Markers of Myocardial Damage Predict Mortality in Patients With Aortic Stenosis.
Aged
Aortic Valve Stenosis
/ complications
Cardiac Imaging Techniques
/ methods
Female
Fibrosis
/ diagnostic imaging
Heart Function Tests
/ methods
Heart Valve Prosthesis Implantation
/ methods
Humans
Machine Learning
Magnetic Resonance Imaging, Cine
/ methods
Male
Myocardium
/ pathology
Prognosis
Reproducibility of Results
Risk Assessment
/ methods
Severity of Illness Index
Survival Analysis
Ventricular Remodeling
aortic valve stenosis
magnetic resonance imaging
random survival forest
Journal
Journal of the American College of Cardiology
ISSN: 1558-3597
Titre abrégé: J Am Coll Cardiol
Pays: United States
ID NLM: 8301365
Informations de publication
Date de publication:
10 08 2021
10 08 2021
Historique:
received:
15
04
2021
revised:
07
05
2021
accepted:
10
05
2021
entrez:
6
8
2021
pubmed:
7
8
2021
medline:
16
12
2021
Statut:
ppublish
Résumé
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. Using machine learning, the study aimed to identify prognostically important CMR markers in AS and their thresholds of mortality. 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. 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 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.
Sections du résumé
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
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.
Identifiants
pubmed: 34353531
pii: S0735-1097(21)05318-3
doi: 10.1016/j.jacc.2021.05.047
pii:
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
545-558Subventions
Organisme : British Heart Foundation
ID : CH/09/002/26360
Pays : United Kingdom
Organisme : British Heart Foundation
ID : FS/14/78/31020
Pays : United Kingdom
Organisme : British Heart Foundation
ID : FS/19/35/34374
Pays : United Kingdom
Organisme : British Heart Foundation
ID : SP/20/2/34841
Pays : United Kingdom
Commentaires et corrections
Type : CommentIn
Informations de copyright
Crown Copyright © 2021. Published by Elsevier Inc. All rights reserved.
Déclaration de conflit d'intérêts
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.