BACKGROUND: The value of the 12-lead electrocardiogram (ECG) to identify the exit site of postinfarction ventricular tachycardia (VT) has been questioned. The purpose of this study was to assess the accuracy of a computerized algorithm for identifying a VT exit site on the basis of the 12-lead ECG. METHODS AND RESULTS: In 34 postinfarction patients, pace mapping was performed from within scar tissue. A computerized algorithm that used a supervised learning method (support vector machine) received the digitized pace-map morphologies combined with the pacing sites as training data. No other information (ie, infarct localization, bundle branch block morphology, axis, or R-wave pattern) was used in the algorithm. The training data were validated in 58 VTs in 33 patients. The sizes of 10 different anatomic sections within the heart were determined by using the pace maps as the determining factor. Accuracy was found to be 69% for pace maps, and when 2 adjacent regions were combined, accuracy improved to 88%. Validation of the data in 33 patients showed an accuracy of 71% for localizing a VT exit site to 1 of the 10 regions within the left ventricle. If combined with the best adjacent region, accuracy improved to 88%. The median anatomic size of each section was 21 cm(2). The median spatial resolution of the 12-lead ECG pattern of the pace maps for a particular region was 15 cm(2). CONCLUSION: The 12-lead ECG of postinfarction VT contains localizing information that enables determination of a region of interest in the 10-20 cm(2) range for more than 70% of VT exit sites in a given sector. Copyright Â
BACKGROUND: The value of the 12-lead electrocardiogram (ECG) to identify the exit site of postinfarction ventricular tachycardia (VT) has been questioned. The purpose of this study was to assess the accuracy of a computerized algorithm for identifying a VT exit site on the basis of the 12-lead ECG. METHODS AND RESULTS: In 34 postinfarction patients, pace mapping was performed from within scar tissue. A computerized algorithm that used a supervised learning method (support vector machine) received the digitized pace-map morphologies combined with the pacing sites as training data. No other information (ie, infarct localization, bundle branch block morphology, axis, or R-wave pattern) was used in the algorithm. The training data were validated in 58 VTs in 33 patients. The sizes of 10 different anatomic sections within the heart were determined by using the pace maps as the determining factor. Accuracy was found to be 69% for pace maps, and when 2 adjacent regions were combined, accuracy improved to 88%. Validation of the data in 33 patients showed an accuracy of 71% for localizing a VT exit site to 1 of the 10 regions within the left ventricle. If combined with the best adjacent region, accuracy improved to 88%. The median anatomic size of each section was 21 cm(2). The median spatial resolution of the 12-lead ECG pattern of the pace maps for a particular region was 15 cm(2). CONCLUSION: The 12-lead ECG of postinfarction VT contains localizing information that enables determination of a region of interest in the 10-20 cm(2) range for more than 70% of VT exit sites in a given sector. Copyright Â
Authors: Edmond M Cronin; Frank M Bogun; Philippe Maury; Petr Peichl; Minglong Chen; Narayanan Namboodiri; Luis Aguinaga; Luiz Roberto Leite; Sana M Al-Khatib; Elad Anter; Antonio Berruezo; David J Callans; Mina K Chung; Phillip Cuculich; Andre d'Avila; Barbara J Deal; Paolo Della Bella; Thomas Deneke; Timm-Michael Dickfeld; Claudio Hadid; Haris M Haqqani; G Neal Kay; Rakesh Latchamsetty; Francis Marchlinski; John M Miller; Akihiko Nogami; Akash R Patel; Rajeev Kumar Pathak; Luis C Saenz Morales; Pasquale Santangeli; John L Sapp; Andrea Sarkozy; Kyoko Soejima; William G Stevenson; Usha B Tedrow; Wendy S Tzou; Niraj Varma; Katja Zeppenfeld Journal: J Interv Card Electrophysiol Date: 2020-10 Impact factor: 1.900
Authors: Edmond M Cronin; Frank M Bogun; Philippe Maury; Petr Peichl; Minglong Chen; Narayanan Namboodiri; Luis Aguinaga; Luiz Roberto Leite; Sana M Al-Khatib; Elad Anter; Antonio Berruezo; David J Callans; Mina K Chung; Phillip Cuculich; Andre d'Avila; Barbara J Deal; Paolo Della Bella; Thomas Deneke; Timm-Michael Dickfeld; Claudio Hadid; Haris M Haqqani; G Neal Kay; Rakesh Latchamsetty; Francis Marchlinski; John M Miller; Akihiko Nogami; Akash R Patel; Rajeev Kumar Pathak; Luis C Sáenz Morales; Pasquale Santangeli; John L Sapp; Andrea Sarkozy; Kyoko Soejima; William G Stevenson; Usha B Tedrow; Wendy S Tzou; Niraj Varma; Katja Zeppenfeld Journal: Europace Date: 2019-08-01 Impact factor: 5.214
Authors: Prashnna K Gyawali; Jaideep Vitthal Murkute; Maryam Toloubidokhti; Xiajun Jiang; B Milan Horacek; John L Sapp; Linwei Wang Journal: IEEE Trans Biomed Eng Date: 2022-01-21 Impact factor: 4.538
Authors: Ryan Missel; Prashnna K Gyawali; Jaideep Vitthal Murkute; Zhiyuan Li; Shijie Zhou; Amir AbdelWahab; Jason Davis; James Warren; John L Sapp; Linwei Wang Journal: Comput Biol Med Date: 2020-09-23 Impact factor: 4.589
Authors: Shijie Zhou; Amir AbdelWahab; B Milan Horáček; Paul J MacInnis; James W Warren; Jason S Davis; Ihab Elsokkari; David C Lee; Ciorsti J MacIntyre; Ratika Parkash; Chris J Gray; Martin J Gardner; Curtis Marcoux; Rajin Choudhury; Natalia A Trayanova; John L Sapp Journal: Circ Arrhythm Electrophysiol Date: 2020-06-15