使用考克斯比例危险模型评估了持续的腹腔动脉短心,突然心脏死亡或昏迷的复合终点.
主要成果:
- 确定了两个表型集群. 与集群1相比,集群2 (42%) 在LGE-心脏MRI上表现出更严重的 mitra 变性,心室重塑和心肌纤维化,而集群1则表现出更严重的心肌纤维化.
- 人口和临床数据对集群差异的影响最小.
- 与集群1相比,集群2患者的研究终点风险增加了3.79倍,根据LGE程度进行调整.
结论:
- 无监督机器学习有效地根据心脏MRI特征在MVP患者中识别了两个表型集群.
- 这些群体表明心律失常事件的风险有显著差异.
- 使用心脏MRI进行深入的基于成像的表型识别对于MVP中的心律失常风险分层有价值.
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相关概念视频
Mitral Valve Prolapse I: Introduction
Mitral Valve Prolapse II: Assessment and Management
Mitral Valve Prolapse III: Nursing Management
Mitral Regurgitation I: Introduction
Mitral Regurgitation II: Clinical Features and Diagnostic Tests
Mitral Stenosis II: Clinical features and Diagnostic Tests
