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L.G. Kearney, K. Lu, M. Ord, S.K. Patel, K. Profitis, G. Matalanis, L.M. Burrell, P.M. Srivastava, Global longitudinal strain is a strong independent predictor of all-cause mortality in patients with aortic stenosis, European Heart Journal - Cardiovascular Imaging, Volume 13, Issue 10, October 2012, Pages 827–833, https://doi.org/10.1093/ehjci/jes115
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Abstract
To assess the capacity of global longitudinal strain (GLS) in patients with aortic stenosis (AS) to (i) detect the subclinical left ventricular (LV) dysfunction [LV ejection fraction (LVEF) ≥50% patients]; (ii) predict all-cause mortality and major adverse cardiac events (MACE) (all patients), and (iii) provide incremental prognostic information over current risk markers.
Patients with AS (n= 146) and age-matched controls (n= 12) underwent baseline echocardiography to assess AS severity, conventional LV parameters and GLS via speckle tracking echocardiography. Baseline demographics, symptom severity class and comorbidities were recorded. Outcomes were identified via hospital record review and subject/physician interview. The mean age was 75 ± 11, 62% were male. The baseline aortic valve (AV) area was 1.0 ± 0.4 cm2 and LVEF was 59 ± 11%. In patients with a normal LVEF (n= 122), the baseline GLS was controls −21 ± 2%, mild AS −18 ± 3%, moderate AS −17 ± 3% and severe AS −15 ± 3% (P< 0.001). GLS correlated with the LV mass index, LVEF, AS severity, and symptom class (P< 0.05). During a median follow-up of 2.1 (inter-quartile range: 1.8–2.4) years, there were 20 deaths and 101 MACE. Unadjusted hazard ratios (HRs) for GLS (per %) were all-cause mortality (HR: 1.42, P< 0.001) and MACE (HR: 1.09, P< 0.001). After adjustment for clinical and echocardiographic variables, GLS remained a strong independent predictor of all-cause mortality (HR: 1.38, P< 0.001).
GLS detects subclinical dysfunction and has incremental prognostic value over traditional risk markers including haemodynamic severity, symptom class, and LVEF in patients with AS. Incorporation of GLS into risk models may improve the identification of the optimal timing for AV replacement.
