AIMS: In patients without pre-excitation, the differential diagnosis of paroxysmal atrioventricular (AV) reciprocating tachycardias consists mainly of atrioventricular nodal re-entrant tachycardias (AVNRTs) and AV reciprocating tachycardias (AVRTs) through a concealed bypass. Our purpose was to validate the diagnostic accuracy of a predictive logistic model using classical electrocardiographic (ECG) criteria. METHODS AND RESULTS: We included 470 patients who underwent an electrophysiological study for paroxysmal, regular, and narrow-QRS complex tachycardia without pre-excitation in sinus rhythm. The ECG recordings were reviewed for the presence of the following: (i) pseudo r' deflection (V1) and/or pseudo s-wave (inferior leads), (ii) identifiable P-wave after the QRS complex, (iii) QRS alternans, and (iv) repolarization abnormalities during tachycardia. We performed a cross-validation method using the first 300 patients to develop a logistic model to predict the tachycardia diagnosis. The model was validated through the remaining 170 patients. The invasive study demonstrated AVNRT in 314 patients and AVRT in 156 patients. The presence of pseudo r' deflection and/or pseudo s-wave, a visible P-wave after the QRS complex, and QRS alternans were selected by a stepwise multiple logistic regression analysis as predictors for the diagnosis of AVNRT. The application of the model in the validation group showed a shrinkage prediction factor of 3%. Diagnostic probabilities for both tachycardia mechanisms depending on every combination of selected ECG criteria were >75% in 70% of the patients. CONCLUSION: The presence of pseudo r' deflection and/or pseudo s-wave, an identifiable P-wave after the QRS, and QRS alternans during tachycardia permit us to derive a reliable logistic model to predict the mechanism of paroxysmal AVRT in patients without pre-excitation. Precise probabilities for a correct diagnosis associated with every combination of those classical ECG criteria are presented.
AIMS: In patients without pre-excitation, the differential diagnosis of paroxysmal atrioventricular (AV) reciprocating tachycardias consists mainly of atrioventricular nodal re-entrant tachycardias (AVNRTs) and AV reciprocating tachycardias (AVRTs) through a concealed bypass. Our purpose was to validate the diagnostic accuracy of a predictive logistic model using classical electrocardiographic (ECG) criteria. METHODS AND RESULTS: We included 470 patients who underwent an electrophysiological study for paroxysmal, regular, and narrow-QRS complex tachycardia without pre-excitation in sinus rhythm. The ECG recordings were reviewed for the presence of the following: (i) pseudo r' deflection (V1) and/or pseudo s-wave (inferior leads), (ii) identifiable P-wave after the QRS complex, (iii) QRS alternans, and (iv) repolarization abnormalities during tachycardia. We performed a cross-validation method using the first 300 patients to develop a logistic model to predict the tachycardia diagnosis. The model was validated through the remaining 170 patients. The invasive study demonstrated AVNRT in 314 patients and AVRT in 156 patients. The presence of pseudo r' deflection and/or pseudo s-wave, a visible P-wave after the QRS complex, and QRS alternans were selected by a stepwise multiple logistic regression analysis as predictors for the diagnosis of AVNRT. The application of the model in the validation group showed a shrinkage prediction factor of 3%. Diagnostic probabilities for both tachycardia mechanisms depending on every combination of selected ECG criteria were >75% in 70% of the patients. CONCLUSION: The presence of pseudo r' deflection and/or pseudo s-wave, an identifiable P-wave after the QRS, and QRS alternans during tachycardia permit us to derive a reliable logistic model to predict the mechanism of paroxysmal AVRT in patients without pre-excitation. Precise probabilities for a correct diagnosis associated with every combination of those classical ECG criteria are presented.
Authors: Esteban González-Torrecilla; Angel Arenal; Felipe Atienza; Tomás Datino; Leonardo F Atea; David Calvo; Marta Pachón; Angel Miracle; Francisco Fernández-Avilés Journal: Ann Noninvasive Electrocardiol Date: 2011-01 Impact factor: 1.468
Authors: Halil Dogan; Dogac Niyazi Ozucelik; Kurtulus Aciksari; Ilker Murat Caglar; Nursel Okutan; Mustafa Yazicioglu; Baris Murat Avyaci; Cem Simsek; Derya Ozasir; Tufan Akin Giray; Cem Ayan; Feridun Celikmen; Yıldız Okuturlar; Sezgin Sarikaya Journal: Int J Clin Exp Med Date: 2015-06-15
Authors: Karol Deutsch; Sebastian Stec; Piotr Kukla; Aleksandra Morka; Marek Jastrzebski; Artur Baszko; Maciej Pitak; Janusz Sledz; Kamil Fijorek; Mariusz Mazij; Bartosz Ludwik; Marcin Gubaro; Leslaw Szydlowski Journal: Medicine (Baltimore) Date: 2015-12 Impact factor: 1.817