Year: 2020
Type: dissertation
Study design: Observational Study
Abstract: Atrial fibrillation (AF) is associated with a high morbidity and mortality. Early identification of patients with AF may reduce morbidity and mortality. Current models predicting AF have limitations and focus on mainly clinical variables which are not always apparent in AF patients. Models focusing on pathophysiological mechanisms such as blood based biomarkers and ECG markers may be more accurate in identifying patients with AF. This study is based on the Birmingham and Black Country Atrial Fibrillation Registry (BBC-AF Registry) which recruited a cohort of 800 patients with and without AF. Blood based biomarkers and ECG markers were compared between the two groups of patients. The blood based biomarker analysis \nusing a novel proteomics chip technique demonstrated that BNP and a novel biomarker, fibroblast growth factor 23 (FGF-23) were increased in AF patients and were also independently predictive of AF. In the ECG analysis, QT interval was increased in AF patients and independently predicted AF. A combined model using blood based biomarkers, ECG markers and clinical variables demonstrated that a simple model consisting of simple clinical variables, QT interval, BNP and FGF-23 had a good ability to predict AF and performed better than contemporary AF prediction models in the current literature.
Author: Yanish Purmah
Language: English
Subfield: Cardiology and Cardiovascular Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W3153676737