Σφακιανάκης Αλέξανδρος
ΩτοΡινοΛαρυγγολόγος
Αναπαύσεως 5 Άγιος Νικόλαος
Κρήτη 72100
00302841026182
00306932607174
alsfakia@gmail.com

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Πέμπτη 17 Αυγούστου 2017

Prediction for Improvement and Remodeling in First-Onset Myocardial Infarction by Speckle Tracking Echocardiography: Is Global or Regional Selection Better?

Publication date: October 2017
Source:Ultrasound in Medicine & Biology, Volume 43, Issue 10
Author(s): Bo Hu, Qing Zhou, Jinling Chen, Tuantuan Tan, Xue Yao, Hongning Song, Ruiqiang Guo
Cardiac function improvement and chamber remodeling after the onset of acute myocardial infarction (AMI) is crucial as it is closely related to the outcomes of patients. We sought to investigate the predictive value of left ventricular (LV) global and region of interest (ROI) assessment for prognosis of AMI patients by speckle tracking echocardiography (STE). We prospectively enrolled 81 first-onset AMI patients for baseline and 6-mo follow-up analysis. The echocardiography-derived parameters were compared in receiver operator characteristics (ROC) analysis for prediction of LV remodeling (LVR) (a minimum 20% increase of LV end-diastolic volume) and cardiac function improvement (a minimum 5% increase of LV ejection fraction). The ROI strain was selected by wall motion score index (WMSI) scores ≥2. The time of whole analysis process was recorded. Cut-off values of −9.92% for global circumferential strain (CS) and −5.53% for ROI CS predicted LVR. Cut-off values of −10.40% for global longitudinal strain (LS) and −5.33% for ROI LS predicted cardiac function improvement. Areas under curves of global and ROI parameters were comparable in ROC analysis (p > 0.05, all). The time of global analysis was less than the time of ROI analysis (p < 0.05) and the reproducibility of global analysis was slightly better than the ROI analysis. Our results demonstrated that STE was valuable for the prediction of LVR and cardiac function improvement after AMI. Compared with ROI parameters, global parameters were more integral and efficient as predictive factors with high predictive power, less analysis time and better reproducibility.



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