Digital Electrocardiographic Complex for Risk Stratification of Paroxysmal Atrial Fibrillation
The aim of the study was to develop and clinically test a hardware and software system capable of identifying the predictors of the hidden forms of atrial fibrillation (AF) using 12-lead ECG data in sinus rhythm.
Materials and Methods. There was developed the hardware and software system “Intecard 8.1” to assess a set of markers for atrial electrical instability by 3–5-minute ECG recordings in sinus rhythm. The markers include P-wave amplitude in lead II <0.1 mV, P-wave duration >120 ms, advanced interatrial block, the area of the biphasic P-wave terminal part <–4 mV·ms, and MVP (morphology–voltage–
P-wave duration) score >3 points.
The clinical testing of “Intecard 8.1” system was carried out on 120 patients with ischemic heart disease or dilated cardiomyopathy. The patients’ average age was 57.9±13.1 years.
Results. P-wave detection is a challenging task due to a low signal amplitude, noise, high error probability in atrioventricular block or T-wave and P-wave superposition in case of marked tachycardia. To improve detection, a phase transformation method was used, according to which there was studied its phase component arctg[x(n)/Rv], where x(n) — ECG signal samples, Rv — a constant. We developed an identification algorithm implemented in “Intecard 8.1” software, its clinical trials being conducted.
During the 12 [6; 22] month observation period, AF episodes were recorded in 22 from 120 patients (18.3%). The patients with AF episodes exhibited a significant decrease in P-wave amplitude (p=0.029), its duration increase (p<0.001), and a significantly high MVP score (p<0.01). The MVP score with a cut-off point >3 points is of the highest prognostic significance. The area under the ROC curve AUC was 0.988 with a 95% confidence interval: 0.975–0.999 (p<0.001). The prediction model of hidden AF paroxysms has sensitivity and specificity: 92 and 89%, respectively.
Conclusion. The digital electrocardiographic complex “Intecard 8.1” when analyzing 3–5-minute ECG recordings with sinus rhythm enables to identify the patients with high risk or with hidden AF forms. The dynamic assessment of P-wave parameters offers an opportunity to personalize heart rhythm control in this patient cohort.
- Rahman F., Kwan G.F., Benjamin E.J. Global epidemiology of atrial fibrillation. Nat Rev Cardiol 2014; 11(11): 639–654, https://doi.org/10.1038/nrcardio.2014.118.
- Wolf P.A., Dawber T.R., Thomas H.E. Jr., Kannel W.B. Epidemiologic assessment of chronic atrial fibrillation and risk of stroke: the Framingham study. Neurology 1978; 28(10): 973–977, https://doi.org/10.1212/wnl.28.10.973.
- Camm A.J., Corbucci G., Padeletti L. Usefulness of continuous electrocardiographic monitoring for atrial fibrillation. Am J Cardiol 2012; 110(2): 270–276, https://doi.org/10.1016/j.amjcard.2012.03.021.
- Kirchhof P., Benussi S., Kotecha D., Ahlsson A., Atar D., Casadei B., Castella M., Diener H.C., Heidbuchel H., Hendriks J., Hindricks G., Manolis A.S., Oldgren J., Popescu B.A., Schotten U., Van Putte B., Vardas P.; ESC Scientific Document Group. 2016 ESC Guidelines for the management of atrial fibrillation developed in collaboration with EACTS. Eur Heart J 2016; 37(38): 2893–2962, https://doi.org/10.1093/eurheartj/ehw210.
- Antzelevitch C., Dumaine R. Electrical heterogeneity in the heart: physiological, pharmacological and clinical implications. In: Comprehensive physiology. Supplement 6. Handbook of physiology, the cardiovascular system, the heart. American Physiological Society; 2011; p. 654–692, https://doi.org/10.1002/cphy.cp020117.
- Verrier R.L., Nearing B.D., D’Avila A. Spectrum of clinical applications of interlead ECG heterogeneity assessment: from myocardial ischemia detection to sudden cardiac death risk stratification. Ann Noninvasive Electrocardiol 2021; 26(6): e12894, https://doi.org/10.1111/anec.12894.
- Alexander B., Milden J., Hazim B., Haseeb S., Bayes-Genis A., Elosua R., Martínez-Sellés M., Yeung C., Hopman W., Bayes de Luna A., Baranchuk A. New electrocardiographic score for the prediction of atrial fibrillation: the MVP ECG risk score (morphology-voltage-P-wave duration). Ann Noninvasive Electrocardiol 2019; 24(6): e12669, https://doi.org/10.1111/anec.12669.
- Murase Y., Imai H., Ogawa Y., Kano N., Mamiya K., Ikeda T., Okabe K., Arai K., Yamazoe S., Torii J., Kawaguchi K. Usefulness of P-wave duration in patients with sick sinus syndrome as a predictor of atrial fibrillation. J Arrhythm 2021; 37(5): 1220–1226, https://doi.org/10.1002/joa3.12604.
