Effectiveness of SharpSense™ algorithms in reducing bradycardia and pause detection: real-world performance in Confirm Rx™ insertable cardiac monitor

Artificial intelligence cloud platform improves arrhythmia detection from insertable cardiac monitors to 25 cardiac rhythm patterns through multi-label classification

BACKGROUND

Electrocardiogram (ECG) is the gold standard for the diagnosis of cardiac arrhythmias and other heart diseases. Insertable cardiac monitors (ICMs) have been developed to continuously monitor cardiac activity over long periods of time and to detect 4 cardiac patterns (atrial tachyarrhythmias, ventricular tachycardia, bradycardia, and pause). However, interpretation of ECG or ICM subcutaneous ECG (sECG) is time-consuming for clinicians. Artificial intelligence (AI) classifies ECG and sECG with high accuracy in short times.

OBJECTIVE

To demonstrate whether an AI algorithm can expand ICM arrhythmia recognition from 4 to many cardiac patterns.

METHODS

We performed an exploratory retrospective study with sECG raw data coming from 20 patients wearing a Confirm Rx™ (Abbott, Sylmar, USA) ICM. The sECG data were recorded in standard conditions and then analyzed by AI (Willem™, IDOVEN, Madrid, Spain) and cardiologists, in parallel.

RESULTS

In nineteen patients, ICMs recorded 2261 sECGs in an average follow-up of 23 months. Within these 2261 sECG episodes, AI identified 7882 events and classified them according to 25 different cardiac rhythm patterns with a pondered global accuracy of 88%. Global positive predictive value, sensitivity, and F1-score were 86.77%, 83.89%, and 85.52% respectively. AI was especially sensitive for bradycardias, pauses, rS complexes, premature atrial contractions, and inverted T waves, reducing the median time spent to classify each sECG compared to cardiologists.

CONCLUSION

AI can process sECG raw data coming from ICMs without previous training, extending the performance of these devices and saving cardiologists' time in reviewing cardiac rhythm patterns detection.

Novel algorithms improve arrhythmia detection accuracy in insertable cardiac monitors

INTRODUCTION

Insertable cardiac monitors (ICMs) are commonly used to diagnose cardiac arrhythmias. False detections in the latest ICM systems remain an issue, primarily due to inaccurate R-wave sensing. New discrimination algorithms were developed and tested to reduce false detections of atrial fibrillation (AF), pause, and tachycardia episodes in ICMs.

METHODS

Stored electrograms (EGMs) of AF, pause, and tachycardia episodes detected by Abbott Confirm Rx™ ICMs were extracted from the Merlin.net™ Patient Care Network, and manually adjudicated to establish independent training and testing datasets. New discrimination algorithms were developed to reject false episodes due to inaccurate R-wave sensing, P-wave identification, and R-R interval patterns. The performance of these new algorithms was quantified by false positive reduction (FPR) and true positive maintenance (TPM), relative to the existing algorithms.

RESULTS

The new AF detection algorithm was trained on 5911 EGMs from 744 devices, resulting in 66.9% FPR and 97.8% TPM. In the testing data set of 1354 EGMs from 119 devices, this algorithm achieved 45.8% FPR and 97.0% TPM. The new pause algorithm was trained on 7178 EGMs from 1490 devices, resulting in 70.9% FPR and 98.7% TPM. In the testing data set of 1442 EGMs from 340 devices, this algorithm achieved 74.4% FPR and 99.3% TPM. The new tachycardia algorithm was trained on 520 EGMs from 204 devices, resulting in 57.0% FPR and 96.6% TPM. In the testing data set of 459 EGMs from 237 devices, this algorithm achieved 57.9% FPR and 96.5% TPM.

CONCLUSION

The new algorithms substantially reduced false AF, pause, and tachycardia episodes while maintaining the majority of true arrhythmia episodes detected by the Abbott ICM algorithms that exist today. Implementing these algorithms in the next-generation ICM systems may lead to improved detection accuracy, in-clinic efficiency, and device battery longevity.

Artificial intelligence augments detection accuracy of cardiac insertable cardiac monitors: Results from a pilot prospective observational study

Background

Insertable cardiac monitors (ICMs) are indicated for long-term monitoring of patients with unexplained syncope or who are at risk for cardiac arrhythmias. The volume of ICM-transmitted information may result in long data review times to identify true and clinically relevant arrhythmias.

Objective

The purpose of this study was to evaluate whether artificial intelligence (AI) may improve ICM detection accuracy.

Methods

We performed a retrospective analysis of consecutive patients implanted with the Confirm Rx^TM ICM (Abbott) and followed in a prospective observational study. This device continuously monitors subcutaneous electrocardiograms (SECGs) and transmits to clinicians information about detected arrhythmias and patient-activated symptomatic episodes. All SECGs were classified by expert electrophysiologists and by the Willem^TM AI algorithm (IDOVEN).

Results

During mean follow-up of 23 months, of 20 ICM patients (mean age 68 ± 12 years; 50% women), 19 had 2261 SECGs recordings associated with cardiac arrhythmia detections or patient symptoms. True arrhythmias occurred in 11 patients: asystoles in 2, bradycardias in 3, ventricular tachycardias in 4, and atrial tachyarrhythmias (atrial tachycardia/atrial fibrillation [AT/AF]) in 10; with 6 patients having >1 arrhythmia type. AI algorithm overall accuracy for arrhythmia classification was 95.4%, with 97.19% sensitivity, 94.52% specificity, 89.74% positive predictive value, and 98.55% negative predictive value. Application of AI would have reduced the number of false-positive results by 98.0% overall: 94.0% for AT/AF, 87.5% for ventricular tachycardia, 99.5% for bradycardia, and 98.8% for asystole.

Conclusion

Application of AI to ICM-detected episodes is associated with high classification accuracy and may significantly reduce health care staff workload by triaging ICM data.

Reducing the Electrogram Review Burden Imposed by Insertable Cardiac Monitors

BACKGROUND

Insertable cardiac monitors (ICMs) are essential for ambulatory arrhythmia diagnosis. However, definitive diagnoses still require time-consuming, manual adjudication of electrograms (EGMs).

OBJECTIVE

To evaluate the clinical impact of selecting only key EGMs for review.

METHODS

Retrospective analyses of randomly selected Abbott Confirm Rx^TM devices with ≥90 days of remote transmission history was performed, with each EGM adjudicated as true or false positive (TP, FP). For each device, up to 3 "key EGMs" per arrhythmia type per day were prioritized for review based on ventricular rate and episode duration. The reduction in EGMs and TP days (patient-days with at least 1 TP EGM), and any diagnostic delay (from the first TP), were calculated vs. reviewing all EGMs.

RESULTS

In 1,000 ICMs over a median duration of 8.1 months, at least one atrial fibrillation (AF), tachycardia, bradycardia, or pause EGM was transmitted by 424, 343, 190, and 325 devices, respectively, with a total of 95716 EGMs. Approximately 90% of episodes were contributed by 25% of patients. Key EGM selection reduced EGM review burden by 43%, 66%, 77%, and 50% (55% overall), while reducing TP days by 0.8%, 2.1%, 0.2%, and 0.0%, respectively. Despite reviewing fewer EGMs, 99% of devices with a TP EGM were ultimately diagnosed on the same day vs. reviewing all EGMs.

CONCLUSIONS

Key EGM selection reduced the EGM review substantially with no delay-to-diagnosis in 99% of patients exhibiting true arrhythmias. Implementing these rules in the Abbott patient care network may accelerate clinical workflow without compromising diagnostic timelines. This article is protected by copyright. All rights reserved.