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J Cardiovasc Electrophysiol ; 35(6): 1083-1094, 2024 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-38514968

RESUMO

INTRODUCTION: Precise electrocardiographic localization of accessory pathways (AP) can be challenging. Seminal AP localization studies were limited by complexity of algorithms and sample size. We aimed to create a nonalgorithmic method for AP localization based on color-coded maps of AP distribution generated by a web-based application. METHODS: APs were categorized into 19 regions/types based on invasive electrophysiologic mapping. Preexcited QRS complexes were categorized into 6 types based on polarity and notch/slur. For each QRS type in each lead the distribution of APs was visualized on a gradient map. The principle of common set was used to combine the single lead maps to create the distribution map for AP with any combination of QRS types in several leads. For the validation phase, a separate cohort of APs was obtained. RESULTS: A total of 800 patients with overt APs were studied. The application used the exploratory data set of 553 consecutive APs and the corresponding QRS complexes to generate AP localization maps for any possible combination of QRS types in 12 leads. Optimized approach (on average 3 steps) for evaluation of preexcited electrcardiogram was developed. The area of maximum probability of AP localization was pinpointed by providing the QRS type for the subsequent leads. The exploratory data set was validated with the separate cohort of APs (n = 256); p = .23 for difference in AP distribution. CONCLUSIONS: In the largest data set of APs to-date, a novel probabilistic and semi-automatic approach to electrocardiographic localization of APs was highly predictive for anatomic localization.


Assuntos
Feixe Acessório Atrioventricular , Potenciais de Ação , Técnicas Eletrofisiológicas Cardíacas , Frequência Cardíaca , Aplicativos Móveis , Valor Preditivo dos Testes , Humanos , Feixe Acessório Atrioventricular/fisiopatologia , Reprodutibilidade dos Testes , Masculino , Feminino , Processamento de Sinais Assistido por Computador , Eletrocardiografia , Adulto , Algoritmos , Fatores de Tempo , Pessoa de Meia-Idade , Adulto Jovem
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