Could quantitative longitudinal peak systolic strain help in the detection of left ventricular wall motion abnormalities in our daily echocardiographic practice?
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文摘
Transthoracic echocardiography is the most commonly used tool for the detection of left ventricular wall motion (LVWM) abnormalities using “naked eye evaluation”. This subjective and operator-dependent technique requires a high level of clinical training and experience. Two-dimensional speckle-tracking echocardiography (2D-STE), which is less operator-dependent, has been proposed for this purpose. However, the role of on-line segmental longitudinal peak systolic strain (LPSS) values in the prediction of LVWM has not been fully evaluated.

Aim

To test segmental LPSS for predicting LVWM abnormalities in routine echocardiography laboratory practice.

Methods

LVWM was evaluated by an experienced cardiologist, during routine practice, in 620 patients; segmental LPSS values were then calculated.

Results

In this work, reflecting real life, 99.6% of segments were successfully tracked. Mean (95% confidence interval [CI]) segmental LPSS values for normal basal (n = 3409), mid (n = 3468) and apical (n = 3466) segments were –16.7% (–16.9% to –16.5%), –18.2% (–18.3% to –18.0%) and –21.1% (–21.3% to –20.9%), respectively. Mean (95% CI) segmental LPSS values for hypokinetic basal (n = 114), mid (n = 116) and apical (n = 90) segments were –7.7% (–9.0% to –6.3%), –10.1% (–11.1% to –9.0%) and –9.3% (–10.5% to –8.1%), respectively. Mean (95% CI) segmental LPSS values for akinetic basal (n = 128), mid (n = 95) and apical (n = 91) segments were –6.6% (–8.0% to –5.1%), –6.1% (–7.7% to –4.6%) and –4.2% (–5.4% to –3.0%), respectively. LPSS allowed the differentiation between normal and abnormal segments at basal, mid and apical levels. An LPSS value ≥ –12% detected abnormal segmental motion with a sensitivity of 78% for basal, 70% for mid and 82% for apical segments.

Conclusions

Segmental LPSS values may help to differentiate between normal and abnormal left ventricular segments.

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