J Am Coll Cardiol. 2021;78(6):545-558.
Markers of Myocardial Damage Predict Mortality in Patients With Aortic Stenosis.
제1저자 곽순구 · 교신저자 이승표, Marc R Dweck
소속 (21)
- Department of Internal Medicine, Seoul National University Hospital, Seoul, South Korea.
- British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, United Kingdom.
- Barts Health NHS Trust and University College London, London, United Kingdom.
- Office of Hospital Information, Seoul National University Hospital, Seoul, Korea.
- Department of Radiology, Seoul National University Hospital, Seoul, Korea.
- National Heart Center Singapore, Singapore.
- Cardiovascular Imaging Research Center and Core Lab, Minneapolis Heart Institute Foundation, Minneapolis, Minnesota, USA.
- Multidisciplinary Cardiovascular Research Centre & The Division of Biomedical Imaging, Leeds Institute for Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, United Kingdom.
- University of Oxford Centre for Clinical Magnetic Resonance Research, BHF Centre of Research Excellence (Oxford), NIHR Biomedical Research Centre (Oxford), Oxford, United Kingdom.
- Department of Cardiovascular Sciences, Glenfield Hospital, University of Leicester and NIHR Leicester Biomedical Research Centre, Leicester, United Kingdom.
- Institut Universitaire de Cardiologie et de Pneumologie de Québec/Québec Heart and Lung Institute, Université Laval, Québec City, Québec, Canada.
- Charité Campus Buch ECRC and Helios Clinics Cardiology Germany, DZHK partner site, Berlin, Germany.
- Inherited Heart Muscle Disease Clinic, Department of Cardiology, Royal Free Hospital, NHS Foundation Trust, London, United Kingdom.
- Division of Cardiology, Asan Medical Center Heart Institute, University of Ulsan College of Medicine, Seoul, South Korea.
- UPMC Cardiovascular Magnetic Resonance Center, Heart and Vascular Institute, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, USA.
- Division of Cardiology, Department of Medicine, Cardiovascular Imaging Center, Heart Vascular Stroke Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.
- Department of Cardiology, German Heart Center Munich, Munich, Germany.
- Department of Radiology and Nuclear Medicine, German Heart Center Munich, Munich, Germany.
- Division of Cardiology, Department of Cardiovascular Diseases, Cliniques Universitaires St. Luc and Institut de Recherche Cardiovasculaire, Université Catholique de Louvain, Brussels, Belgium.
- British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, United Kingdom. Electronic address: marc.dweck@ed.ac.uk.
- Department of Internal Medicine, Seoul National University Hospital, Seoul, South Korea; Center for Precision Medicine, Seoul National University Hospital, Seoul, South Korea. Electronic address: sproll1@snu.ac.kr.
Abstract
BACKGROUND Cardiovascular magnetic resonance (CMR) is increasingly used for risk stratification in aortic stenosis (AS). However, the relative prognostic power of CMR markers and their respective thresholds remains undefined.
OBJECTIVES Using machine learning, the study aimed to identify prognostically important CMR markers in AS and their thresholds of mortality.
METHODS Patients with severe AS undergoing AVR (n = 440, derivation; n = 359, validation cohort) were prospectively enrolled across 13 international sites (median 3.8 years' follow-up). CMR was performed shortly before surgical or transcatheter AVR. A random survival forest model was built using 29 variables (13 CMR) with post-AVR death as the outcome.
RESULTS There were 52 deaths in the derivation cohort and 51 deaths in the validation cohort. The 4 most predictive CMR markers were extracellular volume fraction, late gadolinium enhancement, indexed left ventricular end-diastolic volume (LVEDVi), and right ventricular ejection fraction. Across the whole cohort and in asymptomatic patients, risk-adjusted predicted mortality increased strongly once extracellular volume fraction exceeded 27%, while late gadolinium enhancement >2% showed persistent high risk. Increased mortality was also observed with both large (LVEDVi >80 mL/m2) and small (LVEDVi ≤55 mL/m2) ventricles, and with high (>80%) and low (≤50%) right ventricular ejection fraction. The predictability was improved when these 4 markers were added to clinical factors (3-year C-index: 0.778 vs 0.739). The prognostic thresholds and risk stratification by CMR variables were reproduced in the validation cohort.
CONCLUSIONS Machine learning identified myocardial fibrosis and biventricular remodeling markers as the top predictors of survival in AS and highlighted their nonlinear association with mortality. These markers may have potential in optimizing the decision of AVR.
Crown Copyright © 2021. Published by Elsevier Inc. All rights reserved.
Keywords
aortic valve stenosismagnetic resonance imagingrandom survival forest
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Conflict of interest statement
Funding Support and Author Disclosures The work was supported by a National Research Foundation of Korea grant funded by the Korea government (Ministry of Science and ICT; No. 2019R1A2C2084099) and a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number HI18C2383). The authors have reported that they have no relationships relevant to the contents of this paper to disclose.
그 밖의 정보
- 출판 형태
- Journal Article, Research Support, Non-U.S. Gov't
- 저널
- Journal of the American College of Cardiology · eISSN 1558-3597
- MeSH
- Aged · Aortic Valve Stenosis* / complications / diagnosis / mortality · Cardiac Imaging Techniques / methods · Female · Fibrosis / diagnostic imaging · Heart Function Tests / methods · Heart Valve Prosthesis Implantation* / methods / mortality · Humans · Machine Learning · Magnetic Resonance Imaging, Cine* / methods / statistics & numerical data · Male · Myocardium / pathology · Prognosis · Reproducibility of Results · Risk Assessment / methods · Severity of Illness Index · Survival Analysis · Ventricular Remodeling*
- 연구비
- British Heart Foundation FS/14/78/31020 · British Heart Foundation SP/20/2/34841 · Wellcome Trust G-MGU0427 · Wellcome Trust G-MRC0240 · British Heart Foundation FS/19/35/34374 · British Heart Foundation CH/09/002/26360