Skip to main content
SpringerLink
Log in

Atrial conduction time associated predictors of recurrent atrial fibrillation

  • Original Paper
  • Published:
The International Journal of Cardiovascular Imaging Aims and scope Submit manuscript

Abstract

Identifying patients at high risk of atrial fibrillation (AF) recurrence remains challenging. This study aimed to evaluate total atrial conduction time (TACT) and left atrial (LA) asynchrony as predictors of AF recurrence. Consecutive patients after the first AF episode, terminated either spontaneously or with cardioversion, underwent transthoracic echocardiography. TACT, estimated by the time delay between the onset of P-wave and the peak A′-wave on the Tissue Doppler Imaging (PA-TDI duration), atrial volumetric and functional parameters, and biatrial strain were assessed. We calculated mean PA-TDI—the average of PA-TDI measurements in all left atrial (LA) walls—and the difference between the longest and the shortest PA interval (DLS) and the standard deviation of 4 PA intervals (SD4) to assess the LA global remodeling and asynchrony, respectively. The primary endpoint was AF recurrence. Patients with recurrent AF had significantly prolonged PA-TDI intervals in each LA wall—and thus mean PA-TDI—than those without recurrence (mean PA-TDI: 157.4 ± 17.9 vs. 110.2 ± 7.7 ms, p < 0.001). At univariate analysis, LA maximum volume index, total LA emptying fraction, right atrial maximum volume index, PA-TDI, DLS, and SD4 were predictors of AF recurrence. At multivariable analysis, PA-TDI intervals in all LA walls remained strong predictors with mean PA-TDI (odds ratio 1.04; 95% confidence interval 1.03–1.06) having an optimal cutoff of 125.8 ms in receiver operator characteristics curve analysis providing 98% sensitivity and 100% specificity for AF recurrence (area under the curve = 0.989). PA-TDI was an independent predictor of AF recurrence and outperformed established echocardiographic parameters.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Code availability

R code available upon request.

Data availability

Data available upon request

References

  1. Conen D (2018) Epidemiology of atrial fibrillation. Eur Heart J 39(16):1323–1324. https://doi.org/10.1093/eurheartj/ehy171

    Article  PubMed  Google Scholar 

  2. Kirchhof P, Benussi S, Kotecha D et al (2016) 2016 ESC Guidelines for the management of atrial fibrillation developed in collaboration with EACTS. Eur Heart J 37(38):2893–2962. https://doi.org/10.1093/eurheartj/ehw210

    Article  Google Scholar 

  3. Haim M, Hoshen M, Reges O, Rabi Y, Balicer R, Leibowitz M (2015) Prospective national study of the prevalence, incidence, management and outcome of a large contemporary cohort of patients with incident non-valvular atrial fibrillation. J Am Heart Assoc. 4(1):1–12. https://doi.org/10.1161/JAHA.114.001486

    Article  Google Scholar 

  4. Wolf PA, Abbott RD, Kannel WB (1991) Atrial fibrillation as an independent risk factor for stroke: the framingham study. Stroke. https://doi.org/10.1161/01.STR.22.8.983

    Article  PubMed  Google Scholar 

  5. White H, Boden-Albala B, Wang C et al (2005) Ischemic stroke subtype incidence among whites, blacks, and Hispanics: the northern Manhattan study. Circulation 111(10):1327–1331. https://doi.org/10.1161/01.CIR.0000157736.19739.D0

    Article  PubMed  Google Scholar 

  6. Ganesan AN, Shipp NJ, Brooks AG et al (2013) Long-term outcomes of catheter ablation of atrial fibrillation: a systematic review and meta-analysis. J Am Heart Assoc. 2(2):e004549. https://doi.org/10.1161/JAHA.112.004549

    Article  PubMed  PubMed Central  Google Scholar 

  7. Hirose T, Kawasaki M, Tanaka R et al (2012) Left atrial function assessed by speckle tracking echocardiography as a predictor of new-onset non-valvular atrial fibrillation: results from a prospective study in 580 adults. Eur Heart J Cardiovasc Imaging. 13(3):243–250. https://doi.org/10.1093/ejechocard/jer251

    Article  PubMed  Google Scholar 

  8. Blume GG, McLeod CJ, Barnes ME et al (2011) Left atrial function: physiology, assessment, and clinical implications. Eur J Echocardiogr 12(6):421–430. https://doi.org/10.1093/ejechocard/jeq175

