Are wide complex tachycardia algorithms applicable in children and patients with congenital heart disease?
ABSTRACT Several algorithms have been developed to help determine the etiology of wide complex tachycardias (WCTs) in adults. Sensitivity and specificity for differentiating supraventricular tachycardia (SVT) with aberration from ventricular tachycardia (VT) in adults have been demonstrated to be as high as 98% and 97%. These algorithms have not been tested in the pediatric population. We hypothesize that these algorithms have lower diagnostic accuracy in children and patients with congenital heart disease.
A retrospective review of the pediatric electrophysiology database at Stanford from 2001 to 2008 was performed. All children with WCT, a 12-lead electrocardiogram (ECG) available for review, and an electrophysiology study confirming the etiology of the rhythm were included. Patients with a paced rhythm were excluded. The ECGs were analyzed by 2 electrophysiologists blinded to the diagnosis according to the algorithms described in Brugada et al,(2) and Vereckei et al.(5) Additional ECG findings were recorded by each electrophysiologist.
A total of 65 WCT ECGs in 58 patients were identified. Supraventricular tachycardia was noted in 62% (40/65) and VT in 38% (25/65) of the ECGs. The mean age was 13.5 years (SD ± 5.1), the mean weight was 51.8 kg (SD ± 22.4), and 48% (31/65) were male. The mean tachycardia cycle length was 340 milliseconds (SD ± 95). Congenital heart disease (CHD) was present in 37% (24/65) of patients (7 tetralogy of Fallot, 6 Ebstein's, 4 double-outlet right ventricle, 3 complex CHD, 2 d-transposition of great arteries, 1 status-post orthotopic heart transplantation, 1 ventricular septal defect). The Brugada algorithm correctly predicted the diagnosis 69% (45/65) of the time, the Vereckei algorithm correctly predicted the diagnosis 66% (43/65) of the time, and the blinded reviewer correctly predicted the diagnosis 78% (51/65) of the time. There was no difference in the efficacy of the algorithms in patients with CHD vs those with structurally normal hearts. The findings of left superior axis deviation (P < .01) and a notch in the QRS downstroke of V(1) or V(2) (P < .01) were more common in VT than SVT, whereas a positive QRS deflection in V(1) (P = .03) was more commonly present in SVT than VT.
The Brugada and Vereckei algorithms have lower diagnostic accuracy in the pediatric population and in patients with congenital heart disease than in the adult population. Left superior axis deviation and a notch in the QRS downstroke were more commonly associated with VT, whereas a positive QRS deflection in V(1) was more commonly associated with SVT in this population.
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ABSTRACT: To compare the sensitivity (SN), specificity (SP), and diagnostic accuracy (ACC) for ventricular tachycardia (VT) diagnosis of five electrocardiographic methods for wide QRS-complex tachycardia (WCT) differentiation, specifically the Brugada, Bayesian, Griffith, and aVR algorithms, and the lead II R-wave-peak-time (RWPT) criterion. We retrospectively analysed 260 WCTs from 204 patients with proven diagnoses. The SN, SP, ACC, and likelihood ratios (LRs) were determined for the five methods. Of the 260 tracings, there were 159 VTs and 101 supraventricular tachycardias. All five methods were found to have a similar ACC although the RWPT had a lower ACC than the Brugada algorithm (68.8 vs. 77.5%, P = 0.04). The RWPT had lower (60%) SN than the Brugada (89.0%), Griffith (94.2%), and Bayesian (89%) algorithms (P < 0.001). The Griffith algorithm showed lower (39.8%) SP than the RWPT (82.7%), Brugada (59.2%), and Bayesian (52.0%) algorithms (P< 0.05). The positive LRs for a VT diagnosis for the RWPT criterion and the Brugada, Bayesian, aVR, and Griffith algorithms were 3.46, 2.18, 1.86, 1.67, and 1.56, respectively. The present study is the first independent 'head-to-head' comparison of several WCT differentiation methods. We found that all five algorithms/criteria had rather moderate ACC, and that the newer methods were not more accurate than the classic Brugada algorithm. However, the algorithms/criteria differed significantly in terms of SN, SP, and LR, suggesting that the value of a diagnosis may differ depending on the method used.Europace 02/2012; 14(8):1165-71. · 2.77 Impact Factor
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ABSTRACT: The diagnostic values of the aVR lead or "Vereckei algorithm," and the lead II R-wave peak time (RWPT) criterion, recently devised for the differential diagnosis of wide QRS complex tachycardias (WCTs), were compared. A total of 212 WCTs (142 ventricular tachycardias [VTs], 62 supraventricular tachycardias [SVT], and eight preexcitation SVTs) from 145 patients with proven electrophysiologic diagnoses were retrospectively analyzed by seven examiners blinded to the electrophysiologic diagnoses. The overall test accuracy of the Vereckei algorithm was superior to that of the RWPT criterion (84.3% vs. 79.6%; p = 0.0003). The sensitivity of the Vereckei algorithm for VT diagnosis was greater than that of RWPT criterion (92.4% vs. 79.1%; p < 0.0001). The negative predictive value (NPV) for the Vereckei algorithm was also greater (77.8%; 95% confidence interval [CI] = 73.6% to 82.1%) than that of the RWPT criterion (61.6%; 95% CI = 57.6% to 65.6%). The specificity of the Vereckei algorithm was lower than that of the RWPT criterion (64.7% vs. 80.9%; p < 0.0001). The positive predictive value (PPV) was also lower for the Vereckei algorithm (86.4%; 95% CI = 84.4% to 88.4%) than for the RWPT criterion (90.9%; 95% CI = 89.1% to 92.8%). Incorrect diagnoses made by the Vereckei algorithm were mainly due to misdiagnosis of SVT as VT (65.7% of cases), and those made by the RWPT criterion were due to the more dangerous misdiagnosis of VT as SVT (72.5% of cases). The Vereckei algorithm was superior in overall test accuracy, sensitivity, and NPV for VT diagnosis and inferior in specificity and PPV to the RWPT criterion. All of these parameters were lower in "real life" than those reported by the original authors for each of the particular electrocardiographic methods.Academic Emergency Medicine 11/2013; 20(11):1121-1130. · 1.76 Impact Factor