Markers of Myocardial Damage Predict Mortality in Patients With Aortic Stenosis

被引:68
作者
Kwak, Soongu [1 ]
Everett, Russell J. [2 ]
Treibel, Thomas A. [3 ,4 ]
Yang, Seokhun [1 ]
Hwang, Doyeon [1 ]
Ko, Taehoon [5 ]
Williams, Michelle C. [2 ]
Bing, Rong [2 ]
Singh, Trisha [2 ]
Joshi, Shruti [2 ]
Lee, Heesun [1 ]
Lee, Whal [6 ]
Kim, Yong-Jin [1 ]
Chin, Calvin W. L. [7 ]
Fukui, Miho [8 ]
Al Musa, Tarique [9 ]
Rigolli, Marzia [10 ]
Singh, Anvesha
Tastet, Lionel
Dobson, Laura E. [9 ]
Wiesemann, Stephanie [12 ]
Ferreira, Vanessa M. [11 ]
Captur, Gabriella [13 ,14 ]
Lee, Sahmin [15 ]
Schulz-Menger, Jeanette
Schelbert, Erik B. [16 ]
Clavel, Marie-Annick
Park, Sung-Ji [17 ]
Rheude, Tobias [18 ,23 ]
Hadamitzky, Martin [19 ,24 ]
Gerber, Bernhard L. [20 ,21 ,25 ]
Newby, David E. [2 ]
Myerson, Saul G.
Pibarot, Phillipe
Cavalcante, Joao L.
McCann, Gerry P.
Greenwood, John P.
Moon, James C.
Dweck, Marc R. [2 ]
Lee, Seung-Pyo [1 ,22 ]
机构
[1] Seoul Natl Univ Hosp, Dept Internal Med, Seoul, South Korea
[2] Univ Edinburgh, British Heart Fdn Ctr Cardiovasc Sci, Edinburgh, Midlothian, Scotland
[3] Barts Hlth NHS Trust, London, England
[4] UCL, London, England
[5] Seoul Natl Univ Hosp, Off Hosp Informat, Seoul, South Korea
[6] Seoul Natl Univ Hosp, Dept Radiol, Seoul, South Korea
[7] Nat Heart Ctr Singapore, Singapore, Singapore
[8] Minneapolis Heart Inst Fdn, Cardiovasc Imaging Res Ctr, Minneapolis, MN USA
[9] Minneapolis Heart Inst Fdn, Core Lab, Minneapolis, MN USA
[10] Univ Leeds, Multidisciplinary Cardiovasc Res Ctr, Leeds Inst Cardiovasc & Metab Med, Leeds, W Yorkshire, England
[11] Univ Leeds, Div Biomed Imaging, Leeds Inst Cardiovasc & Metab Med, Leeds, W Yorkshire, England
[12] Univ Oxford, BHF Ctr Res Excellence Oxford, NIHR Biomed Res Ctr Oxford, Ctr Clin Magnet Resonance Res, Oxford, England
[13] Univ Leicester, Glenfield Hosp, Dept Cardiovasc Sci, Leicester, Leics, England
[14] NIHR Leicester Biomed Res Ctr, Leicester, Leics, England
[15] Univ Laval, Inst Univ Cardiol & Pneumol Quebec, Quebec Heart & Lung Inst, Quebec City, PQ, Canada
[16] Charite Campus Buch ECRC & Helios Clin Cardiol G, DZHK Partner Site, Berlin, Germany
[17] Royal Free Hosp, NHS Fdn Trust, Dept Cardiol, Inherited Heart Muscle Dis Clin, London, England
[18] Univ Ulsan, Asan Med Ctr, Div Cardiol, Inst Heart,Coll Med, Seoul, South Korea
[19] Univ Pittsburgh, Inst Heart & Vasc, UPMC Cardiovasc Magnet Resonance Ctr, Med Ctr, Pittsburgh, PA USA
[20] Sungkyunkwan Univ, Sch Med, Samsung Med Ctr, Div Cardiol,Dept Med,Cardiovasc Imaging Ctr, Seoul, South Korea
[21] German Heart Ctr Munich, Dept Cardiol, Munich, Germany
[22] German Heart Ctr Munich, Dept Radiol & Nucl Med, Munich, Germany
[23] Clin Univ St Luc, Dept Cardiovasc Dis, Div Cardiol, Brussels, Belgium
[24] Catholic Univ Louvain, Inst Rech Cardiovasc, Brussels, Belgium
[25] Seoul Natl Univ Hosp, Ctr Precis Med, Seoul, South Korea
基金
新加坡国家研究基金会;
关键词
aortic valve stenosis; magnetic resonance imaging; random survival forest; FIBROSIS; SOCIETY;
D O I
10.1016/j.jacc.2021.05.047
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
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/m(2)) and small (LVEDVi <= 55 mL/m(2)) 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 (C) 2021 Published by Elsevier on behalf of the American College of Cardiology Foundation. All rights reserved.
引用
收藏
页码:545 / 558
页数:14
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