The Bayesian approach improves the electrocardiographic diagnosis of broadcomplex tachycardia

Despite numerous attempts at devising algorithms for diagnosing broad compl ex tachycardia (BCT) on the basis of the electrocardiogram (ECG), misdiagno sis is still common. The reason for this may lie with difficulty in impleme nting existent algorithms in practice, due to imperfect ascertainment of EC G features within them. An attempt was made to approach the problem afresh with the Bayesian inference by the construction of a diagnostic algorithm c entered around the likelihood ratio (LR). Previously studied ECG features m ost effective in discriminating ventricular tachycardia (VT) from supravent ricular 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 obs ervers 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 sta ndard. Clinical judgement correctly diagnosed 35% of SVTAC, 85% of VT, and 47% of fascicular tachycardia. In comparison, by the Bayesian algorithm dev ised, 52% of SVTAC, 95% of VT, and 97% of fascicular tachycardia were corre ctly diagnosed. The Bayesian algorithm devised has proved to be superior to the clinical judgement of the observers who participated in this study, an d theoretically will obviate the problem of imperfect ascertainment of ECG features. Hence, it holds the promise for being an effective tool for routi ne use in clinical practice.