Citations

IntroductionAdjudication of thousands of implantable cardioverter defibrillator (ICD)‐treated arrhythmia episodes is labor intensive and, as a result, is most often left undone. The objective of this study was to evaluate an automatic classification algorithm for adjudication of ICD‐treated arrhythmia episodes.MethodsThe algorithm uses a machine learning algorithm and was developed using 776 arrhythmia episodes. The algorithm was validated on 131 dual‐chamber ICD shock episodes from 127 patients adjudicated by seven electrophysiologists (EPs). Episodes were classified by panel consensus as ventricular tachycardia/ventricular fibrillation (VT/VF) or non‐VT/VF, with the resulting classifications used as the reference. Subsequently, each episode electrogram (EGM) data was randomly assigned to three EPs without the atrial lead information, and to three EPs with the atrial lead information. Those episodes were also classified by the automatic algorithm with and without atrial information. Agreement with the reference was compared between the three EPs consensus group and the algorithm.ResultsThe overall agreement with the reference was similar between three‐EP consensus and the algorithm for both with atrial EGM (94% vs 95%, P = 0.87) and without atrial EGM (90% vs 91%, P = 0.91). The odds of accurate adjudication, after adjusting for covariates, did not significantly differ between the algorithm and EP consensus (odds ratio 1.02, 95% confidence interval: 0.97–1.06).ConclusionsThis algorithm performs at a level comparable to an EP panel in the adjudication of arrhythmia episodes treated by both dual‐ and single‐chamber ICDs. This type of algorithm has the potential for automated analysis of clinical ICD episodes, and adjudication of EGMs for research studies and quality analyses.

The traditional route for teaching cardiac anatomy involves didactic instruction, cadaver dissections, and familiarization with the main structure and relationships of the cardiac chambers, valves, and vasculature. In contemporary cardiac electrophysiology, however, a very different view of anatomy is required including details rarely appreciated with a general overview. In this review, we discuss the critical advances in cardiac electrophysiology that were possible only because of understanding detailed anatomic relationships. While we briefly discuss the clinical relevance, we explain in depth the necessary structural information for the student of clinical anatomy. Interspersed through the text are boxes that highlight and summarize the critical pieces of knowledge to be borne in mind while studying the fascinating structural anatomy of the human heart. Clin. Anat. 27:885–893, 2014. © 2014 Wiley Periodicals, Inc.

ECG Lead I and Outflow Tract ArrhythmiaIntroductionOutflow tract ventricular arrhythmia (OTVA) can be complicated to target for ablation when originating from either the periaortic or pulmonary valve (PV) region. Both sites may present with a small R wave in lead V1. However, the utility of lead I in distinguishing these arrhythmia locations is unknown.Methods and ResultsThirty‐six consecutive patients (mean age 41 ± 14 years, 13 male) underwent catheter ablation for OTVA. OTVA origin was determined from intracardiac electrogram tracings and electroanatomic maps. Observers blinded to results measured QRS waveform amplitude and duration from standard 12‐lead ECG tracings. Measurements with highest diagnostic performance were modeled into an algorithm. Sites of successful ablation were anterior right ventricular outflow tract (RVOT; n = 6), posterior RVOT (n = 4), PV (n = 18), and right coronary cusp (RCC; n = 8). Highest performing surface ECG discriminators were from lead I to V1 vectors: RCC, lead I R wave ≥ 1.5 mV, and V1 R wave ≥2.0 mV (sensitivity 87%, specificity 93%); PV, V1 R wave > 0 mV, and lead I R/(R+S) ≤ 0.75 (sensitivity 78%, specificity 72%); anterior RVOT, V1 R wave = 0 mV, and lead I R/(R+S) <0.4 (sensitivity 67%, specificity 97%); posterior RVOT, V1 R wave > 0 mV, and lead I R/(R+S) > 0.75 (sensitivity 75%, specificity 84%). Sequential algorithmic application of these criteria resulted in an overall accuracy of 72% in predicting site of OTVA origin.ConclusionsA relatively large R wave in lead I is seen with RCC origin but not PV origin. A sequential algorithm has limited but potentially significant value beyond assessment of lead I in approaching OTVA.

Ablation and Mapping of the Conus Papillary MuscleCatheter ablation is an increasingly used and successful treatment choice for right ventricular outflow tract (RVOT) arrhythmias. While the role of endocavitary structures and the regional morphology of the ventricular inflow tract and the right atrium as a cause for difficulty with successful ablation are well described, similar issues within the RVOT are not well understood. It is also not commonly appreciated that one of the papillary muscles is located within the proximal RVOT. We report 3 patients in which ventricular arrhythmia was targeted and ablated in the conus papillary muscle. The anatomic features, potential role of the fascicular conduction system, and unique challenges with mapping arrhythmia arising from this structure are discussed.