1. Predicting atrial fibrillation in primary care using machine learning.
- Author
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Hill, Nathan R., Ayoubkhani, Daniel, McEwan, Phil, Sugrue, Daniel M., Farooqui, Usman, Lister, Steven, Lumley, Matthew, Bakhai, Ameet, Cohen, Alexander T., O'Neill, Mark, Clifton, David, and Gordon, Jason
- Subjects
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ATRIAL fibrillation , *MACHINE learning , *PRIMARY care , *ARTIFICIAL neural networks , *BODY mass index , *SUPPORT vector machines - Abstract
Atrial fibrillation (AF) is the most common sustained heart arrhythmia. However, as many cases are asymptomatic, a large proportion of patients remain undiagnosed until serious complications arise. Efficient, cost-effective detection of the undiagnosed may be supported by risk-prediction models relating patient factors to AF risk. However, there exists a need for an implementable risk model that is contemporaneous and informed by routinely collected patient data, reflecting the real-world pathology of AF. This study sought to develop and evaluate novel and conventional statistical and machine learning models for risk-predication of AF. This was a retrospective, cohort study of adults (aged ≥30 years) without a history of AF, listed on the Clinical Practice Research Datalink, from January 2006 to December 2016. Models evaluated included published risk models (Framingham, ARIC, CHARGE-AF), machine learning models, which evaluated baseline and time-updated information (neural network, LASSO, random forests, support vector machines), and Cox regression. Analysis of 2,994,837 individuals (3.2% AF) identified time-varying neural networks as the optimal model achieving an AUROC of 0.827 vs. 0.725, with number needed to screen of 9 vs. 13 patients at 75% sensitivity, when compared with the best existing model CHARGE-AF. The optimal model confirmed known baseline risk factors (age, previous cardiovascular disease, antihypertensive medication usage) and identified additional time-varying predictors (proximity of cardiovascular events, body mass index (both levels and changes), pulse pressure, and the frequency of blood pressure measurements). The optimal time-varying machine learning model exhibited greater predictive performance than existing AF risk models and reflected known and new patient risk factors for AF. [ABSTRACT FROM AUTHOR]
- Published
- 2019
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