Predicting atrial fibrillation in primary care using machine learning.
BACKGROUND: 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.
METHODS: 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.
RESULTS: 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).
CONCLUSION: 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.
|投稿者||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; Gordon, Jason|
|組織名||Bristol-Myers Squibb Pharmaceutical Ltd, Uxbridge, United Kingdom.;Health Economics and Outcomes Research Ltd, Cardiff, United Kingdom.;Pfizer Ltd, Surrey, United Kingdom.;Department of Cardiology, Royal Free Hospital, London, United Kingdom.;Department of Haematological Medicine, Guys and St Thomas' NHS Foundation Trust,;King's College London, London, United Kingdom.;Division of Cardiovascular Medicine, Guys and St Thomas' NHS Foundation Trust,;Department of Engineering Science, University of Oxford, Oxford, United Kingdom.|