Feature-Tracking Global Longitudinal Strain Predicts Mortality in Patients With Preserved Ejection Fraction: A Multicenter Study.
cardiac magnetic resonance imaging
feature-tracking
global longitudinal strain
left ventricular function
mortality
prognosis
Journal
JACC. Cardiovascular imaging
ISSN: 1876-7591
Titre abrégé: JACC Cardiovasc Imaging
Pays: United States
ID NLM: 101467978
Informations de publication
Date de publication:
04 2020
04 2020
Historique:
received:
16
09
2019
accepted:
03
10
2019
pubmed:
16
11
2019
medline:
6
1
2021
entrez:
16
11
2019
Statut:
ppublish
Résumé
The goal of this study was to evaluate the prognostic value of global longitudinal strain (GLS) derived from cardiac magnetic resonance (CMR) feature-tracking in a large multicenter population of patients with preserved ejection fraction. Ejection fraction is the principal parameter used clinically to assess cardiac mechanics and provides prognostic information. However, significant abnormalities of myocardial deformation can be present despite preserved ejection fraction. CMR feature-tracking techniques now allow assessment of strain from routine cine images, without specialized pulse sequences. Whether abnormalities of strain measured by using CMR feature-tracking have prognostic value in patients with preserved ejection fraction is unknown. Consecutive patients with preserved ejection fraction (≥50%) and a clinical indication for CMR at 4 U.S. medical centers were included in this retrospective study. Feature-tracking GLS was calculated from 3 long-axis cine views. The primary endpoint was all-cause death. Cox proportional hazards regression modeling was used to examine the independent association between GLS and death. The incremental prognostic value of GLS was assessed in nested models. Of the 1,274 patients in this study, 115 died during a median follow-up of 6.2 years. By Kaplan-Meier analysis, patients with GLS ≥ median (-20%) had significantly reduced event-free survival compared with those with GLS < median (log-rank test, p < 0.001). By Cox multivariable regression modeling, each 1% worsening in GLS was associated with a 22.8% increased risk of death after adjustment for clinical and imaging risk factors (hazard ratio: 1.228 per percent; p < 0.001). Addition of GLS in this model resulted in significant improvement in the global chi-square test (94 to 183; p < 0.001) and Harrell's C-statistic (0.75 to 0.83; p < 0.001). GLS derived from CMR feature-tracking is a powerful independent predictor of mortality in patients with preserved ejection fraction, incremental to common clinical and imaging risk factors.
Sections du résumé
OBJECTIVES
The goal of this study was to evaluate the prognostic value of global longitudinal strain (GLS) derived from cardiac magnetic resonance (CMR) feature-tracking in a large multicenter population of patients with preserved ejection fraction.
BACKGROUND
Ejection fraction is the principal parameter used clinically to assess cardiac mechanics and provides prognostic information. However, significant abnormalities of myocardial deformation can be present despite preserved ejection fraction. CMR feature-tracking techniques now allow assessment of strain from routine cine images, without specialized pulse sequences. Whether abnormalities of strain measured by using CMR feature-tracking have prognostic value in patients with preserved ejection fraction is unknown.
METHODS
Consecutive patients with preserved ejection fraction (≥50%) and a clinical indication for CMR at 4 U.S. medical centers were included in this retrospective study. Feature-tracking GLS was calculated from 3 long-axis cine views. The primary endpoint was all-cause death. Cox proportional hazards regression modeling was used to examine the independent association between GLS and death. The incremental prognostic value of GLS was assessed in nested models.
RESULTS
Of the 1,274 patients in this study, 115 died during a median follow-up of 6.2 years. By Kaplan-Meier analysis, patients with GLS ≥ median (-20%) had significantly reduced event-free survival compared with those with GLS < median (log-rank test, p < 0.001). By Cox multivariable regression modeling, each 1% worsening in GLS was associated with a 22.8% increased risk of death after adjustment for clinical and imaging risk factors (hazard ratio: 1.228 per percent; p < 0.001). Addition of GLS in this model resulted in significant improvement in the global chi-square test (94 to 183; p < 0.001) and Harrell's C-statistic (0.75 to 0.83; p < 0.001).
CONCLUSIONS
GLS derived from CMR feature-tracking is a powerful independent predictor of mortality in patients with preserved ejection fraction, incremental to common clinical and imaging risk factors.
Identifiants
pubmed: 31727563
pii: S1936-878X(19)30950-7
doi: 10.1016/j.jcmg.2019.10.004
pmc: PMC7150621
mid: NIHMS1543111
pii:
doi:
Types de publication
Journal Article
Multicenter Study
Observational Study
Langues
eng
Sous-ensembles de citation
IM
Pagination
940-947Subventions
Organisme : NHLBI NIH HHS
ID : K23 HL132011
Pays : United States
Organisme : NHLBI NIH HHS
ID : R42 HL106864
Pays : United States
Organisme : NHLBI NIH HHS
ID : R42 HL117397
Pays : United States
Commentaires et corrections
Type : CommentIn
Informations de copyright
Copyright © 2020 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.
Références
Circ Cardiovasc Imaging. 2011 Nov;4(6):610-9
pubmed: 21911738
Eur Heart J. 2016 Jun 1;37(21):1642-50
pubmed: 26417058
Stat Med. 2008 Jan 30;27(2):157-72; discussion 207-12
pubmed: 17569110
JACC Cardiovasc Imaging. 2019 Aug;12(8 Pt 1):1585-1587
pubmed: 31005535
JACC Cardiovasc Imaging. 2018 Oct;11(10):1433-1444
pubmed: 29454776
JACC Cardiovasc Imaging. 2020 Jan;13(1 Pt 1):58-65
pubmed: 31005520
Circ Cardiovasc Imaging. 2009 Sep;2(5):356-64
pubmed: 19808623
JACC Cardiovasc Imaging. 2018 Oct;11(10):1448-1457
pubmed: 29248649
Heart. 1999 Feb;81(2):111-3
pubmed: 9922343
JACC Cardiovasc Imaging. 2019 Sep;12(9):1769-1779
pubmed: 30409557
Eur Heart J Cardiovasc Imaging. 2013 Mar;14(3):205-12
pubmed: 23161791
Heart. 1999 Mar;81(3):229-31
pubmed: 10026340
Heart. 2008 Mar;94(3):262-3
pubmed: 18276809
Eur Heart J Cardiovasc Imaging. 2019 Nov 1;20(11):1259-1261
pubmed: 31157364
Circulation. 2017 Jun 6;135(23):2313-2315
pubmed: 28584033
JACC Cardiovasc Imaging. 2019 Aug;12(8 Pt 2):1686-1695
pubmed: 30409558
Stat Med. 2004 Jul 15;23(13):2109-23
pubmed: 15211606
J Am Coll Cardiol. 2009 Dec 29;55(1):1-16
pubmed: 20117357
Eur Heart J Cardiovasc Imaging. 2015 Mar;16(3):307-15
pubmed: 25246506
JACC Cardiovasc Imaging. 2018 Oct;11(10):1419-1429
pubmed: 29361479
Circ Res. 1991 Sep;69(3):561-70
pubmed: 1873859
J Am Coll Cardiol. 1999 Sep;34(3):618-20
pubmed: 10483939
Lancet. 2001 Jan 6;357(9249):21-8
pubmed: 11197356
Am J Physiol Heart Circ Physiol. 2007 Mar;292(3):H1452-9
pubmed: 17098822