Prediction of Paroxysmal Atrial Fibrillation in Patients with Sinus Rhythm

Olga A. Germanova 1 * , Yulia Reshetnikova 2, Andrey Germanov 3, Giuseppe Galati 4, Inga Prokhorenko 3
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1 Department of clinical medicine of postgraduate education, Medical University Reaviz, Samara, Russia
2 Laboratory of cardiovisualization, Samara State Medical University, Samara, Russia
3 Department of internal diseases, Medical University Reaviz, Samara, Russia
4 Cardiovascular department, I.R.C.C.S. Ospedale Multimedica – Cardiovascular Scientific Institute, Milan, Italy
* Corresponding Author
J CLIN MED KAZ, Volume 23, Issue 3, pp. 24-30. https://doi.org/10.23950/jcmk/18532
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Author Contributions: Conceptualization, A. G.; methodology, O. G. and A. G.; validation, O. G. and Y. R.; formal analysis, G. G.; investigation, O. G. and Y. R.; resources, Y. R.; data curation, A. G.; writing – original draft preparation, O. G.; writing – review and editing, A. G. and G. G.; visualization, Y. R.; supervision, I. P.; project administration, O. G. and I. P.; funding acquisition, Y. R. All authors have read and agreed to the published version of the manuscript.

Data availability statement: The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Artificial Intelligence (AI) Disclosure Statement: The authors declare no AI Tools used for preparation of this work.

ABSTRACT

Objective: To create a tool for the prediction of paroxysmal atrial fibrillation (PAF) in patients with sinus rhythm.
Methods: Single-center, case-control study. None of the patients had a prior diagnosis of AF or reported symptoms of heart arrhythmias. Among the cohort of 6,630 individuals, paroxysms of AF were incidentally detected during 24-hours Holter ECG monitoring in 97 patients (main group). The control group - 99 patients from the same primary cohort without PAF. We assessed supraventricular and ventricular ectopic activity, the presence of pauses and blocks, changes of ST segment, QT interval durations, and heart rate variability.
Results: We formulated a regression equation to estimate the probability of PAF in patients with sinus rhythm. The most significant risk predictors: early "P on T" premature ectopic complexes (p<0.0001); coupled premature ventricular ectopic complexes (p=0.021); ventricular allorhythmias (OR=0.997). Other analyzed factors, including the frequency of both atrial and atrioventricular premature ectopic complexes, as well as single ventricular ectopic complexes, did not exhibit a statistically significant effect on the risk of PAF, according to this model.
Conclusion: The final regression equation, based on the evaluation of data from 24-hours Holter ECG monitoring, incorporates the following criteria: gender, the number of atrial and atrioventricular supraventricular complexes, the count of single and paired ventricular complexes, variations in rhythms with ventricular complexes, as well as the presence or absence of early "P on T" complexes (AUC=0.996).

CITATION

Germanova OA, Reshetnikova Y, Germanov A, Galati G, Prokhorenko I. Prediction of Paroxysmal Atrial Fibrillation in Patients with Sinus Rhythm. J CLIN MED KAZ. 2026;23(3):24-30. https://doi.org/10.23950/jcmk/18532

