Usefulness of the ACTEL Score to Predict Atrial Fibrillation in Patients with Cryptogenic Stroke

Antonio Muscari1,2, Pietro Barone3, Luca Faccioli4

  • 1Stroke Unit, Medical Department of Continuity of Care and Disability, S.Orsola-Malpighi Hospital, Bologna, Italy, antonio.muscari@unibo.it.

Cardiology
|January 29, 2020
PubMed

Insights

The ACTEL score helps identify patients with atrial fibrillation (AF) after ischemic stroke. This new score, derived from patient data, offers high accuracy in detecting AF in cryptogenic stroke cases.

Area of Science:

  • Neurology
  • Cardiology
  • Medical Diagnostics

Background:

  • Assessing undetected atrial fibrillation (AF) in ischemic stroke patients is crucial.
  • Previous research developed the MrWALLETS score for AF detection.
  • Cryptogenic stroke (CS) patients are a key group for AF screening.

Purpose of the Study:

  • To develop and validate a new scoring system, ACTEL, for identifying AF in ischemic stroke patients.
  • To improve upon the existing MrWALLETS score.
  • To utilize cryptogenic stroke patients as a control group for AF detection.

Main Methods:

  • Retrospective analysis of 191 ischemic stroke patients (68 with AF, 123 with CS).
  • Data collected included brain CT scans, echocardiography, ultrasound, ECG monitoring, and cardiovascular risk factor assessment.
  • Logistic regression analysis was used to identify independent predictors of AF.

Main Results:

  • The ACTEL score comprises 5 variables: Age ≥75, hypercholesterolemia, tricuspid regurgitation, left ventricular end-diastolic volume, and left atrium size.
  • The score demonstrated an area under the receiver operating characteristic curve of 0.80 for AF identification.
  • A cutoff of ≥2 yielded a positive predictive value of 80.8% and specificity of 92.7%.

Conclusions:

  • The ACTEL score is a simplified and improved tool for identifying patients with first-diagnosed AF.
  • It is particularly effective in the context of cryptogenic strokes.
  • The score offers a high positive predictive value for AF detection.
Abstract

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