| Authors |
Dyatlov Nikita Evgen’evich, assistant, Medical Institute, Penza State University (40 Krasnaya street, Penza, Russia), E-mail: sakedas@gmail.com
Mitrokhina Natal'ya Yur'evna, candidate of technical sciences, associate professor, sub-department of theoretical and applied mechanics and graphics, Penza State University (40 Krasnaya street, Penza, Russia), E-mail: natena1@yandex.ru
Rakhmatullov Fagim Kasymovich, doctor of medical sciences, professor, head of the department of internal diseases, Medical Institute, Penza State University (40 Krasnaya street, Penza, Russia), E-mail: pgu-vb2004@mail.ru
Mitrokhin Maksim Аleksandrovich, doctor of technical sciences, head of the department of computer engineering, Penza State University (40 Krasnaya street, Penza, Russia), E-mail: vt@pnzgu.ru
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| Abstract |
Subject and goals. The priority area of modern cardiology is to reduce the burden of atrial fibrillation (AF) in population. The existing AF stratification scales are aimed at assessing the incidence of stroke and bleeding, while the scale for assessing the progression of AF has not yet been developed. The purpose of the study was to identify predictors of AF development in pregnancy and to develop a method for predicting the number and duration of AF paroxysms during different gestational terms.
Materials and methods. To build mathematical models of the prediction of AF, a sample of daily Holter ECG monitoring (HM) records of 90 pregnant women with paroxysmal isolated AF recorded at each gestational age was used. Prediction was performed using correlation and regression analysis with distributed lag.
Results. Spearman's correlation analysis (SCA) and the neighboring components analysis (NCA) allowed us to select 4 specific intrinsic predictors of the development of AF in pregnancy from the general population in 30 signs: mean night-time heart rate (HRMN), total daily number of supraventricular and ventricular extrasystoles (TE), effective refractory period of the atrioventricular node (AVNRP) and atria (ATRP). Using the method of regression analysis, four predictive models of the course of AF during pregnancy were constructed using the specified predictors.
Conclusion. We have developed mathematical models for predicting the course of AF in pregnant women, which allow clinicians and scientists to guess the dynamics of the disease at different periods of gestation.
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