Autors: Prodanova, K. S., Negreva M.
Title: Coagulation Parameters as Predictors of Paroxysmal Atrial Fibrillation: Data Modeling by Logistic Regression and ROC Analysis
Keywords:

Abstract: Our research on patients with paroxysmal atrial fibrillation (PAF) found significant deviations in fourteen coagulation indicators already occurring in the first twenty-four hours of the clinical manifestation of the disease, namely: significantly increased plasma activity of coagulation factors FII, FV, FVII, FVIII, FIX, FX, FXI, FXII and plasma activity of von Willebrand factor, as well as significantly increased plasma levels of tissue factor, FVIII, vWF, fibrinopeptide A and prothrombin fragment F1+2. The early nature of the deviations raises an important question, namely, to what extent are they a consequence of atrial fibrillation or are they closely related to the clinical manifestation of the arrhythmia and precede it. In search of their predictive value for PAF occurrence, the performed logistic regression analysis and ROC analysis showed that, of the investigated indicators, plasma FVIII activity gives the best diagnostic opportunity to identify the occurrence of the disease (AUC=0.85, Acc=0.85, Se 99 %, Sp 69%, with over 70% correctly classified cases). Our results are important for clinical practice and are a good prerequisite for further prospective clinical studies. Establishment of significant biomarkers predicting PAF appearance will enable detection of patients at increased risk of thromboembolic events, prevention optimization and reduction of the frequency of thromboembolic events associated with the rhythm disorder.

References

  1. A. Arauz at al, Neurologia 37, 362-370 (2019).
  2. E. J. Benjamin at al, Circulation 139, e56-e528 (2019).
  3. D. Ellis at al, Medicine 97, e13830 (2018).
  4. W. Alonso at al, Int J Cardiol 155, 217-222 (2016).
  5. R. B. Schnabel at al, Circulation 121, 200-207 (2010).
  6. H. M. Spronk at al, Eur Heart J 38, 38-50 (2017).
  7. M. Negreva at al, Minerva. Cardiol. Angiol. 69, 269-276 (2021).
  8. M. Negreva at al, Cardiol. Res. 11, 22-32 (2020).
  9. M. Negreva at al, J. Atr. Fibrillation 13, 2297 (2020).
  10. M. Negreva at al, Arch. Med. Sci. Atheroscler. Dis. 5, e140-e147 (2020)..
  11. M. Negreva at al, Medicine (Baltimore) 95, e5184 (2016).
  12. D. W. Hosmer at al, Applied Logistic Regression, John Wiley & Sons, (2013).
  13. K. Hajian-Tilaki, Caspian Journal of Internal Medicine 4, 627-635 (2013).
  14. E. J. Benjamin at al,, JAMA 271, 840-844 (1994).
  15. A. D. Krahn at al, Am. J. Med. 98, 476-484 (1995).
  16. P. A. Wolf at al, Am. Heart. J. 131, 790-795 (1996).
  17. X. Chen at al, Clin. Cardiol. 41, 1507-1512 (2018).
  18. C. Kwong at al, Cardiology 138, 133-140 (2018).
  19. J. Mesquita at al, Europace 20, f428-f435 (2018).

Issue

AIP Conference Proceedings, vol. 3182, pp. 110001, 2025, United States, https://doi.org/10.1063/5.0246073

Full text of the publication

Вид: публикация в международен форум, публикация в издание с импакт фактор, публикация в реферирано издание, индексирана в Scopus