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Year : 2019  |  Volume : 4  |  Issue : 2  |  Page : 48-54

The advanced reference annotation algorithm: A novel approach to reference annotation for electroanatomic mapping

1 Department of Medicine, Cardiac Arrhythmia Service, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
2 Biosense Webster, Inc., Haifa, Israel
3 Department of Medicine, Royal Brompton Hospital, London, UK
4 Department of Medicine, Hopital Cardiologique du Haut Lévêque, CHU Bordeaux, Pessac, France
5 Division of Cardiology, Department of Medical Sciences, University of Turin, Turin, Italy
6 Department of Medicine, Medical University of South Carolina, Charleston, SC, USA
7 Department of Medicine, Minneapolis Heart Institute, Minneapolis, MN, USA
8 Department of Medicine, Baruch Padeh Medical Center, Poriya, Israel
9 Department of Medicine, Rambam Medical Center, Haifa, Israel
10 Department of Cardiology-Electrophysiology, University Heart Center, University Hospital Hamburg-Eppendorf, Berlin, Germany; Deutsches Zentrum Für Herz-Kreislauf-Forschung (German Center for Cardiovascular Research), Partner Site Hamburg/Kiel/Lübeck, Germany

Correspondence Address:
Dr. Paul C Zei
Cardiac Arrhythmia Service, Brigham and Women's Hospital, Harvard Medical School, 70 Francis Street, Boston, MA 02115
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/IJHR.IJHR_1_20

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Background: Reliable reference annotation is critical for accurate activation mapping. Currently used referencing algorithms can be limited by suboptimal detection and stability performance. The advanced reference annotation (ARA) algorithm, a novel algorithm using a weighted reference across multiple electrodes, has been developed to optimize reference annotation. Materials and Methods: To evaluate ARA, recordings using CARTO from 26 clinical cases with complex cardiac arrhythmias, representing mapping of various rhythms, were segmented into test vectors consisting of roughly 62,000 annotation events. These were annotated by an expert clinician (gold standard [GS]) and compared with the legacy/ARA algorithms on detection rate and stability and positive predictive value (PPV). Results: The ARA algorithm detection rate uniformly outperformed legacy, when compared with GS (97 ± 4% vs. 81 ± 19%, respectively; P = 0.001). ARA was performed with high fidelity with an average stability metric (the percentage of true positive ARA annotations within 10 ms of the GS annotation) of 98 ± 3% with most test vectors achieving perfect (100%) stability. Overall, the PPV of ARA annotations was 98 ± 4%; nearly all ARA-annotated activation corresponded to clinically observed events; ARA was superior to legacy across all analyzed test vectors (98 ± 4% vs. 88 ± 23%, P = 0.004); all ARA test vector groups had PPV >90%. Conclusion: The ARA algorithm outperformed the clinical standard, compared to an expert clinician GS. These improvements may translate into greater mapping accuracy/efficiency and procedural outcomes in diagnosis of complex cardiac arrhythmias.

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