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• W2796979148 abstract "Myocardial relaxation is impaired in almost all cases with left ventricular diastolic dysfunction (LVDD) and is a strong predictor of cardiovascular and all-cause mortality. This study investigated the feasibility of signal-processed surface electrocardiography (spECG) as a diagnostic tool for predicting the presence of abnormal cardiac muscle relaxation. A total of 188 outpatients referred for coronary computed tomography (CT) angiography underwent an echocardiogram for assessment of LVDD. The use of 12-lead spECG for predicting myocardial relaxation abnormalities as identified using tissue Doppler echocardiography was validated with machine-learning approaches. A total of 188 subjects underwent diagnostic testing, with 133 (70%) showing abnormal myocardial relaxation on tissue Doppler imaging. A 12-lead spECG showed an area under the curve of 91% (95% confidence interval: 86% to 95%) for prediction of abnormal myocardial mechanical relaxation with a sensitivity and specificity of 80% and 84%, respectively. The spECG demonstrated more accurate diagnostic performance in individuals age ≥60 years as well as those with obesity or hypertension, compared with their respective counterparts. Prediction of low early diastolic relaxation velocity (e′) also correctly identified concomitant significant underlying coronary artery disease in 23 of 28 cases (82%). Furthermore, a superior integrated discrimination and net reclassification improvement was observed for spECG over clinical features and traditional ECG. The spECG provides a robust prediction of abnormal myocardial relaxation. These data suggest a potential role for spECG as a novel screening strategy for identifying patients at risk for LVDD who would benefit undergoing echocardiographic evaluations." @default.
• W2796979148 created "2018-04-24" @default.
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• W2796979148 date "2018-04-01" @default.
• W2796979148 modified "2023-09-30" @default.
• W2796979148 title "Prediction of Abnormal Myocardial Relaxation From Signal Processed Surface ECG" @default.
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• W2796979148 doi "https://doi.org/10.1016/j.jacc.2018.02.024" @default.
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