Global longitudinal strain is a strong independent predictor of all-cause mortality in patients with aortic stenosis

L. G. Kearney, K. Lu, M. Ord, S. K. Patel, K. Profitis, G. Matalanis, L. M. Burrell, P. M. Srivastava
2012 European Heart Journal-Cardiovascular Imaging  
Aims 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. Methods and results Patients with AS (n ¼ 146) and age-matched controls (n ¼ 12) underwent baseline echocardiography to
more » ... 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 cm 2 and LVEF was 59 + 11%. In patients with a normal LVEF (n ¼ 122), the baseline GLS was controls 221 + 2%, mild AS 218 + 3%, moderate AS 217 + 3% and severe AS 215 + 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). Conclusions 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. ---
doi:10.1093/ehjci/jes115 pmid:22736713 fatcat:5v73k6gxwfc4bbsv4zvtm3yily