    Article  PubMed  Google Scholar 

  9. Abecasis J, Dourado R, Ferreira A et al (2009) Left atrial volume calculated by multi-detector computed tomography may predict successful pulmonary vein isolation in catheter ablation of atrial fibrillation. Europace 11(10):1289–1294. https://doi.org/10.1093/europace/eup198

    Article  PubMed  Google Scholar 

  10. Weijs B, De Vos CB, Tieleman RG et al (2011) Clinical and echocardiographic correlates of intra-atrial conduction delay. Europace 13(12):1681–1687. https://doi.org/10.1093/europace/eur261

    Article  PubMed  Google Scholar 

  11. Merckx KL, De Vos CB, Palmans A et al (2005) Atrial activation time determined by transthoracic doppler tissue imaging can be used as an estimate of the total duration of atrial electrical activation. J Am Soc Echocardiogr 18(9):940–944. https://doi.org/10.1016/j.echo.2005.03.022

    Article  PubMed  Google Scholar 

  12. Erdem FH, Erdem A, Özlü F et al (2016) Electrophysiological validation of total atrial conduction time measurement by tissue doppler echocardiography according to age and sex in healthy adults. J Arrhythmia 32(2):127–132. https://doi.org/10.1016/j.joa.2015.11.006

    Article  Google Scholar 

  13. De Vos CB, Weijs B, Crijns HJGM et al (2009) Atrial tissue Doppler imaging for prediction of new-onset atrial fibrillation. Heart 95(10):835–840. https://doi.org/10.1136/hrt.2008.148528

    Article  PubMed  Google Scholar 

  14. Löbe S, Knopp H, Le TV et al (2018) Left atrial asynchrony measured by pulsed-wave tissue doppler is associated with abnormal atrial voltage and recurrences of atrial fibrillation after catheter ablation. JACC Clin Electrophysiol 4(12):1640–1641. https://doi.org/10.1016/j.jacep.2018.08.017

    Article  PubMed  Google Scholar 

  15. Den Uijl DW, Gawrysiak M, Tops LF et al (2011) Prognostic value of total atrial conduction time estimated with tissue Doppler imaging to predict the recurrence of atrial fibrillation after radiofrequency catheter ablation. Europace 13(11):1533–1540. https://doi.org/10.1093/europace/eur186

    Article  Google Scholar 

  16. Dinov B, Knopp H, Löbe S et al (2016) Patterns of left atrial activation and evaluation of atrial dyssynchrony in patients with atrial fibrillation and normal controls: factors beyond the left atrial dimensions. Hear Rhythm. 13(9):1829–1836. https://doi.org/10.1016/j.hrthm.2016.06.003

    Article  Google Scholar 

  17. Dilaveris PE, Gialafos EJ, Sideris SK et al (1998) Simple electrocardiographic markers for the prediction of paroxysmal idiopathic atrial fibrillation. Am Heart J 135(5):733–738. https://doi.org/10.1016/s0002-8703(98)70030-4

    Article  CAS  PubMed  Google Scholar 

  18. Hatam N, Aljalloud A, Mischke K et al (2014) Interatrial conduction disturbance in postoperative atrial fibrillation: a comparative study of P-wave dispersion and Doppler myocardial imaging in cardiac surgery. J Cardiothorac Surg. 9(1):114. https://doi.org/10.1186/1749-8090-9-114

    Article  PubMed  PubMed Central  Google Scholar 

  19. Abou R, Leung M, Tonsbeek AM et al (2017) Effect of aging on left atrial compliance and electromechanical properties in subjects without structural heart disease. Am J Cardiol 120(1):140–147. https://doi.org/10.1016/j.amjcard.2017.03.243

    Article  PubMed  Google Scholar 

  20. Kuppahally SS, Akoum N, Burgon NS et al (2010) Left atrial strain and strain rate in patients with paroxysmal and persistent atrial fibrillation: relationship to left atrial structural remodeling detected by delayed-enhancement MRI. Circ Cardiovasc Imaging 3(3):231–239. https://doi.org/10.1161/CIRCIMAGING.109.865683

    Article  PubMed  Google Scholar 

  21. Maffè S, Paffoni P, Dellavesa P et al (2015) Prognostic value of total atrial conduction time measured with tissue doppler imaging to predict the maintenance of sinus rhythm after external electrical cardioversion of persistent atrial fibrillation. Echocardiography. 32(3):420–427. https://doi.org/10.1111/echo.12702

