Partho P Sengupta1, Hemant Kulkarni2, Jagat Narula3. 1. Department of Cardiology, Icahn School of Medicine at Mount Sinai University, New York, New York; West Virginia University Heart and Vascular Institute, West Virginia University, Morgantown, West Virginia. Electronic address: Partho.sengupta@wvumedicine.org. 2. M&H Research, LLC, San Antonio, Texas. 3. Department of Cardiology, Icahn School of Medicine at Mount Sinai University, New York, New York.
Abstract
BACKGROUND: 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. OBJECTIVES: This study investigated the feasibility of signal-processed surface electrocardiography (spECG) as a diagnostic tool for predicting the presence of abnormal cardiac muscle relaxation. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
BACKGROUND: 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. OBJECTIVES: This study investigated the feasibility of signal-processed surface electrocardiography (spECG) as a diagnostic tool for predicting the presence of abnormal cardiac muscle relaxation. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
Authors: Albert K Feeny; Mina K Chung; Anant Madabhushi; Zachi I Attia; Maja Cikes; Marjan Firouznia; Paul A Friedman; Matthew M Kalscheur; Suraj Kapa; Sanjiv M Narayan; Peter A Noseworthy; Rod S Passman; Marco V Perez; Nicholas S Peters; Jonathan P Piccini; Khaldoun G Tarakji; Suma A Thomas; Natalia A Trayanova; Mintu P Turakhia; Paul J Wang Journal: Circ Arrhythm Electrophysiol Date: 2020-07-06
Authors: Chris D Cantwell; Yumnah Mohamied; Konstantinos N Tzortzis; Stef Garasto; Charles Houston; Rasheda A Chowdhury; Fu Siong Ng; Anil A Bharath; Nicholas S Peters Journal: Comput Biol Med Date: 2018-10-18 Impact factor: 4.589