Artificial Intelligence–Electrocardiography to Predict Incident Atrial Fibrillation and Clinical Outcomes in Kidney Transplant Recipients
This pre-transplant AI-ECG parameter identified patients at increased risk of new-onset AF post-KTx and provided prognostic utility. Overall, this easy to obtain tool allows for risk stratification of patients who may benefit from closer monitoring, targeted risk factor modification, and early intervention.
Authors
- Hani M. Wadei (ORCID: https://orcid.org/0000-0003-4886-0897)
- Girish Pathangey (ORCID: https://orcid.org/0000-0002-7594-9056)
- Girish K. Mour (ORCID: https://orcid.org/0000-0003-3764-0047)
- Chieh‐Ju Chao (ORCID: https://orcid.org/0000-0001-6155-0266)
- Hicham El Masry (ORCID: https://orcid.org/0000-0002-2470-2862)
- Said Alsidawi (ORCID: https://orcid.org/0000-0001-8750-7880)
- Dan Sorajja (ORCID: https://orcid.org/0000-0003-1976-6420)
- Oh Jk
- Nima Baba Ali
- Mohammed Tiseer Abbas
- D. W. Johnson
- Steven J. Lester
- Samy M. Riad
- Yeoungjee Cho
- Kwan Lee
- Chadi Ayoub
- Ahmed K. Mahmoud
- Milagros Pereyra Pietri
- Reza Arsanjani
- Juan M. Farina
- Isabel G. Scalia
- Kamal Awad
- D. Eric Steidley
- Mahmoud H. Abdelnabi
- Ramzi Ibrahim
- Luis R. Scott
Institutions
- Translational Research Institute (AU)
- Mayo Clinic (US)
- The University of Queensland (AU)
- Jacksonville College (US)
- Princess Alexandra Hospital (AU)
- WinnMed (US)
- Mayo Clinic in Arizona (US)
- Mayo Clinic Hospital (US)
- Mayo Clinic in Florida (US)
Publication Details
- Journal
- Kidney360
- Published
- 2026-02-04
- DOI
- https://doi.org/10.34067/kid.0000001063
- Primary Topic
- Atrial Fibrillation Management and Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 0.00