Improved diagnostic performance of insertable cardiac monitors by an artificial intelligence-based algorithm
© The Author(s) 2024. Published by Oxford University Press on behalf of the European Society of Cardiology..
AIMS: The increasing use of insertable cardiac monitors (ICM) produces a high rate of false positive (FP) diagnoses. Their verification results in a high workload for caregivers. We evaluated the performance of an artificial intelligence (AI)-based ILR-ECG Analyzer™ (ILR-ECG-A). This machine-learning algorithm reclassifies ICM-transmitted events to minimize the rate of FP diagnoses, while preserving device sensitivity.
METHODS AND RESULTS: We selected 546 recipients of ICM followed by the Implicity™ monitoring platform. To avoid clusterization, a single episode per ICM abnormal diagnosis (e.g. asystole, bradycardia, atrial tachycardia (AT)/atrial fibrillation (AF), ventricular tachycardia, artefact) was selected per patient, and analyzed by the ILR-ECG-A, applying the same diagnoses as the ICM. All episodes were reviewed by an adjudication committee (AC) and the results were compared. Among 879 episodes classified as abnormal by the ICM, 80 (9.1%) were adjudicated as 'Artefacts', 283 (32.2%) as FP, and 516 (58.7%) as 'abnormal' by the AC. The algorithm reclassified 215 of the 283 FP as normal (76.0%), and confirmed 509 of the 516 episodes as abnormal (98.6%). Seven undiagnosed false negatives were adjudicated as AT or non-specific abnormality. The overall diagnostic specificity was 76.0% and the sensitivity was 98.6%.
CONCLUSION: The new AI-based ILR-ECG-A lowered the rate of FP ICM diagnoses significantly while retaining a > 98% sensitivity. This will likely alleviate considerably the clinical burden represented by the review of ICM events.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:26 |
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| Enthalten in: |
Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology - 26(2023), 1 vom: 28. Dez. |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Crespin, Eliot [VerfasserIn] |
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| Links: |
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| Themen: |
Arrhythmia |
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| Anmerkungen: |
Date Completed 15.01.2024 Date Revised 16.01.2024 published: Print Citation Status MEDLINE |
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| doi: |
10.1093/europace/euad375 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
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| 520 | |a © The Author(s) 2024. Published by Oxford University Press on behalf of the European Society of Cardiology. | ||
| 520 | |a AIMS: The increasing use of insertable cardiac monitors (ICM) produces a high rate of false positive (FP) diagnoses. Their verification results in a high workload for caregivers. We evaluated the performance of an artificial intelligence (AI)-based ILR-ECG Analyzer™ (ILR-ECG-A). This machine-learning algorithm reclassifies ICM-transmitted events to minimize the rate of FP diagnoses, while preserving device sensitivity | ||
| 520 | |a METHODS AND RESULTS: We selected 546 recipients of ICM followed by the Implicity™ monitoring platform. To avoid clusterization, a single episode per ICM abnormal diagnosis (e.g. asystole, bradycardia, atrial tachycardia (AT)/atrial fibrillation (AF), ventricular tachycardia, artefact) was selected per patient, and analyzed by the ILR-ECG-A, applying the same diagnoses as the ICM. All episodes were reviewed by an adjudication committee (AC) and the results were compared. Among 879 episodes classified as abnormal by the ICM, 80 (9.1%) were adjudicated as 'Artefacts', 283 (32.2%) as FP, and 516 (58.7%) as 'abnormal' by the AC. The algorithm reclassified 215 of the 283 FP as normal (76.0%), and confirmed 509 of the 516 episodes as abnormal (98.6%). Seven undiagnosed false negatives were adjudicated as AT or non-specific abnormality. The overall diagnostic specificity was 76.0% and the sensitivity was 98.6% | ||
| 520 | |a CONCLUSION: The new AI-based ILR-ECG-A lowered the rate of FP ICM diagnoses significantly while retaining a > 98% sensitivity. This will likely alleviate considerably the clinical burden represented by the review of ICM events | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a arrhythmia | |
| 650 | 4 | |a artificial intelligence | |
| 650 | 4 | |a implantable loop recorder | |
| 650 | 4 | |a insertable cardiac monitor | |
| 650 | 4 | |a machine learning | |
| 650 | 4 | |a remote monitoring | |
| 700 | 1 | |a Rosier, Arnaud |e verfasserin |4 aut | |
| 700 | 1 | |a Ibnouhsein, Issam |e verfasserin |4 aut | |
| 700 | 1 | |a Gozlan, Alexandre |e verfasserin |4 aut | |
| 700 | 1 | |a Lazarus, Arnaud |e verfasserin |4 aut | |
| 700 | 1 | |a Laurent, Gabriel |e verfasserin |4 aut | |
| 700 | 1 | |a Menet, Aymeric |e verfasserin |4 aut | |
| 700 | 1 | |a Bonnet, Jean-Luc |e verfasserin |4 aut | |
| 700 | 1 | |a Varma, Niraj |e verfasserin |4 aut | |
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