REFERENCES

  • Vyas R, Jain S, Thakre A, Thotamgari SR, Raina S, Brar V, Sengupta P, Agrawal P. Smart watch applications in atrial fibrillation detection: Current state and future directions. J Cardiovasc Electrophysiol. 2024;35(12):2474-2482. https://doi.org/10.1111/jce.16451.
  • Desteghe L, Heidbuchel H. Performance of handheld electrocardiogram devices to detect atrial fibrillation in a cardiology and geriatric ward setting: authors' response. Europace. 2017;19(8):1408-1409. https://doi.org/10.1093/europace/euw237.
  • Chiuariu T, Anghel L, Popa DM, Bîrgoan GS, Fechet ȘD, Zanfirescu RL, Balasanian MO, Sascău RA, Stătescu C. Predictors for Device-Detected Subclinical Atrial Fibrillation: An Up-to-Date Narrative Review. J Clin Med. 2026;15(2):578. https://doi.org/10.3390/jcm15020578.
  • Sau A, Sieliwonczyk E, Barker J, Zeidaabadi B, Pastika L, Patlatzoglou K, Khattak GR, McGurk KA, Peters NS, Kramer DB, Waks JW, Ware JS, Ng FS. Prediction of incident atrial fibrillation: A comprehensive evaluation of conventional and artificial intelligence-enhanced approaches. Heart Rhythm. 2026;23(2):e183-e191. https://doi.org/10.1016/j.hrthm.2025.08.024.
  • Manemann SM, Alonso A, Noseworthy PA, Siontis KC, Gersh BJ, Roger VL, Ryu E, Killian JM, Weston SA, Vaughan LE, Chamberlain AM. Multimorbidity in Atrial Fibrillation: Impact on Outcomes. J Am Heart Assoc. 2026;15(5):e040612. https://doi.org/10.1161/JAHA.124.040612.
  • Taha A, Martinsson A, Nielsen SJ, Rezk M, Pivodic A, Gudbjartsson T, Herrmann FEM, Bergfeldt LB, Jeppsson A. New-onset atrial fibrillation after coronary surgery and stroke risk: a nationwide cohort study. Heart. 2024;111(1):18-26. https://doi.org/10.1136/heartjnl-2024-324573.
  • Wu LD, Li F, Chen JY, Zhang J, Qian LL, Wang RX. Analysis of potential genetic biomarkers using machine learning methods and immune infiltration regulatory mechanisms underlying atrial fibrillation. BMC Med Genomics. 2022;15(1):64. https://doi.org/10.1186/s12920-022-01212-0.
  • Lu Z, Ntlapo N, Tilly MJ, Geurts S, Aribas E, Ikram MK, de Groot NMS, Kavousi M. Burden of cardiometabolic disorders and lifetime risk of new-onset atrial fibrillation among men and women: the Rotterdam Study. Eur J Prev Cardiol. 2024;31(9):1141-1149. https://doi.org/10.1093/eurjpc/zwae045.
  • Chung MK, Eckhardt LL, Chen LY, Ahmed HM, Gopinathannair R, Joglar JA, Noseworthy PA, Pack QR, Sanders P, Trulock KM; American Heart Association Electrocardiography and Arrhythmias Committee and Exercise, Cardiac Rehabilitation, and Secondary Prevention Committee of the Council on Clinical Cardiology; Council on Arteriosclerosis, Thrombosis and Vascular Biology; Council on Cardiovascular and Stroke Nursing; and Council on Lifestyle and Cardiometabolic Health. Lifestyle and Risk Factor Modification for Reduction of Atrial Fibrillation: A Scientific Statement From the American Heart Association. Circulation. 2020;141(16):e750-e772. https://doi.org/10.1161/CIR.0000000000000748.
  • Piano MR, Marcus GM, Aycock DM, Buckman J, Hwang CL, Larsson SC, Mukamal KJ, Roerecke M; on behalf the American Heart Association Council on Lifestyle and Cardiometabolic Health; Council on Cardiovascular and Stroke Nursing; Council on Clinical Cardiology; and Stroke Council. Alcohol Use and Cardiovascular Disease: A Scientific Statement From the American Heart Association. Circulation. 2025;152(1):e7-e21. https://doi.org/10.1161/CIR.0000000000001341.
  • Lee KY, Lee SR, Choi EK, Choi J, Ahn HJ, Kwon S, Han KD, Oh S, Lip GYH. Cardiovascular benefits of early rhythm control and healthy lifestyle in young atrial fibrillation. Eur J Clin Invest. 2025;55(6):e70018. https://doi.org/10.1111/eci.70018.
  • Deng H, Mei Y, Wu C, Gong C, Lai Z, Huang J, Zheng M, Chen J, Xie Y, Fan H, Wu X, Cai X, Xue Y, Wu S, Liu X. Association of healthy lifestyle and the incidence of atrial fibrillation in senior adults: a prospective cohort study. BMC Geriatr. 2025;25(1):160. https://doi.org/10.1186/s12877-025-05825-9.
