Machine learning for prediction of ventricular arrhythmia episodes from intracardiac electrograms of automatic implantable cardioverter-defibrillators

Yong-Mei Cha; Itzhak Zachi Attia; Coby Metzger; Francisco Lopez-Jimenez; Nicholas Y Tan; Jessica Cruz; Gaurav A Upadhyay; Steven Mullane; Camden Harrell; Yaron Kinar; Ilya Sedelnikov; Amir Lerman; Paul A Friedman; Samuel J Asirvatham

doi:10.1016/j.hrthm.2024.05.040

Machine learning for prediction of ventricular arrhythmia episodes from intracardiac electrograms of automatic implantable cardioverter-defibrillators

Heart Rhythm. 2024 May 24:S1547-5271(24)02634-1. doi: 10.1016/j.hrthm.2024.05.040. Online ahead of print.

Affiliations

¹ Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota. Electronic address: ycha@mayo.edu.
² Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota.
³ Medial EarlySign, Hod Hasharon, Israel.
⁴ Department of Cardiology, The University of Chicago Medicine, Chicago, Illinois.
⁵ Biotronik Inc, Lake Oswego, Oregon.

PMID: 38797305
DOI: 10.1016/j.hrthm.2024.05.040

Abstract

Background: Despite effectiveness of the implantable cardioverter-defibrillator (ICD) in saving patients with life-threatening ventricular arrhythmias (VAs), the temporal occurrence of VA after ICD implantation is unpredictable.

Objective: The study aimed to apply machine learning (ML) to intracardiac electrograms (IEGMs) recorded by ICDs as a unique biomarker for predicting impending VAs.

Methods: The study included 13,516 patients who received Biotronik ICDs and enrolled in the CERTITUDE registry between January 1, 2010, and December 31, 2020. Database extraction included IEGMs from standard quarterly transmissions and VA event episodes. The processed IEGM data were pulled from device transmissions stored in a centralized Home Monitoring Service Center and reformatted into an analyzable format. Long-range (baseline or first scheduled remote recording), mid-range (scheduled remote recording every 90 days), or short-range predictions (IEGM within 5 seconds before the VA onset) were used to determine whether ML-processed IEGMs predicted impending VA events. Convolutional neural network classifiers using ResNet architecture were employed.

Results: Of 13,516 patients (male, 72%; age, 67.5 ± 11.9 years), 301,647 IEGM recordings were collected; 27,845 episodes of sustained ventricular tachycardia or ventricular fibrillation were observed in 4467 patients (33.0%). Neural networks based on convolutional neural networks using ResNet-like architectures on far-field IEGMs yielded an area under the curve of 0.83 with a 95% confidence interval of 0.79-0.87 in the short term, whereas the long-range and mid-range analyses had minimal predictive value for VA events.

Conclusion: In this study, applying ML to ICD-acquired IEGMs predicted impending ventricular tachycardia or ventricular fibrillation events seconds before they occurred, whereas midterm to long-term predictions were not successful. This could have important implications for future device therapies.

Keywords: Artificial intelligence; Implantable cardioverter-defibrillator; Machine learning; Sudden cardiac death; Ventricular fibrillation; Ventricular tachycardia.