Predicting atrial fibrillation in primary care using machine learning.

Autor: Hill NR; Bristol-Myers Squibb Pharmaceutical Ltd, Uxbridge, United Kingdom., Ayoubkhani D; Health Economics and Outcomes Research Ltd, Cardiff, United Kingdom., McEwan P; Health Economics and Outcomes Research Ltd, Cardiff, United Kingdom., Sugrue DM; Health Economics and Outcomes Research Ltd, Cardiff, United Kingdom., Farooqui U; Bristol-Myers Squibb Pharmaceutical Ltd, Uxbridge, United Kingdom., Lister S; Bristol-Myers Squibb Pharmaceutical Ltd, Uxbridge, United Kingdom., Lumley M; Pfizer Ltd, Surrey, United Kingdom., Bakhai A; Department of Cardiology, Royal Free Hospital, London, United Kingdom., Cohen AT; Department of Haematological Medicine, Guys and St Thomas' NHS Foundation Trust, King's College London, London, United Kingdom., O'Neill M; Division of Cardiovascular Medicine, Guys and St Thomas' NHS Foundation Trust, King's College London, London, United Kingdom., Clifton D; Department of Engineering Science, University of Oxford, Oxford, United Kingdom., Gordon J; Health Economics and Outcomes Research Ltd, Cardiff, United Kingdom.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2019 Nov 01; Vol. 14 (11), pp. e0224582. Date of Electronic Publication: 2019 Nov 01 (Print Publication: 2019).
DOI: 10.1371/journal.pone.0224582
Abstrakt: 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.
Competing Interests: NR Hill, U Farooqui, & S Lister are employees of Bristol-Myers Squibb Company. M Lumley is an employee of Pfizer Inc. D Ayoubkhani, P McEwan, D M Sugrue, J Gordon are employed by HEOR Ltd., which provides consulting and other research services to pharmaceutical, medical device, and related organizations. In their salaried positions, they work with a variety of companies and organizations, and are precluded from receiving payments or honoraria directly from these organizations for services rendered. AT Cohen reports grants and personal fees from Bristol-Myers Squibb Company and Pfizer Inc. during the conduct of the study; personal fees from Boehringer Ingelheim, grants and personal fees from Bristol-Myers Squibb Company, grants and personal fees from Daiichi-Sankyo Europe, personal fees from Abbvie, ACI Clinical, grants and personal fees from Pfizer, Inc., personal fees from Portola, personal fees from Janssen, personal fees from ONO Pharmaceuticals, and grants and personal fees from Bayer AG, outside the submitted work. A Bakhai reports personal fees from Bristol-Myers Squibb Company and Pfizer Inc. during the conduct of the study; personal fees from Boehringer Ingelheim, personal fees from Bristol-Myers Squibb Company, personal fees from Daiichi-Sankyo Europe, personal fees from Johnson & Johnson, personal fees from Pfizer, Inc., personal fees from Novartis, personal fees from Sanofi, personal fees from MSD, personal fees from Janssen, personal fees from Roche, and personal fees from Bayer AG, outside the submitted work. D Clifton reports personal fees from Bristol-Myers Squibb Company during the conduct of the study; and outside the submitted work, personal fees from Drayson Health (now Sensyne Health), personal fees from Ferrovial plc., personal fees from Quanta Dialysis, and personal fees from BioBeats Ltd. M O’Neill reports personal fees from Bristol-Myers Squibb Company and Pfizer Inc. during the conduct of the study; grants and personal fees from Biosense Webster, grants and personal fees from Abbott, personal fees from Siemens, personal fees from Vytronus, personal fees from Medtronic outside the submitted work.
Databáze: MEDLINE
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