The Bayesian approach improves the electrocardiographic diagnosis of broad complex tachycardia.

Despite numerous attempts at devising algorithms for diagnosing broad complex tachycardia (BCT) on the basis of the electrocardiogram (ECG), misdiagnosis is still common. The reason for this may lie with difficulty in implementing existent algorithms in practice, due to imperfect ascertainment of ECG features within them. An attempt was made to approach the problem afresh with the Bayesian inference by the construction of a diagnostic algorithm centered around the likelihood ratio (LR). Previously studied ECG features most effective in discriminating ventricular tachycardia (VT) from supraventricular tachycardia with aberrant conduction (SVTAC), according to their LR values, were selected for inclusion into a Bayesian diagnostic algorithm. A test set of 244 BCT ECGs was assembled and shown to three independent observers who were blinded to the diagnoses made at electrophysiological study. Their diagnostic accuracy by the Bayesian algorithm was compared against that by clinical judgement with the diagnoses from EPS as the criterial standard. Clinical judgement correctly diagnosed 35% of SVTAC, 85% of VT, and 47% of fascicular tachycardia. In comparison, by the Bayesian algorithm devised, 52% of SVTAC, 95% of VT, and 97% of fascicular tachycardia were correctly diagnosed. The Bayesian algorithm devised has proved to be superior to the clinical judgement of the observers who participated in this study, and theoretically will obviate the problem of imperfect ascertainment of ECG features. Hence, it holds the promise for being an effective tool for routine use in clinical practice.

Electrocardiographic criteria for diagnosis of irregular broad complex tachycardia with a high sensitivity for preexcited atrial fibrillation.

AF may appear as an irregular broad complex tachycardia (BCT) if atrioventricular conduction occurs via an accessory pathway (preexcited AF) or if bundle branch block (BBB), preexistent or rate related, exists in the His-Purkinje system (BBB-AF). While BBB-AF is relatively benign, preexcited AF may herald sudden cardiac death. Hence it is important that the two conditions can be reliably distinguished. Yet, there is no preexistent algorithms for this purpose. Griffith et al. previously proposed a simple algorithm for a similar problem, that of distinguishing the two differential diagnoses for regular BCT: VT and SVT with BBB, on the basis that unless the QRS morphologies in V1 and V6 are absolutely typical of BBB, VT will be diagnosed. The authors propose an extrapolation of this principle to irregular BCT by stating that, unless the QRS morphologies in V1 and V6 are absolutely typical of BBB, preexcited AF will be diagnosed. Seventy-five ECGs showing irregular BCT (41 preexcited AF and 34 BBB-AF) were shown to two fellows in electrophysiology who were given no other information and were instructed to diagnose preexcited AF unless the QRS morphology pattern was typical of BBB. Observer 1 achieved a sensitivity of 100% (41/41) and a specificity of 79% (27/34), while observer 2 achieved a sensitivity of 100% and a specificity of 85% (29/34). By QRS morphology pattern, an average sensitivity of 100% and specificity of 82% were achieved for the diagnosis of irregular BCT. The algorithm is simple and easy to implement and recommended for clinical use.