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. / Shakibfar, Saeed; Krause, Oswin; Lund-Andersen, Casper; Aranda, Alfonso; Moll, Jonas; Andersen, Tariq Osman; Svendsen, Jesper Hastrup; Petersen, Helen Høgh; Igel, Christian.
I: Europace, Bind 21, Nr. 2, 2019, s. 268–274.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - Predicting electrical storms by remote monitoring of implantable cardioverter-defibrillator patients using machine learning
AU - Shakibfar, Saeed
AU - Krause, Oswin
AU - Lund-Andersen, Casper
AU - Aranda, Alfonso
AU - Moll, Jonas
AU - Andersen, Tariq Osman
AU - Svendsen, Jesper Hastrup
AU - Petersen, Helen Høgh
AU - Igel, Christian
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
U2 - 10.1093/europace/euy257
DO - 10.1093/europace/euy257
M3 - Journal article
C2 - 30508072
VL - 21
SP - 268
EP - 274
JO - Europace
JF - Europace
SN - 1099-5129
IS - 2
ER -
ID: 209804181