    Article  PubMed  Google Scholar 

  22. Müller P, Schiedat F, Bialek A et al (2014) Total atrial conduction time assessed by tissue doppler imaging (PA-TDI interval) to predict early recurrence of persistent atrial fibrillation after successful electrical cardioversion. J Cardiovasc Electrophysiol. https://doi.org/10.1111/jce.12306

    Article  PubMed  Google Scholar 

  23. Chen MS, Marrouche NF, Khaykin Y et al (2004) Pulmonary vein isolation for the treatment of atrial fibrillation in patients with impaired systolic function. J Am Coll Cardiol 43(6):1004–1009. https://doi.org/10.1016/j.jacc.2003.09.056

    Article  PubMed  Google Scholar 

  24. Berruezo A, Tamborero D, Mont L et al (2007) Pre-procedural predictors of atrial fibrillation recurrence after circumferential pulmonary vein ablation. Eur Heart J 28(7):836–841. https://doi.org/10.1093/eurheartj/ehm027

    Article  PubMed  Google Scholar 

  25. Schneider C, Malisius R, Krause K et al (2008) Strain rate imaging for functional quantification of the left atrium: atrial deformation predicts the maintenance of sinus rhythm after catheter ablation of atrial fibrillation. Eur Heart J 29(11):1397–1409. https://doi.org/10.1093/eurheartj/ehn168

    Article  PubMed  Google Scholar 

  26. Vasamreddy CR, Lickfett L, Jayam VK et al (2004) Predictors of recurrence following catheter ablation of atrial fibrillation using an irrigated-tip ablation catheter. J Cardiovasc Electrophysiol. https://doi.org/10.1046/j.1540-8167.2004.03538.x

    Article  PubMed  Google Scholar 

  27. Mouselimis D, Tsarouchas AS, Pagourelias ED et al (2020) Left atrial strain, intervendor variability, and atrial fibrillation recurrence after catheter ablation: a systematic review and meta-analysis. Hell J Cardiol. https://doi.org/10.1016/j.hjc.2020.04.008

    Article  Google Scholar 

  28. Antoni ML, Bertini M, Atary JZ et al (2010) Predictive value of total atrial conduction time estimated with tissue doppler imaging for the development of new-onset atrial fibrillation after acute myocardial infarction. Am J Cardiol 106(2):198–203. https://doi.org/10.1016/j.amjcard.2010.02.030

    Article  PubMed  Google Scholar 

  29. Müller P, Ivanov V, Kara K et al (2017) Total atrial conduction time to predict occult atrial fibrillation after cryptogenic stroke. Clin Res Cardiol 106(2):113–119. https://doi.org/10.1007/s00392-016-1029-2

    Article  PubMed  Google Scholar 

  30. Ntaios G (2020) Embolic stroke of undetermined source. J Am Coll Cardiol 75(3):333. https://doi.org/10.1016/j.jacc.2019.11.024

    Article  CAS  PubMed  Google Scholar 

  31. Kishore A, Vail A, Majid A et al (2014) Detection of atrial fibrillation after ischemic stroke or transient ischemic attack: a systematic review and meta-analysis. Stroke 45(2):520–526. https://doi.org/10.1161/STROKEAHA.113.003433

    Article  CAS  PubMed  Google Scholar 

  32. Lang RM, Badano LP, Mor-Avi V et al (2015) Recommendations for cardiac chamber quantification by echocardiography in adults: an update from the American society of echocardiography and the European association of cardiovascular imaging. Eur Heart J Cardiovasc Imaging. 16(3):233–271. https://doi.org/10.1093/ehjci/jev014

    Article  PubMed  Google Scholar 

Download references

Funding

None.

