Personalized and automated remote monitoring of atrial fibrillation

Rosier, Arnaud; Mabo, Philippe; Temal, Lynda; Hille, Pascal Van; Dameron, Olivier; Deléger, Louise; Grouin, Cyril; Zweigenbaum, Pierre; Jacques, Julien; Chazard, Emmanuel; Laporte, Laure; Henry, C. J. K.; Burgun, Anita

doi:10.1093/europace/euv234

Cited by 26 publications

(18 citation statements)

References 12 publications

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“…Ours is not the first study to utilize unstructured EHR data in AF research. [15–17,23] Our study builds on this previous work through the use of text data with an NLP pipeline, the calculation of additional risk scores and an analysis of prescribing patterns. Whilst we evaluate our pipeline in the context of AF, our aim is to provide an open tool for clinical risk scoring calculations in general.…”

Section: Discussionmentioning

confidence: 99%

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Semantic computational analysis of anticoagulation use in atrial fibrillation from real world data

Bean

Teo

et al. 2019

PLoS ONE

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Atrial fibrillation (AF) is the most common arrhythmia and significantly increases stroke risk. This risk is effectively managed by oral anticoagulation. Recent studies using national registry data indicate increased use of anticoagulation resulting from changes in guidelines and the availability of newer drugs. The aim of this study is to develop and validate an open source risk scoring pipeline for free-text electronic health record data using natural language processing. AF patients discharged from 1st January 2011 to 1st October 2017 were identified from discharge summaries (N = 10,030, 64.6% male, average age 75.3 ± 12.3 years). A natural language processing pipeline was developed to identify risk factors in clinical text and calculate risk for ischaemic stroke (CHA2DS2-VASc) and bleeding (HAS-BLED). Scores were validated vs two independent experts for 40 patients. Automatic risk scores were in strong agreement with the two independent experts for CHA2DS2-VASc (average kappa 0.78 vs experts, compared to 0.85 between experts). Agreement was lower for HAS-BLED (average kappa 0.54 vs experts, compared to 0.74 between experts). In high-risk patients (CHA2DS2-VASc ≥2) OAC use has increased significantly over the last 7 years, driven by the availability of DOACs and the transitioning of patients from AP medication alone to OAC. Factors independently associated with OAC use included components of the CHA2DS2-VASc and HAS-BLED scores as well as discharging specialty and frailty. OAC use was highest in patients discharged under cardiology (69%). Electronic health record text can be used for automatic calculation of clinical risk scores at scale. Open source tools are available today for this task but require further validation. Analysis of routinely collected EHR data can replicate findings from large-scale curated registries.

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Section: Discussionmentioning

confidence: 99%

“…Previous studies have found it is possible to accurately predict CHA 2 DS 2 -VASc using EHR text. [15–17] We build on this work to develop a flexible open source pipeline and calculate additional risk scores. Our specific objectives are to:…”

Section: Introductionmentioning

confidence: 99%

Semantic computational analysis of anticoagulation use in atrial fibrillation from real world data

Bean

Teo

et al. 2019

PLoS ONE

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“…• Identify at-risk patients early (even before symptoms develop) and permit pre-emptive care (Boehmer 2017, Rosier et al, 2016).…”

Section: Healthcare Deliverymentioning

confidence: 99%

2021 ISHNE/ HRS/ EHRA/ APHRS collaborative statement on mHealth in Arrhythmia Management: Digital Medical Tools for Heart Rhythm Professionals

Varma

Cygankiewicz

Turakhia

et al. 2021

Noninvasive Electrocardiol

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This collaborative statement from the International Society for Holter and Noninvasive Electrocardiology/ Heart Rhythm Society/ European Heart Rhythm Association/ Asia Pacific Heart Rhythm Society describes the current status of mobile health ("mHealth") technologies in arrhythmia management. The range of digital medical tools and heart rhythm disorders that they may be applied to and clinical decisions that may be enabled are discussed. The facilitation of comorbidity and lifestyle management (increasingly recognized to play a role in heart rhythm disorders) and patient self‐management are novel aspects of mHealth. The promises of predictive analytics but also operational challenges in embedding mHealth into routine clinical care are explored.

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“…ML applications have allowed for risk stratification, improved arrhythmia localisation and streamlined remote monitoring which may significantly reduce the workload faced by electrophysiologists. [ 42 – 44 ]…”

Section: Cardiac Implantable Electronic Devicesmentioning

confidence: 99%

“…Rosier et al examined the potential for ML to automate monitoring of alerts from CIEDs. [ 44 ] They used natural language processing to examine EHR data to determine the significance of AF alerts from CIEDs. The natural language processing algorithm was able to calculate CHA 2 DS 2 -VASc scores and anticoagulant status for each patient and thus, classify the importance of the AF alert.…”

Section: Cardiac Implantable Electronic Devicesmentioning

confidence: 99%

Applications of Machine Learning in Cardiac Electrophysiology

Muthalaly

Evans

2020

Arrhythmia &Amp; Electrophysiology Review

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Artificial intelligence through machine learning (ML) methods is becoming prevalent throughout the world, with increasing adoption in healthcare. Improvements in technology have allowed early applications of machine learning to assist physician efficiency and diagnostic accuracy. In electrophysiology, ML has applications for use in every stage of patient care. However, its use is still in infancy. This article will introduce the potential of ML, before discussing the concept of big data and its pitfalls. The authors review some common ML methods including supervised and unsupervised learning, then examine applications in cardiac electrophysiology. This will focus on surface electrocardiography, intracardiac mapping and cardiac implantable electronic devices. Finally, the article concludes with an overview of how ML may impact on electrophysiology in the future.

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Personalized and automated remote monitoring of atrial fibrillation

Cited by 26 publications

References 12 publications

Semantic computational analysis of anticoagulation use in atrial fibrillation from real world data

Semantic computational analysis of anticoagulation use in atrial fibrillation from real world data

2021 ISHNE/ HRS/ EHRA/ APHRS collaborative statement on mHealth in Arrhythmia Management: Digital Medical Tools for Heart Rhythm Professionals

Applications of Machine Learning in Cardiac Electrophysiology

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