Wei-Chun Wu1, Hong Ma1,2, Rong-Ai Xie3, Li-Jian Gao1, Yue Tang4, Hao Wang1. 1. State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China. 2. Department of Echocardiography, the First Affiliated Hospital of Nanjing Medical University, Nanjing, China. 3. Department of Cardiology, Peking University Shougang Hospital, Beijing, China. 4. The Animal Experimental Center of Fuwai Hospital, Beijing, China.
Abstract
BACKGROUND: This study evaluated the role of two-dimensional speckle tracking echocardiography (2DSTE) for predicting left ventricular (LV) diastolic dysfunction in pacing-induced canine heart failure. METHODS: Pacing systems were implanted in 8 adult mongrel dogs, and continuous rapid right ventricular pacing (RVP, 240 beats/min) was maintained for 2 weeks. The obtained measurements from 2DSTE included global strain rate during early diastole (SRe) and during late diastole (SRa) in the longitudinal (L-SRe, L-SRa), circumferential (C-SRe, C-SRa), and radial directions (R-SRe, R-SRa). Changes in heart morphology were observed by light microscopy and transmission electron microscopy at 2 weeks. RESULTS: The onset of LV diastolic dysfunction with early systolic dysfunction occurred 3 days after RVP initiation. Most of the strain rate imaging indices were altered at 1 or 3 days after RVP onset and continued to worsen until heart failure developed. Light and transmission electron microscopy showed myocardial vacuolar degeneration and mitochondrial swelling in the left ventricular at 2 weeks after RVP onset. Pearson's correlation analysis revealed that parameters of conventional echocardiography and 2DSTE showed moderate correlation with LV pressure parameters, including E/Esep' (r = 0.58, P < 0.01), L-SRe (r = -0.58, P < 0.01), E/L-SRe (r = 0.65, P < 0.01), and R-SRe (r = 0.53, P < 0.01). ROC curves analysis showed that these indices of conventional echocardiography and strain rate imaging could effectively predict LV diastolic dysfunction (area under the curve: E/Esep' 0.78; L-SRe 0.84; E/L-SRe 0.80; R-SRe 0.80). CONCLUSION: 2DSTE was a sensitive and accurate technique that could be used for predicting LV diastolic dysfunction in canine heart failure model.
BACKGROUND: This study evaluated the role of two-dimensional speckle tracking echocardiography (2DSTE) for predicting left ventricular (LV) diastolic dysfunction in pacing-induced canineheart failure. METHODS: Pacing systems were implanted in 8 adult mongrel dogs, and continuous rapid right ventricular pacing (RVP, 240 beats/min) was maintained for 2 weeks. The obtained measurements from 2DSTE included global strain rate during early diastole (SRe) and during late diastole (SRa) in the longitudinal (L-SRe, L-SRa), circumferential (C-SRe, C-SRa), and radial directions (R-SRe, R-SRa). Changes in heart morphology were observed by light microscopy and transmission electron microscopy at 2 weeks. RESULTS: The onset of LV diastolic dysfunction with early systolic dysfunction occurred 3 days after RVP initiation. Most of the strain rate imaging indices were altered at 1 or 3 days after RVP onset and continued to worsen until heart failure developed. Light and transmission electron microscopy showed myocardial vacuolar degeneration and mitochondrial swelling in the left ventricular at 2 weeks after RVP onset. Pearson's correlation analysis revealed that parameters of conventional echocardiography and 2DSTE showed moderate correlation with LV pressure parameters, including E/Esep' (r = 0.58, P < 0.01), L-SRe (r = -0.58, P < 0.01), E/L-SRe (r = 0.65, P < 0.01), and R-SRe (r = 0.53, P < 0.01). ROC curves analysis showed that these indices of conventional echocardiography and strain rate imaging could effectively predict LV diastolic dysfunction (area under the curve: E/Esep' 0.78; L-SRe 0.84; E/L-SRe 0.80; R-SRe 0.80). CONCLUSION:2DSTE was a sensitive and accurate technique that could be used for predicting LV diastolic dysfunction in canineheart failure model.