Personalized and automated remote monitoring of atrial fibrillation.
OBJECTIVE
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.


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 >5 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.


CONCLUSIONS
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.
INSERM CIC-IT 804, CHU Pontchaillou, CCP, rue Henri le Guilloux, Rennes, France Service de Rythmologie, Hôpital Privé Jacques Cartier, Groupe GDS, 6 Avenue du Noyer Lambert, Massy 91300, France INSERM UMR_S 1138 Eq 22, Paris Descartes University, Paris, France dr.arnaud.rosier@gmail.com.
Arnaud
A
Rosier
INSERM CIC-IT 804, CHU Pontchaillou, CCP, rue Henri le Guilloux, Rennes, France Cardiology Department, CHU Pontchaillou, CCP, rue Henri le Guilloux, Rennes, France.
Philippe
P
Mabo
LTSI (Inserm UMR1099), Université de Rennes 1, Rennes, France.
Lynda
L
Temal
LTSI (Inserm UMR1099), Université de Rennes 1, Rennes, France.
Pascal
P
Van Hille
University of Rennes 1, UMR 6074 IRISA, Campus de Beaulieu, Rennes 35042, France.
Olivier
O
Dameron
LIMSI-CNRS, Orsay 91405, France.
Louise
L
Deléger
LIMSI-CNRS, Orsay 91405, France.
Cyril
C
Grouin
LIMSI-CNRS, Orsay 91405, France.
Pierre
P
Zweigenbaum
ALICANTE SA, France.
Julie
J
Jacques
CERIM, Faculté de Médecine, 1 Place de Verdun, Lille, France.
Emmanuel
E
Chazard
SORIN CRM SAS, Parc Noveos, 4 Avenue Réaumur, Clamart, France.
Laure
L
Laporte
SORIN CRM SAS, Parc Noveos, 4 Avenue Réaumur, Clamart, France.
Christine
C
Henry
INSERM UMR_S 1138 Eq 22, Paris Descartes University, Paris, France Hôpital Européen Georges Pompidou, Paris, France.
Anita
A
Burgun
D000200:Action Potentials; D000465:Algorithms; D000925:Anticoagulants / Q000627:therapeutic use; D001185:Artificial Intelligence; D001281:Atrial Fibrillation / Q000175:diagnosis* / Q000503:physiopathology / Q000628:therapy; D001331:Automation; D003661:Decision Support Techniques; D004562:Electrocardiography / Q000295:instrumentation*; D005602:France; D006329:Heart Conduction System / Q000503:physiopathology*; D006339:Heart Rate; D006801:Humans; D010138:Pacemaker, Artificial; D010865:Pilot Projects; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D012189:Retrospective Studies; D018570:Risk Assessment; D012815:Signal Processing, Computer-Assisted; D013686:Telemetry / Q000295:instrumentation*; D057188:Workflow; D016526:Workload
D000925:Anticoagulants
Artificial intelligence; Atrial fibrillation; Cardiac implantable electronic devices; Decision support systems; Remote monitoring