Prediction of Atrial Fibrillation identification in patients with Embolic Stroke of Undetermined Source

Chousou, Panagiota (2023) Prediction of Atrial Fibrillation identification in patients with Embolic Stroke of Undetermined Source. Doctoral thesis, University of East Anglia.

[thumbnail of PhD thesis P A Chousou Final submission 19.06.24.pdf] PDF
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More than 30% of patients who experience an embolic stroke of undetermined source (ESUS) are found to have atrial fibrillation (AF) when monitored with an implantable loop recorder (ILR). Detecting AF in ESUS survivors holds crucial therapeutic implications, underscoring the importance of assessing AF risk.

In this thesis, I assessed the incidence of AF in patients with and without ESUS, which was found to be significantly higher amongst the ESUS group. I also demonstrated that monitoring ESUS patients with smart phone-based device is feasible and could be cost-effective prior to ILR implantation. I further assessed, clinical, electrocardiographic, Holter and echocardiographic derived parameters of ESUS patients. I demonstrated that age, diastolic blood pressure (DBP), advanced interatrial block (A-IAB), runs of supraventricular extrasystoles (SVEs), impaired left atrial (LA) reservoir strain and lateral PA (defined as the time interval from the beginning of P wave on ECG to the A’ on pulse wave tissue Doppler of the lateral mitral annulus) to be independent predictors of AF. Age, DBP and imaging parameters were then combined to derive the PADS risk model for AF prediction (lateral PA, age, DBP, LA reservoir strain). This model showed good discrimination ability on the derivation cohort with consistent results during internal validation, which I then validated with an external cohort with excellent discrimination ability.

I further assessed whether specific blood biomarkers associate with AF and increase the predictive ability of PADS in a different cohort of ESUS patients. Neither blood biomarkers or other variables increased the predictive ability of PADS.

In conclusion, I investigated the incidence of AF detection in ESUS patients and multiple predictors of future AF. A dedicated score, the PADS score was then derived and validated which is a robust risk prediction model to identify risk of AF in ESUS survivors.

Item Type: Thesis (Doctoral)
Faculty \ School: Faculty of Medicine and Health Sciences > Norwich Medical School
Depositing User: Chris White
Date Deposited: 02 Jul 2024 10:29
Last Modified: 02 Jul 2024 10:29

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