This study sought to develop and validate diagnostic models to identify individuals with atrial fibrillation (AF) using amplified sinus-p-wave analysis.
A total of 1,492 patients (491 healthy controls, 499 with paroxysmal AF and 502 with persistent AF) underwent digital 12-lead-ECG recording during sinus rhythm. The patient cohort was divided into training and validation set in a 3:2 ratio. P-wave indices (PWI) including duration of standard p-wave (standard PWD; scale at 10 mm/mV, sweep speed at 25 mm/s) and amplified sinus-p-wave (APWD, scale at 60–120 mm/mV, sweep speed at 100 mm/s) and advanced inter-atrial block (aIAB) along with other clinical parameters were used to develop diagnostic models using logistic regression. Each model was developed from the training set and further tested in both training and validation sets for its diagnostic performance in identifying individuals with AF.
Compared to standard PWD (Reference model), which achieved an AUC of 0.637 and 0.632, for training and validation set, respectively, APWD (Basic model) importantly improved the accuracy to identify individuals with AF (AUC = 0.86 and 0.866). The PWI-based model combining APWD, aIAB and body surface area (BSA) further improved the diagnostic performance for AF (AUC = 0.892 and 0.885). The integrated model, which further combined left atrial diameter (LAD) with parameters of the PWI-based model, achieved optimal diagnostic performance (AUC = 0.916 and 0.902).
Analysis of amplified p-wave during sinus rhythm allows identification of individuals with atrial fibrillation.