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Controlled Clinical Trial
Journal Article
Electrocardiogram-based algorithm to predict the left ventricular lead position in recipients of cardiac resynchronization systems.
Pacing and Clinical Electrophysiology : PACE 2009 March
INTRODUCTION: Biventricular pacing is associated with various electrocardiographic patterns depending on the position of the left ventricular (LV) lead. We aimed to develop an electrocardiogram-based algorithm to predict the position of the LV lead.
METHODS: The algorithm was developed in 100 consecutive recipients of cardiac resynchronization therapy (CRT) systems. QRS axis, morphology, and polarity were analyzed with a view to define the specific electrocardiographic characteristics associated with the various LV lead positions. The algorithm was prospectively validated in 50 consecutive CRT device recipients.
RESULTS: The first analysis of the algorithm was the QRS morphology in V(1). A positive R wave in V(1) suggested LV lateral or posterior wall stimulation. A QS pattern was specific of anterior LV leads. In the presence of an R wave in V(1), V(6) was analyzed to distinguish between an inferior and anterior LV lead. Inferior leads were never associated with a positive V(6). To differentiate between lateral and posterior positions, we analyzed the pattern in V(2). Lateral leads were associated with an R morphology in V(1) and a negative V(2). Posterior leads were associated with an R morphology in V(1) and V(2). The algorithm allowed a reliable distinction between an inferior or anterior and a lateral or posterior lead position in 90% of patients. Inferior, anterior, lateral, and posterior positions were reliably distinguished in 80% of patients.
CONCLUSION: This algorithm predicted the position of the LV lead with a high sensitivity and predictive value.
METHODS: The algorithm was developed in 100 consecutive recipients of cardiac resynchronization therapy (CRT) systems. QRS axis, morphology, and polarity were analyzed with a view to define the specific electrocardiographic characteristics associated with the various LV lead positions. The algorithm was prospectively validated in 50 consecutive CRT device recipients.
RESULTS: The first analysis of the algorithm was the QRS morphology in V(1). A positive R wave in V(1) suggested LV lateral or posterior wall stimulation. A QS pattern was specific of anterior LV leads. In the presence of an R wave in V(1), V(6) was analyzed to distinguish between an inferior and anterior LV lead. Inferior leads were never associated with a positive V(6). To differentiate between lateral and posterior positions, we analyzed the pattern in V(2). Lateral leads were associated with an R morphology in V(1) and a negative V(2). Posterior leads were associated with an R morphology in V(1) and V(2). The algorithm allowed a reliable distinction between an inferior or anterior and a lateral or posterior lead position in 90% of patients. Inferior, anterior, lateral, and posterior positions were reliably distinguished in 80% of patients.
CONCLUSION: This algorithm predicted the position of the LV lead with a high sensitivity and predictive value.
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