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Optimizing atrial fibrillation management using a novel patient-level computational model

Minsi Cai1, Cristian Barrios-Espinosa2, Michiel Rienstra3

  • 1Department of Cardiology, Cardiovascular Research Institute Maastricht, Faculty of Health, Medicine, and Life Sciences, Maastricht University, Maastricht, the Netherlands.

Med (New York, N.Y.)
|November 1, 2025
PubMed

Insights

Early atrial fibrillation (AF) detection is challenging. Frequent screening improves AF detection, but stroke reduction depends on patient and healthcare factors, with thrice-daily ECGs showing highest rates.

Area of Science:

  • Computational modeling in cardiovascular disease
  • Health informatics and predictive analytics
  • Epidemiology of atrial fibrillation

Background:

  • Early detection of atrial fibrillation (AF) is difficult due to episode variability and poor symptom correlation.
  • The optimal screening strategy for AF and its impact on stroke prevention remain unclear.

Purpose of the Study:

  • To analyze the impact of screening-mediated AF detection on stroke risk using a computational model.
  • To assess the effectiveness of different screening strategies for early AF diagnosis and stroke risk modulation.

Main Methods:

  • A Markov-like computer model simulating seven clinical states and AF-related atrial remodeling was developed.
  • Model calibration and validation were performed by replicating clinical studies.
  • The model assessed screening strategies' effects on AF diagnosis and simulated oral anticoagulation's impact on stroke rates.

Main Results:

  • The model accurately simulates patient lifetime AF episodes and clinical outcomes.
  • Intermittent AF screening benefits were frequency and duration-dependent; thrice-daily ECGs yielded the highest detection rates.
  • Screening groups showed comparable 5-year and lower 25-year stroke rates, with benefits amplified by effective anticoagulation and higher baseline stroke risk.

Conclusions:

  • A novel computational patient-level AF model was developed, consistent with real-world data.
  • This model allows systematic assessment of AF management strategies.
  • While frequent screening increases AF detection, stroke reduction is contingent on patient and healthcare system characteristics.
Abstract

Keywords:
Markov modelTranslation to patientsatrial fibrillationcomputational modelingin silico trialsoral anticoagulation therapyscreeningsimulationstroke

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