Abstract

The use of implantable Loop Recorders (ILRs) is increasing rapidly but generates a high workload, challenging timely adjudication of transmissions. This burden is aggravated by excessive false positive alerts. A novel artificial intelligence (AI)-based algorithm has been developed to reclassify ILR episodes aiming to decrease false positive rate by 80% while maintaining 99% sensitivity. To evaluate the performance of the algorithm in real-life conditions. 2,269 Medtronic Reveal LINQ ILR patients were enrolled from European medical centers from June to October 202. Using a cloud based remote monitoring platform, all episodes detected as “Abnormal” by ILRs were re-diagnosed by the AI algorithm. Healthcare professionals (HCPs) monitoring their patients on this platform could review the diagnosis indicated by the algorithm but, if necessary, attribute a new diagnosis to any episode. The positive predictive value (PPV) and negative predictive value (NPV) of the algorithm detecting “Abnormal” episodes were evaluated by comparing AI algorithm classification with healthcare professionals’ diagnoses, excluding uncertain diagnoses (artifact, lead noise, atrial and ventricular monitoring). Among 27,158 “Abnormal” LINQ episodes analyzed by the AI algorithm and checked by HCPs, 12,065 (44.4%) were diagnosed as “Normal Rhythm” by the algorithm. HCPs submitted a “certain” diagnosis on 2,683 of these episodes, among which 2,657 were “Normal Rhythm”, resulting in a NPV of 99.0%. The other 15,093 (55,6%) “Abnormal” diagnoses were confirmed by the AI algorithm. HCPs submitted a certain diagnosis on 2,351 of these episodes, among which 1,832 were “Abnormal”, resulting in a PPV of 77.9%. In this real-world study, the AI algorithm re-diagnosed 43.1% of LINQ detected episodes as “Normal Rhythm”. In addition, the AI algorithm showed good agreement rates with the HCPs reviewing the same episodes, with a 99.0% NPV and a 78.2% PPV.

Full Text
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