Using machine learning to predict anticoagulation control in atrial fibrillation: A UK Clinical Practice Research Datalink study

Abstract

Objective: To investigate the predictive performance of machine learning (ML) algorithms for estimating anticoagulation control in patients with atrial fibrillation (AF) who are treated with warfarin. Methods: This was a retrospective cohort study of adult patients (≥18 years) between 2007 and 2016 using linked primary and secondary care data (Clinical Practice Research Datalink GOLD and Hospital Episode Statistics). Various ML techniques were explored to predict suboptimal anticoagulation control, defined as time in therapeutic range (TTR) 80 years and <70 kg, respectively). Addition of time-varying data to the LSTM NN improved predictive performance, plateauing at AUC of 0.830 at 30 weeks. Conclusion: ML algorithms displayed clinically useful ability to predict patients who are at greater risk of suboptimal control. The addition of time-varying data to the algorithm, especially prior INR measurements, improved predictive performance. These algorithms provide improved predictive tools for identifying patients who may benefit from more frequent INR monitoring or switching to alternative therapies.

Keywords

• Machine learning
• Unsupervised learning
• Atrial fibrillation
• Warfarin
• Anticoagulation control
• International normalised ratio