Cardiology

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




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

Europace. 2019 Feb 01;21(2):268-274

Authors: Shakibfar S, Krause O, Lund-Andersen C, Aranda A, Moll J, Andersen TO, Svendsen JH, Petersen HH, Igel C

Abstract
Aims: Electrical storm (ES) is a serious arrhythmic syndrome that is characterized by recurrent episodes of ventricular arrhythmias. Electrical storm is associated with increased mortality and morbidity despite the use of implantable cardioverter-defibrillators (ICDs). Predicting ES could be essential; however, models for predicting this event have never been developed. The goal of this study was to construct and validate machine learning models to predict ES based on daily ICD remote monitoring summaries.
Methods and results: Daily ICD summaries from 19 935 patients were used to construct and evaluate two models [logistic regression (LR) and random forest (RF)] for predicting the short-term risk of ES. The models were evaluated on the parts of the data not used for model development. Random forest performed significantly better than LR (P < 0.01), achieving a test accuracy of 0.96 and an area under the curve (AUC) of 0.80 (vs. an accuracy of 0.96 and an AUC of 0.75). The percentage of ventricular pacing and the daytime activity were the most relevant variables in the RF model.
Conclusion: The use of large-scale machine learning showed that daily summaries of ICD measurements in the absence of clinical information can predict the short-term risk of ES.

PMID: 30508072 [PubMed - indexed for MEDLINE]

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