Publications by authors named "Lauren Lee Shaffer"
Background: Atrial Fibrillation (AF) is a common and clinically heterogeneous arrythmia. Machine learning (ML) algorithms can define data-driven disease subtypes in an unbiased fashion, but whether the AF subgroups defined in this way align with underlying mechanisms, such as high polygenic liability to AF or inflammation, and associate with clinical outcomes is unclear.
Methods: We identified individuals with AF in a large biobank linked to electronic health records (EHR) and genome-wide genotyping.
Article Synopsis
- Genetic testing in individuals without symptoms can reveal carriers of harmful arrhythmia gene variants, but the clinical implications of these findings are still not fully understood.
- In a study of nearly 22,000 participants, 0.6% were found to carry pathogenic or likely pathogenic variants linked to arrhythmias, with many displaying significant arrhythmia-related health records.
- Follow-up investigations showed that variant results led to new diagnoses in some individuals, highlighting the potential for genome sequencing to uncover important health information.