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Preventing Recurrent Cardioembolic Stroke: Right Approach, Right Patient (PRECISE) Study Protocol.

Alan C Cameron1, Georgios Katsas1, Markus Arnold2

  • 1Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK.

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Summary
This summary is machine-generated.

This study develops a predictive model using clinical, ECG, blood, and genetic data to identify patients with a low risk of atrial fibrillation (AF) after stroke or TIA, optimizing cardiac monitoring strategies.

Keywords:
Atrial fibrillationBiomarkerCardiac rhythm monitoringIschaemic stroke

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Area of Science:

  • Cardiology
  • Neurology
  • Biomarker Research

Background:

  • Atrial fibrillation (AF) detection after ischaemic stroke or transient ischaemic attack (TIA) is crucial for preventing recurrence.
  • Current prolonged cardiac rhythm monitoring yields higher AF detection rates but poses implementation challenges and is not universally necessary.
  • Identifying individuals at low risk for AF can refine monitoring strategies and resource allocation.

Purpose of the Study:

  • To develop and validate a predictive model for identifying patients with a low probability of AF detection post-ischaemic stroke or TIA.
  • To integrate clinical, electrocardiogram (ECG), blood-based (including MRproANP), and genetic biomarkers into a comprehensive risk assessment tool.
  • To enable a personalized care pathway for AF screening, optimizing the use of cardiac rhythm monitoring.

Main Methods:

  • Prospective recruitment of 675 participants aged over 18 admitted with ischaemic stroke or TIA within 5 days, without known AF.
  • Collection of baseline demographics, clinical data, 12-lead ECG, blood samples for biomarkers, and genetic data.
  • Up to 28 days of cardiac rhythm monitoring using R-test or patch devices, with primary outcome of AF detection (≥30s duration).

Main Results:

  • The study aims to achieve 92.5% sensitivity in detecting AF in the target population.
  • The sample size calculation is based on detecting a significant difference from a null hypothesis sensitivity of 80% with 80% power and 5% significance.
  • Secondary outcomes include AF detection at 1-year, recurrent cardiovascular events, and mortality.

Conclusions:

  • The developed model has the potential to personalize AF screening after stroke or TIA.
  • It may reduce unnecessary prolonged cardiac rhythm monitoring for low-risk individuals.
  • This approach allows for focused, intensive monitoring on patients with a higher likelihood of AF, improving clinical outcomes and resource utilization.