Personalized and automated remote monitoring of atrial fibrillation

Arnaud Rosier 1, 2, 3 Philippe Mabo 4, 5, 1 Lynda Temal 5 Pascal Van Hille 5 Olivier Dameron 6 Louise Deléger 7 Cyril Grouin 7 Pierre Zweigenbaum 7 Julie Jacques 8 Emmanuel Chazard 9 Laure Laporte 10 Christine Henry 10 Anita Burgun 11, 2
1 CIC-IT Rennes
2 CRC - Centre de Recherche des Cordeliers
3 Hôpital Privé Jacques Cartier [Massy]
4 Service de cardiologie et maladies vasculaires
5 LTSI - Laboratoire Traitement du Signal et de l'Image
6 Dyliss - Dynamics, Logics and Inference for biological Systems and Sequences
IRISA-D7 - GESTION DES DONNÉES ET DE LA CONNAISSANCE, Inria Rennes – Bretagne Atlantique
7 LIMSI - Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur
8 Alicante [Seclin]
9 CERIM - Centre d'Etudes et de Recherche en Informatique Médicale
10 Sorin Group [Clamart]
11 HEGP - Hôpital Européen Georges Pompidou [APHP]
Abstract : AIMS: Remote monitoring of cardiac implantable electronic devices is a growing standard; yet, remote follow-up and management of alerts represents a time-consuming task for physicians or trained staff. This study evaluates an automatic mechanism based on artificial intelligence tools to filter atrial fibrillation (AF) alerts based on their medical significance. METHODS AND RESULTS: We evaluated this method on alerts for AF episodes that occurred in 60 pacemaker recipients. AKENATON prototype workflow includes two steps: natural language-processing algorithms abstract the patient health record to a digital version, then a knowledge-based algorithm based on an applied formal ontology allows to calculate the CHA2DS2-VASc score and evaluate the anticoagulation status of the patient. Each alert is then automatically classified by importance from low to critical, by mimicking medical reasoning. Final classification was compared with human expert analysis by two physicians. A total of 1783 alerts about AF episode \textgreater5 min in 60 patients were processed. A 1749 of 1783 alerts (98%) were adequately classified and there were no underestimation of alert importance in the remaining 34 misclassified alerts. CONCLUSION: This work demonstrates the ability of a pilot system to classify alerts and improves personalized remote monitoring of patients. In particular, our method allows integration of patient medical history with device alert notifications, which is useful both from medical and resource-management perspectives. The system was able to automatically classify the importance of 1783 AF alerts in 60 patients, which resulted in an 84% reduction in notification workload, while preserving patient safety
Keywords : decision support systems Remote monitoring Artificial intelligence atrial fibrillation cardiac implantable electronic devices
Type de document :
Article dans une revue
EP-Europace, Oxford University Press (OUP): Policy B, 2016, 18 (3), pp.347-352. 〈10.1093/europace/euv234〉
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Arnaud Rosier, Philippe Mabo, Lynda Temal, Pascal Van Hille, Olivier Dameron, et al.. Personalized and automated remote monitoring of atrial fibrillation. EP-Europace, Oxford University Press (OUP): Policy B, 2016, 18 (3), pp.347-352. 〈10.1093/europace/euv234〉. 〈hal-01331019〉

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