Stratifying risk for covert atrial fibrillation after stroke: a multimodal analysis of clinical, biomarker, and AI tools

Katsas, Georgios (2026) Stratifying risk for covert atrial fibrillation after stroke: a multimodal analysis of clinical, biomarker, and AI tools. PhD thesis, University of Glasgow.

Abstract

In short, I explored strategies to improve AF detection following ischaemic stroke or TIA, focusing on identifying patients unlikely to benefit from prolonged CRM. A systematic review confirmed a dose–response relationship between monitoring duration and AF detection, with the highest yields observed after 30 days or more of continuous monitoring. Through a series of retrospective and prospective cohort studies, I demonstrated that the integration of clinical variables, ECG parameters, natriuretic peptides, and AI–based models enhances risk prediction for the absence of AFDAS. I showed that younger age, absence of lipid-lowering therapy, shorter QT interval, and higher Q offset on ECG are independent predictors of AFDAS absence, forming the basis of the PRECISE risk score. I further demonstrated that the addition of MR-proANP and NT-proBNP levels improves the discrimination of clinical risk models, with MR-proANP achieving an AUROC of 0.80 and reducing unnecessary monitoring by more than 25%. In a separate analysis of 12-lead ECGs from 3,966 stroke patients in SR, I evaluated CNNs and AutoML frameworks for AF prediction through collaborations with the University of Liverpool. ECG-based models alone had limited discriminatory performance (AUROC, 0.51), but models incorporating clinical data achieved AUROC values up to 0.70, highlighting the benefit of multimodal approaches.

These findings support a more nuanced and resource-efficient approach to poststroke AF screening. They advocate for the targeted use of prolonged CRM in highrisk individuals and reinforce the need for external validation of integrated biomarker and AI-based tools. Future work should focus on refining prediction models in diverse populations, exploring wearable technologies, and integrating AI into routine care to guide personalised secondary prevention.

• Item Type: Thesis (PhD)
• Qualification Level: Doctoral
• Additional Information: Supported by funding from Tenovus Scotland to support electronic health record linkage through the NHS GG&C Safe Haven platform.
• Subjects: R Medicine > R Medicine (General); R Medicine > RC Internal medicine
• Colleges/Schools: College of Medical Veterinary and Life Sciences > School of Cardiovascular & Metabolic Health
• Funder's Name: Tenovus Scotland
• Supervisor's Name: Dawson, Professor Jesse and Cameron, Dr. Alan
• Date of Award: 2026
• Depositing User: Theses Team
• Unique ID: glathesis:2026-86085
• Copyright: Copyright of this thesis is held by the author.
• Date Deposited: 26 Jun 2026 10:44
• Last Modified: 26 Jun 2026 10:45
• Thesis DOI: 10.5525/gla.thesis.86085
• URI: https://theses.gla.ac.uk/id/eprint/86085

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