  • Atta-Fosu T, LaBarbera M, Ghose S, Schoenhagen P, Saliba W, Tchou PJ, Lindsay BD, Desai MY, Kwon D, Chung MK, Madabhushi A. A new machine learning approach for predicting likelihood of recurrence following ablation for atrial fibrillation from CT. BMC Med Imaging. 2021;21(1):45. https://doi.org/10.1186/s12880-021-00578-4.
  • Zacharia E, Papageorgiou N, Ioannou A, Siasos G, Papaioannou S, Vavuranakis M, Latsios G, Vlachopoulos C, Toutouzas K, Deftereos S, Providência R, Tousoulis D. Inflammatory Biomarkers in Atrial Fibrillation. Curr Med Chem. 2019;26(5):837-854. https://doi.org/10.2174/0929867324666170727103357.
  • Miyasaka Y. Atrial fibrillation progression: what is the impact and how can we intervene? European Heart Journal. 2021;42(29):2872-2881.
  • Gunawardene M, Schmidt B. Management von Risikofaktoren und Begleiterkrankungen bei Vorhofflimmern [Management of Risk Factors and Comobordities in Atrial Fibrillation] [in German]. Dtsch Med Wochenschr. 2025;150(16):945-953. https://doi.org/10.1055/a-2516-8410.
  • Lane DA, Andrade JG, Arbelo E, Boriani G, Hendriks JM, Lee SR, Lip GYH, Mant J, Middeldorp ME. Atrial fibrillation. Lancet. 2026;407(10532):1000-1013. https://doi.org/10.1016/S0140-6736(25)02166-X.
  • Hindricks G, Potpara T, Dagres N, Arbelo E, Bax JJ, Blomström-Lundqvist C, Boriani G, Castella M, Dan GA, Dilaveris PE, Fauchier L, Filippatos G, Kalman JM, La Meir M, Lane DA, Lebeau JP, Lettino M, Lip GYH, Pinto FJ, Thomas GN, Valgimigli M, Van Gelder IC, Van Putte BP, Watkins CL; ESC Scientific Document Group. 2020 ESC Guidelines for the diagnosis and management of atrial fibrillation developed in collaboration with the European Association for Cardio-Thoracic Surgery (EACTS): The Task Force for the diagnosis and management of atrial fibrillation of the European Society of Cardiology (ESC) Developed with the special contribution of the European Heart Rhythm Association (EHRA) of the ESC. Eur Heart J. 2021;42(5):373-498. https://doi.org/10.1093/eurheartj/ehaa612.
  • Liu X, Jiang J, Wei L, Xing W, Shang H, Liu G, Liu F. Prediction of all-cause mortality in coronary artery disease patients with atrial fibrillation based on machine learning models. BMC Cardiovasc Disord. 2021;21(1):499. https://doi.org/10.1186/s12872-021-02314-w.
  • Gupta N, Haft JI, Bajaj S, Samuel A, Parikh R, Pandya D, Shamoon F. Role of the combined CHADS2 score and echocardiographic abnormalities in predicting stroke in patients with paroxysmal atrial fibrillation. J Clin Neurosci. 2012;19(9):1242-5. https://doi.org/10.1016/j.jocn.2011.12.008.
  • Xie C, Wang Z, Yang C, Liu J, Liang H. Machine Learning for Detecting Atrial Fibrillation from ECGs: Systematic Review and Meta-Analysis. Rev Cardiovasc Med. 2024;25(1):8. https://doi.org/10.31083/j.rcm2501008.
  • Dziano JK, Ariyaratnam JP, Middeldorp ME, Sanders P, Elliott AD. Obesity and Atrial Fibrillation: From Mechanisms to Treatment. Heart Lung Circ. 2025;34(10):1021-1032. https://doi.org/10.1016/j.hlc.2025.08.003.
  • Sedighi J, Luedde M, Boettger P, Bengel P, Bauer P, Sossalla S, Rozo Sánchez SE, Kostev K. Overweight, obesity and incident atrial fibrillation: Real-world evidence from 400 000 patients in Germany. Diabetes Obes Metab. 2025;27(10):5822-5830. https://doi.org/10.1111/dom.16637.
  • Linz D, Nattel S, Kalman JM, Sanders P. Sleep Apnea and Atrial Fibrillation. Card Electrophysiol Clin. 2021;13(1):87-94. https://doi.org/10.1016/j.ccep.2020.10.003.
  • Kunts LD, Germanova OA, Reshetnikova YB, Galati G, Milevskaya IV, Biondi-Zoccai G. Extrasystolic arrhythmia as an atrial fibrillation predictor [in Russian]. Science and Innovations in Medicine. 2024;9(2):117-123. https://doi.org/10.35693/SIM624503.
  • Germanova OA, Galati G, Kunts LD, Usenova AA, Reshetnikova YB, Germanov AV, Stefanidis A. Predictors of paroxysmal atrial fibrillation: Analysis of 24-hour ECG Holter monitoring [in Russian]. Science and Innovations in Medicine. 2024;9(1):44-48. https://doi.org/10.35693/SIM626301.
  • Germanova O, Galati G, Germanov A, Stefanidis A. Atrial fibrillation as a new independent risk factor for thromboembolic events: hemodynamics and vascular consequence of long ventricular pauses. Minerva Cardiol Angiol. 2023;71(2):175-181. https://doi.org/10.23736/S2724-5683.22.06000-8.