Multimodal data integration to predict atrial fibrillation
A combination of clinical and polygenic risk scores was the most effective and parsimonious approach to predicting AF. Further addition of an ECG risk score or protein risk score provided only modest incremental improvement for predicting AF.
Authors
- Álvaro Alonso (ORCID: https://orcid.org/0000-0002-2225-8323)
- Wei Pan (ORCID: https://orcid.org/0000-0002-1159-0582)
- James S. Pankow (ORCID: https://orcid.org/0000-0001-7076-483X)
- Ruoyu He (ORCID: https://orcid.org/0009-0000-7459-8429)
- Yuchen Yao (ORCID: https://orcid.org/0000-0002-4154-1829)
- Elsayed Z. Soliman (ORCID: https://orcid.org/0000-0001-5632-8150)
- Yuekai Ji
- Zhong Zhuang (ORCID: https://orcid.org/0000-0002-4845-6256)
- Katherine A. Knutson (ORCID: https://orcid.org/0000-0002-6091-4627)
- Faye L. Norby (ORCID: https://orcid.org/0000-0002-1975-0405)
- Michael J. Zhang (ORCID: https://orcid.org/0000-0003-2537-2831)
- Lin Y. Chen (ORCID: https://orcid.org/0000-0002-0700-814X)
- Wendy Wang (ORCID: https://orcid.org/0000-0001-6025-0173)
- Weihong Tang
Institutions
- University of Minnesota (US)
- Emory University (US)
- Minneapolis Heart Institute Foundation (US)
- University of Minnesota Medical Center (US)
- Wake Forest University (US)
Publication Details
- Journal
- European Heart Journal - Digital Health
- Published
- 2024-11-04
- DOI
- https://doi.org/10.1093/ehjdh/ztae081
- Citations
- 4
- Primary Topic
- Atrial Fibrillation Management and Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 1.61
Funders
- U.S. Department of Health and Human Services
- University of Minnesota
- Norges Idrettshøgskole
- National Institutes of Health
- Minnesota Supercomputing Institute, University of Minnesota
- National Heart, Lung, and Blood Institute
- National Institute on Deafness and Other Communication Disorders
- National Institute of Neurological Disorders and Stroke