Association between layer-specific global longitudinal strain and adverse outcomes following acute coronary syndrome
Kristoffer Grundtvig Skaarup1, Allan Iversen1, Peter Godsk Jørgensen1
1Department of Cardiology, Herlev & Gentofte Hospital, University of Copenhagen, Kildegårdsvej 28, Copenhage Denmark.
Insights
Layer-specific global longitudinal strain (GLS) effectively predicts heart failure (HF) and cardiovascular death (CD) after acute coronary syndrome (ACS). This echocardiographic measure offers valuable prognostic insights beyond traditional assessments.
Area of Science:
- Cardiology
- Echocardiography
- Cardiovascular Imaging
Background:
- Acute coronary syndrome (ACS) is a leading cause of heart failure (HF) and cardiovascular death (CD).
- Accurate prognostication post-ACS is crucial for guiding patient management and improving outcomes.
- Layer-specific myocardial strain analysis offers a novel approach to assess cardiac function.
Purpose of the Study:
- To evaluate the prognostic capability of layer-specific global longitudinal strain (GLS) in predicting HF and CD following ACS.
- To determine if GLS provides incremental prognostic value beyond conventional echocardiographic parameters and clinical factors.
Main Methods:
- Retrospective analysis of 465 ACS patients who underwent transthoracic echocardiography post-percutaneous coronary intervention (PCI).
- Assessment of endomyocardial (GLSendo), mid-wall (GLS), and epimyocardial (GLSepi) global longitudinal strain.
- Multivariable Cox regression analysis and assessment of incremental prognostic value using c-statistics.
Main Results:
- Reduced GLSendo, GLS, and GLSepi were significantly associated with adverse outcomes (HF and/or CD).
- All layer-specific GLS parameters remained independently predictive of the composite outcome in multivariable models.
- GLS and GLSepi demonstrated incremental prognostic information when added to existing clinical and echocardiographic predictors.
Conclusions:
- Layer-specific GLS provides independent prognostic information for predicting HF and CD in ACS patients.
- GLS and GLSepi are valuable echocardiographic parameters that enhance risk stratification beyond traditional methods.
Aims:
To investigate the prognostic value of layer-specific global longitudinal strain (GLS) in predicting heart failure (HF) and cardiovascular death (CD) following acute coronary syndrome (ACS).
Methods And Results:
In this retrospective study, 465 ACS patients underwent transthoracic echocardiography following percutaneous coronary intervention (PCI). The primary endpoint was the composite of HF and/or CD with a median follow-up time of 4.6 (0.2-6.3) years. During follow-up 199 patients (42.7%) suffered HF and/or CD (176 developed HF and 38 suffered CD). Absolute endomyocardial global longitudinal strain (GLSendo) (12% vs. 17%, P < 0.001), GLS (11% vs. 14%, P < 0.001), and epimyocardial global longitudinal strain (GLSepi) (9% vs. 13%, P < 0.001) were all reduced in patients with an adverse outcome. In multivariable Cox regressions, which included clinical baseline characteristics and conventional echocardiographic measurements, GLS obtained from all layers remained independently associated with the composite outcome; GLSendo [hazard ratio: 1.19 (1.10-1.28), P < 0.001, per 1% decrease], GLS [hazard ratio 1.24 (1.14-1.35), P < 0.001, per 1% decrease], and GLSepi [hazard ratio 1.26 (1.15-1.39), P < 0.001, per 1% decrease]. No other echocardiographic measures remained independently associated with the composite outcome in these models. Finally, GLS and GLSepi provided incremental prognostic information on the risk of developing the composite endpoint, when added to all other clinical and echocardiographic measures [adding GLS (c-statistics: 0.76 vs. 0.74, P = 0.048) or adding GLSepi (c-statistics: 0.76 vs. 0.74, P = 0.039)].
Conclusion:
In ACS patients, layer-specific strain provides independent prognostic information regarding risk of developing HF and/or CD. Furthermore, only GLS and GLSepi provided incremental prognostic information when added to all other significant predictors.
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