| Autor: |
Tachmatzidis D; 3rd Cardiology Department, Hippokrateion General Hospital, Aristotle University of Thessaloniki, 546 42 Thessaloniki, Greece., Filos D; Lab of Computing, Medical Informatics and Biomedical Imaging Technologies, School of Medicine, Aristotle University of Thessaloniki, 541 24 Thessaloniki, Greece., Chouvarda I; Lab of Computing, Medical Informatics and Biomedical Imaging Technologies, School of Medicine, Aristotle University of Thessaloniki, 541 24 Thessaloniki, Greece., Tsarouchas A; 3rd Cardiology Department, Hippokrateion General Hospital, Aristotle University of Thessaloniki, 546 42 Thessaloniki, Greece., Mouselimis D; 3rd Cardiology Department, Hippokrateion General Hospital, Aristotle University of Thessaloniki, 546 42 Thessaloniki, Greece., Bakogiannis C; 3rd Cardiology Department, Hippokrateion General Hospital, Aristotle University of Thessaloniki, 546 42 Thessaloniki, Greece., Lazaridis C; 3rd Cardiology Department, Hippokrateion General Hospital, Aristotle University of Thessaloniki, 546 42 Thessaloniki, Greece., Triantafyllou K; 3rd Cardiology Department, Hippokrateion General Hospital, Aristotle University of Thessaloniki, 546 42 Thessaloniki, Greece., Antoniadis AP; 3rd Cardiology Department, Hippokrateion General Hospital, Aristotle University of Thessaloniki, 546 42 Thessaloniki, Greece., Fragakis N; 3rd Cardiology Department, Hippokrateion General Hospital, Aristotle University of Thessaloniki, 546 42 Thessaloniki, Greece., Efthimiadis G; 1st Cardiology Department, AHEPA University Hospital, Aristotle University of Thessaloniki, 546 21 Thessaloniki, Greece., Maglaveras N; Lab of Computing, Medical Informatics and Biomedical Imaging Technologies, School of Medicine, Aristotle University of Thessaloniki, 541 24 Thessaloniki, Greece., Tsalikakis DG; Department of Informatics and Telecommunications Engineering, University of Western Macedonia, 501 00 Kozani, Greece., Vassilikos VP; 3rd Cardiology Department, Hippokrateion General Hospital, Aristotle University of Thessaloniki, 546 42 Thessaloniki, Greece. |
| Abstrakt: |
Early identification of patients at risk for paroxysmal atrial fibrillation (PAF) is essential to attain optimal treatment and a favorable prognosis. We compared the performance of a beat-to-beat (B2B) P-wave analysis with that of standard P-wave indices (SPWIs) in identifying patients prone to PAF. To this end, 12-lead ECG and 10 min vectorcardiogram (VCG) recordings were obtained from 33 consecutive, antiarrhythmic therapy naïve patients, with a short history of low burden PAF, and from 56 age- and sex-matched individuals with no AF history. For both groups, SPWIs were calculated, while the VCG recordings were analyzed on a B2B basis, and the P-waves were classified to a primary or secondary morphology. Wavelet transform was used to further analyze P-wave signals of main morphology. Univariate analysis revealed that none of the SPWIs performed acceptably in PAF detection, while five B2B features reached an AUC above 0.7. Moreover, multivariate logistic regression analysis was used to develop two classifiers-one based on B2B analysis derived features and one using only SPWIs. The B2B classifier was found to be superior to SPWIs classifier; B2B AUC: 0.849 (0.754-0.917) vs. SPWIs AUC: 0.721 (0.613-0.813), p value: 0.041. Therefore, in the studied population, the proposed B2B P-wave analysis outperforms SPWIs in detecting patients with PAF while in sinus rhythm. This can be used in further clinical trials regarding the prognosis of such patients. |
