Model for predicting long-term sinus rhythm retention after electrical cardioversion in patients with persistent atrial fibrillation.

K. O. Pysarevska, O. I. Zharinov

Abstract


The aim - to create a model for predicting long-term sinus rhythm retention after electrical cardioversion (ECV) based on individual patients characteristics. 141 patients with persistent AF who were planning to have sinus rhythm restoration, underwent general clinical examination, ECG and transthoracic echocardiographic. In 6 months after ECV patients were divided into two groups: 83 patients maintained sinus rhythm for 6 months (group I), recurrence of AF was observed in 58 patients (group II). The results of the ROC analysis determined statistically significant parameters that contribute to long-term retention of the sinus rhythm: non-modifiable (age, history and duration of the last episode of arrhythmia, combination of background diseases), modifiable (smoking, antiarrhythmic therapy, severity of heart failure by NYHA). A multifactorial prognostic model is created with the help of the modern approach of creating scorecards. This allowed in the form of a numerical characteristic to determine the probability of a long retention of the sinus rhythm. The selection of factors for the scoring model was in accordance with the criteria of the power of their influence on the retention of the sinus rhythm. The scoring model was created for predicting long-term retention of sinus rhythm after electrical cardioversion based on the individual characteristics of a patient with a persistent form of AF. The model has a high sensitivity (92.77%) and specificity (70.69%) and allows to predict the further course of the disease and to correct the therapy on time.


Keywords


persistent atrial fibrillation; electrical cardioversion; prognosis

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References


Anokhina IYu. [Development of a scoring model using the methods of logistic regression and ROC analysis]. Informatika i kibernetika. 2016;3(5):13-21. Russian.

Bakun SA, Bіdyuk PІ. [Method of constructing scoring cards using the SAS platform]. Naukovі vіstі NTUU «KPІ». 2016;2:23-32. Russian.

Mil'chakov KS. [Scoring cards in medicine: review and analysis of publications]. Vrach i informa­tsionnye tekhnologii. 2015;1:71-79. Russian.

Sychev OS, Romanova EN, Sribnaya OV. [Usage of the metabolic therapy in patients with atrial fibril­lation]. Ukraїns'kiy kardіologіchniy zhurnal. 2015;2:65-70. Russian.

Yaroslavskaya EI. [Features of coronary athe­ro­sclerosis and nonconventional cardiac morphofunctional syndromes in ischemic heart disease: dissertation].Tyumen'; 2016. Russian.

Kirchhof P, Benussi S, Kotecha D, Ahlsson A, Atar D, Casadei B, et al. 2016 ESC Guidelines for the management of atrial fibrillation developed in colla­boration with EACTS. European Heart Journal. 2016;27. doi: 10.1093/eurheartj/ehw210.

Marzona I, O’Donnell M, Teo K, Gao P, An­derson C, Bosch J, et al. Increased risk of cognitive and functional decline in patients with atrial fibrillation: results of the ONTARGET and TRANSCEND studies. CMAJ. 2012;184:E329-36.

Šimundić AM. Measures of Diagnostic Accuracy: Basic Definitions. EJIFCC. 2009;19(4):203-11.

STATISTICA Formula Guide. STATISTICA Scorecard [StatSoft Inc.]. 2013;32. Available from: http://documentation.statsoft.com/portals/0/formula%20guide/STATISTICA%20Scorecard%20Formula%20Guide.pdf.

Chugh SS, Havmoeller R, Narayanan K, et al. Worldwide epidemiology of atrial fibrillation: a global burden of disease 2010 study. Circulation. 2014;129:837-47.


GOST Style Citations


1.       Анохина И.Ю. Разработка скоринговой моде­ли с использованием методов логистической регрес­сии и ROC – анализа / И. Ю. Анохина // Информатика и кибернетика. – 2016. – № 3(5). – С. 13 –21

2.       Бакун С.А. Методика побудови скорингових карт із використанням платформи SAS / С.А. Бакун, П.І. Бідюк // Наукові вісті НТУУ «КПІ». – 2016. – № 2. – С. 23 – 32.

3.       Мильчаков К.С. Скоринговые карты в меди­цине: обзор и анализ публикаций / К.С. Мильчаков, М.П. Шебалков // Врач и информационные техно­логии. – 2015. – № 1. – С. 71 – 79.

4.       Сычев О.С. Использование метаболической тера­пии у больных c фибрилляцией предсердий / О.С. Сычев, Е.Н. Романова, О.В. Срибная // Укр. кардіол. журнал. – 2015. – № 2. – С. 65-70.

5.       Ярославская Е.И. Особенности коронарного атеросклероза и неконвенционные кардиальные мор­фофункциональные синдромы при ишемической бо­лезни сердца: автореф. дис. на соискание учен. сте­пени д-ра мед. наук: спец. 14.01.05 «Кардиология» / Ярославская Елена Ильинична – Тюмень, 2016. –50 с.

6.       2016 ESC Guidelines for the management of atrial fibrillation developed in collaboration with EACTS / P. Kirchhof, S. Benussi, D. Kotecha [et al.] // Eur. Heart J. – 2016. – Vol. 27. – doi: 10.1093/eurheartj/ehw210.

7.       Increased risk of cognitive and functional decline in patients with atrial fibrillation: results of the ONTARGET and TRANSCEND studies / I. Marzona, M. O’Donnell, K. Teo [et al.] // CMAJ. – 2012. – Vol. 184. – P. 329-336.

8.       Šimundić A-M. Measures of Diagnostic Ac­curacy: Basic Definitions / A-M. Šimundić // EJIFCC. – 2009. – Vol. 19, N 4. – Р. 203-211.

9.       STATISTICA Formula Guide. STATISTICA Sco­recard / [StatSoft Inc.]. – 2013, 32 р. – [Електронний ресурс]. – Режим доступу: http://documentation.stat­soft.com/portals/0/formula%20guide/STATISTICA%20Scorecard%20Formula%20Guide.pdf.

10.    Worldwide epidemiology of atrial fibrillation: a global burden of disease 2010 study / S.S. Chugh, R. Havmoeller, K. Narayanan [et al.] // Circulation – 2014. – Vol. 129. – P. 837-847.





DOI: https://doi.org/10.26641/2307-0404.2017.4.117666

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