Author information

Author notes

  1. Iosif Karantoumanis, Ioannis Doundoulakis, and Stefanos Zafeiropoulos have equal authorship.

Authors and Affiliations

  1. First Department of Cardiology, AHEPA Hospital, Aristotle University of Thessaloniki, St. Kyriakidi str 1, 54636, Thessaloniki, Greece

    Iosif Karantoumanis, Ioannis Doundoulakis, Stefanos Zafeiropoulos, Kostas Oikonomou, Christodoulos Pliakos, Haralambos Karvounis & George Giannakoulas

  2. Department of Cardiology, Gen. Hospital Edessa, Pella, Greece

    Iosif Karantoumanis, Kostas Oikonomou & Pantelis Makridis

  3. Department of Cardiology, 424 General Military Training Hospital, Thessaloniki, Greece

    Ioannis Doundoulakis

  4. Elmezzi Graduate School of Molecular Medicine and Feinstein Institutes for Medical Research at Northwell Health, Manhasset, NY, USA

    Stefanos Zafeiropoulos

Authors
  1. Iosif Karantoumanis
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ioannis Doundoulakis
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Stefanos Zafeiropoulos
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kostas Oikonomou
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Pantelis Makridis
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Christodoulos Pliakos
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Haralambos Karvounis
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. George Giannakoulas
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to George Giannakoulas.

Ethics declarations

Conflict of interest

The authors declare that they have no competing interest.

Ethical approval

Approved by the Institutional Review Board of AHEPA Hospital and Aristotle University Ethics Committee and conducted according to the current version of the Declaration of Helsinki (2013).

Informed consent

All subjects gave their informed consent prior to their inclusion in the study. All the authors gave consent to submit for publication.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

All patients were imaged in left lateral decubitus position and the leads were placed in as possible identical positions. A 2D echocardiogram, including M-mode, Doppler (PW, CW and color) and TDI echocardiography were obtained in the parasternal short and long axis views.

LA volumes were obtained from the apical views by disc’s method and were indexed to body surface area (BSA) [32]. They were measured at three phases of cardiac cycle: 1. LA maximum volume at the end-systolic phase (just before mitral valve opening), 2. LA minimum volume at the end-diastolic phase (just before mitral valve closure) and 3. Volume before atrial active contraction, LA preA volume obtained from the last frame before mitral valve reopening or at time of the P wave on surface electrocardiography.

The LA function was assessed based on LA volumes by calculating the following equations: 1. Total atrial emptying fraction. 2. Active atrial emptying fraction: LA active ejection fraction, which is considered an index of LA active contraction. 3. Passive atrial emptying fraction, which is considered an index of LA conduit function; and 4. Atrial expansion index: LA expansion index, which is considered an index of LA reservoir function. Left ventricular ejection fraction (LVEF) was calculated from the standard apical 2- and 4-chamber views using the Simpson’s method.

Atrial Strain: The global and regional LA and RA strain was measured by 2D speckle tracking echocardiography (2DSTE). Recordings were processed with acoustic-tracking software (EchoPAC, GE Healthcare), allowing off line semi automated speckle tracking analysis. Along the LA and RA, the endocardium lines were manually traced. An additional epicardial line was generated automatically by the software, creating a region of interest (ROI). The ROI shape was manually adjusted, and the software divided the LA and RA region into six segments and generated the longitudinal strain curve. The zero strain point was set at the beginning of the P wave. Positive peak strain was measured during ventricular systole and negative peak strain during LA systole globally for each of the 12 segments. The total strain was the difference of positive peak strain and negative peak strain. Positive peak strain rate was measured during ventricular systole while early negative peak strain rate was measured during late diastole (see Tables 6, 7, 8, 9, 10, 11).

Table 6 Univariate and multivariable analysis of AF recurrence after cardioversion including PA-TDI (4Ch lateral) AF pattern and type of cardioversion
Full size table
Table 7 Univariate and multivariable analysis of AF recurrence after cardioversion including PA-TDI (3Ch inferolateral), AF pattern and type of cardioversion
Full size table
Table 8 Univariate and multivariable analysis of AF recurrence after cardioversion including PA-TDI (3Ch anterior), AF pattern and type of cardioversion
Full size table
Table 9 Univariate and multivariable analysis of AF recurrence after cardioversion including PA-TDI (2Ch anterior), AF pattern and type of cardioversion
Full size table
Table 10 Univariate and multivariable analysis of AF recurrence after cardioversion including mean PA-TDI, AF pattern and type of cardioversion
Full size table
Table 11 Patients with paroxysmal AF at baseline
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Karantoumanis, I., Doundoulakis, I., Zafeiropoulos, S. et al. Atrial conduction time associated predictors of recurrent atrial fibrillation. Int J Cardiovasc Imaging 37, 1267–1277 (2021). https://doi.org/10.1007/s10554-020-02113-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10554-020-02113-y

Keywords

Search

Navigation