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Published on: February 26, 2013
Population screening for atrial fibrillation by student pharmacists at health fairs
Background:
Individuals with unrecognized atrial fibrillation (AF) may be at an increased risk of stroke. There is a need to develop a sustainable and reproducible population-based screening model to identify unrecognized AF.
Objective:
The objective of this study is to evaluate AF screening and education at student pharmacist-driven health fairs.
Methods:
Screening for AF was performed by student members of the American Pharmacist Association Academy of Student Pharmacists with preceptor oversight. Participants were screened using the KardiaMobile device (AliveCor, Mountain View, CA), a Food and Drug Administration-cleared device that interprets a medical-grade electrocardiogram in 30 seconds. Student pharmacists also calculated a CHA₂DS₂-VASc score. Participant education was provided using an American Heart Association AF patient information sheet. Learning assessment was evaluated with 3 multiple choice questions.
Results:
Students screened a total of 697 participants over a 6-month period at 13 health fairs. Overall, 71% of the participants were women aged 56 ± 15 years (mean ± SD). Sixteen of the participants (2.3%) who were screened received results indicating possible AF. None of the participants with a possible positive finding had symptoms suggestive of AF. Of these 16 participants, 11 (69%) had a CHA₂DS₂-VASc score greater than or equal to 2 (2.7 ± 0.7). Most participants answered each learning assessment question correctly. More than 95% of participants believed that screening for AF at health fairs was important or very important.
Conclusion:
Student pharmacist-driven health fairs were shown to be feasible models to screen for AF and were effective in providing AF education to the public. Student pharmacists also cultivated a clinical skill that is transferable to their future practice setting, including the community pharmacy setting. Additional studies are needed to assess whether population-based real-time assessment and detection of AF can reduce the risk of stroke in individuals with previously undetected AF.
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