Predicting electrical storms by remote monitoring of implantable cardioverter-defibrillator patients using machine learning

Shakibfar, Saeed; Krause, Oswin; Lund-Andersen, Casper; Aranda, Alfonso; Moll, Jonas; Andersen, Tariq Osman; Svendsen, Jesper Hastrup; Petersen, Hans Draminsky; Igel, Christian

doi:10.1093/europace/euy257

Cited by 33 publications

(23 citation statements)

References 20 publications

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“…This study is based on data from 18,679 ICDs (Medtronic) implanted in the U.S.A. in the period ranging from 2005 to 2016 [8]. The device data were collected in the de-identified Medtronic DiscoveryLink database, and all patients consented to the use of their data for research purposes.…”

Section: Methodsmentioning

confidence: 99%

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Impact of device programming on the success of the first anti-tachycardia pacing therapy: An anonymized large-scale study

et al. 2019

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Background Antitachycardia pacing (ATP) is an effective treatment for ventricular tachycardia (VT). We evaluated the efficacy of different ATP programs based on a large remote monitoring data set from patients with implantable cardioverter-defibrillators (ICDs). Methods A dataset from 18,679 ICD patients was used to evaluate the first delivered ATP treatment. We considered all device programs that were used for at least 50 patients, leaving us with 7 different programs and a total of 32,045 episodes. We used the two-proportions z-test (α = 0.01) to compare the probability of success and the probability for acceleration in each group with the corresponding values of the default setting. Results Overall, the first ATP treatment terminated in 78.4%–97.5% of episodes with slow VT and 81.5%–91.1% of episodes with fast VT. The default setting of the ATP programs with the number of sequences S = 3 was applied to treat 30.1% of the slow and 36.6% of the fast episodes. Reducing the maximum number of sequences to S = 2 decreased the success rate for slow VT ( P < 0.0001, h = 0.38), while the setting S = 4 resulted in the highest success rate of 97.5% ( P < 0 . 0001 , h = 0.27). Conclusion While the default programs performed well, we found that increasing the number of sequences from 3 to 4 was a promising option to improve the overall ATP performance.

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Section: Methodsmentioning

confidence: 99%

“…The device data were collected in the de-identified Medtronic DiscoveryLink database, and all patients consented to the use of their data for research purposes. Data from the same population were used for developing a machine learning algorithm for predicting electrical storm by Shakibfar et al [8].…”

Section: Methodsmentioning

confidence: 99%

Impact of device programming on the success of the first anti-tachycardia pacing therapy: An anonymized large-scale study

et al. 2019

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“…67 A ML prediction model by Shakibfar et al based on data from remote monitoring of the ICD showed that decline in D-PA levels four days prior to the onset of electrical storm was among the most relevant features and yielded an area under the curve of 0.80. 68 Hence, the integration of accelerometer-assessed metrics among other features may lead to accurate real-time prediction of impending cardiac events at a high accuracy.…”

Section: Future Directionsmentioning

confidence: 99%

Accelerometer-assessed physical behavior and the association with clinical outcomes in implantable cardioverter-defibrillator recipients: A systematic review

Kolk

Frodi

Andersen

et al. 2022

Cardiovascular Digital Health Journal

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“…In an application of RF models, Shakibfar et al used only ICD data without clinical variables to predict risk of electrical storm. [ 48 ] They developed 37 ICD electrogram-based features found during the four consecutive days prior to the onset of electrical storm (defined using device detection). They found that their RF model had an AUC of 0.80 for predicting electrical storm.…”

Section: Cardiac Implantable Electronic Devicesmentioning

confidence: 99%

Applications of Machine Learning in Cardiac Electrophysiology

Muthalaly

Evans

2020

Arrhythmia &Amp; Electrophysiology Review

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Artificial intelligence through machine learning (ML) methods is becoming prevalent throughout the world, with increasing adoption in healthcare. Improvements in technology have allowed early applications of machine learning to assist physician efficiency and diagnostic accuracy. In electrophysiology, ML has applications for use in every stage of patient care. However, its use is still in infancy. This article will introduce the potential of ML, before discussing the concept of big data and its pitfalls. The authors review some common ML methods including supervised and unsupervised learning, then examine applications in cardiac electrophysiology. This will focus on surface electrocardiography, intracardiac mapping and cardiac implantable electronic devices. Finally, the article concludes with an overview of how ML may impact on electrophysiology in the future.

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Predicting electrical storms by remote monitoring of implantable cardioverter-defibrillator patients using machine learning

Cited by 33 publications

References 20 publications

Impact of device programming on the success of the first anti-tachycardia pacing therapy: An anonymized large-scale study

Impact of device programming on the success of the first anti-tachycardia pacing therapy: An anonymized large-scale study

Accelerometer-assessed physical behavior and the association with clinical outcomes in implantable cardioverter-defibrillator recipients: A systematic review

Applications of Machine Learning in Cardiac Electrophysiology

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