- Intzes S., Zagoridis K., Symeonidou M., Spanoudakis E., Arya A., Dinov B., Dagres N., Hindricks G., Bollmann A., Kanoupakis E., Koutalas E., Nedios S. P-wave duration and atrial fibrillation recurrence after catheter ablation: a systematic review and meta-analysis. Europace 2023; 25(2): 450–459, https://doi.org/10.1093/europace/euac210.
- Vorobiev A.P., Vaykhanskaya T.G., Melnikova O.P., Krupenin V.P., Polyakov V.B., Frolov A.V. A digital electrocardiographic system for assessing myocardial electrical instability: principles and applications. Sovremennye tehnologii v medicine 2020; 12(6): 15, https://doi.org/10.17691/stm2020.12.6.02.
- Nielsen J.B., Kühl J.T., Pietersen A., Graff C., Lind B., Struijk J.J., Olesen M.S., Sinner M.F., Bachmann T.N., Haunsø S., Nordestgaard B.G., Ellinor P.T., Svendsen J.H., Kofoed K.F., Køber L., Holst A.G. P-wave duration and the risk of atrial fibrillation: results from the Copenhagen ECG Study. Heart Rhythm 2015; 12(9): 1887–1895, https://doi.org/10.1016/j.hrthm.2015.04.026.
- Bayés-de-Luna A., Fiol-Sala M., Martínez-Sellés M., Baranchuk A. Current ECG aspects of interatrial block. Hearts 2021; 2(3): 419–432, https://doi.org/10.3390/hearts2030033.
- Vaikhanskaya T.G., Frolov A.V. The new clinical Bayes syndrome: definitions, epidemiology and clinical significance. Cardiology in Belarus 2022; 14(6): 803–813, https://doi.org/10.34883/pi.2022.14.6.009.
- Huang Z., Zheng Z., Wu B., Tang L., Xie X., Dong R., Luo Y., Li S., Zhu J., Liu J. Predictive value of P wave terminal force in lead V1 for atrial fibrillation: a meta-analysis. Ann Noninvasive Electrocardiol 2020; 25(4): e12739, https://doi.org/10.1111/anec.12739.
- Yang N., Yan N., Cong G., Yang Z., Wang M., Jia S. Usefulness of morphology-voltage-P-wave duration (MVP) score as a predictor of atrial fibrillation recurrence after pulmonary vein isolation. Ann Noninvasive Electrocardiol 2020; 25(6): e12773, https://doi.org/10.1111/anec.12773.
- Martínez A., Alcaraz R., Rieta J.J. Application of the phasor transform for automatic delineation of single-lead ECG fiducial points. Physiol Meas 2010; 31(11): 1467–1485, https://doi.org/10.1088/0967-3334/31/11/005.
- Saclova L., Nemcova A., Smisek R., Smital L., Vitek M., Ronzhina M. Reliable P wave detection in pathological ECG signals. Sci Rep 2022; 12(1): 6589, https://doi.org/10.1038/s41598-022-10656-4.
- Mulder M.J., Kemme M.J.B., Hopman L.H.G.A., Kuşgözoğlu E., Gülçiçek H., van de Ven P.M., Hauer H.A., Tahapary G.J.M., Götte M.J.W., van Rossum A.C., Allaart C.P. Comparison of the predictive value of ten risk scores for outcomes of atrial fibrillation patients undergoing radiofrequency pulmonary vein isolation. Int J Cardiol 2021; 344: 103–110, https://doi.org/10.1016/j.ijcard.2021.09.029.
- Sikorska A., Pilichowska-Paszkiet E., Zuk A., Piotrowski R., Kryński T., Baran J., Zaborska B., Kułakowski P. Acceleration of sinus rhythm following ablation for atrial fibrillation: a simple parameter predicting ablation efficacy. Kardiol Pol 2019; 77(10): 960–965, https://doi.org/10.33963/KP.14950.
- Kreimer F., Aweimer A., Pflaumbaum A., Mügge A., Gotzmann M. Impact of P-wave indices in prediction of atrial fibrillation-Insight from loop recorder analysis. Ann Noninvasive Electrocardiol 2021; 26(5): e12854, https://doi.org/10.1111/anec.12854.
- Attia Z.I., Noseworthy P.A., Lopez-Jimenez F., Asirvatham S.J., Deshmukh A.J., Gersh B.J., Carter R.E., Yao X., Rabinstein A.A., Erickson B.J., Kapa S., Friedman P.A. An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction. Lancet 2019; 394(10201): 861–867, https://doi.org/10.1016/S0140-6736(19)31721-0.