Validation of an automatic diagnosis of strict left bundle branch block criteria using 12‐lead electrocardiograms. Issue 2 (30th August 2016) - Xia, Xiaojuan; Ruwald, Anne‐Christine; Ruwald, Martin H.; Ugoeke, Nene; Szepietowska, Barbara; Kutyifa, Valentina; Aktas, Mehmet K.; Thomsen, Poul Erik B.; Zareba, Wojciech; Moss, Arthur J.; Couderc, Jean‐Philippe
Abstract : Aims: Strict left bundle branch block (LBBB) criteria were recently proposed to identify LBBB patients to benefit most from cardiac resynchronization therapy (CRT). The aim of our study was to automate identification of strict LBBB in order to facilitate its broader application. Methods: We developed a series of algorithms to automatically detect and measure parameters required for strict LBBB criteria and proposed a definition of QRS notch detection. The algorithms were developed using training ( n = 20) and validation ( n = 592) sets consisting of signal‐averaged 12‐lead ECGs (1, 000 Hz sampling) recorded from 612 LBBB patients from Multicenter Automatic Defibrillator Implantation Trial‐CRT. Four trained clinicians independently performed adjudication on 148 different ECGs for comparing automatic and manually adjudicated results, in addition to 13 ECGs for evaluation of intraobserver variability and 32 ECGs for interobserver variability. We assessed the performance of the automated algorithms using manually adjudicated ECGs as references. Results: Overall sensitivity and specificity for detecting strict LBBB were 95% and 86%, respectively. The mean absolute deviation (MAD) of QRS duration and notch/slur locations for the automated method versus the manual method was below 1 ms, and MAD values were lower than 2 ms for interobserver and intraobserver variability. Sensitivity and specificity for detecting notch and slur locations were 87% and 96% for notches and 78% and 90% for slurs using the automatic method. In addition 95% and 93% agreements for notches and 90% and 88% agreements for slurs were reached for intra‐ and interobserver. Conclusion: The proposed algorithms automatically measure QRS features for the diagnosis of strict LBBB. Our study shows good performance in reference to manual results.