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Article

Towards the Prediction of Rearrest during Out-of-Hospital Cardiac Arrest

1
Department of Communications Engineering, University of the Basque Country, 48013 Bilbao, Spain
2
Department of Emergency Medicine, University of Texas Health Science Center, Houston, TX 77030, USA
3
Department of Emergency Medicine, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
*
Author to whom correspondence should be addressed.
A.I. serves as an unpaid volunteer on the American Heart Association National Emergency Cardiovascular Care Committee and the HeartSine, Inc. Clinical Advisory Board.
Entropy 2020, 22(7), 758; https://doi.org/10.3390/e22070758
Received: 21 May 2020 / Revised: 6 July 2020 / Accepted: 7 July 2020 / Published: 9 July 2020
A secondary arrest is frequent in patients that recover spontaneous circulation after an out-of-hospital cardiac arrest (OHCA). Rearrest events are associated to worse patient outcomes, but little is known on the heart dynamics that lead to rearrest. The prediction of rearrest could help improve OHCA patient outcomes. The aim of this study was to develop a machine learning model to predict rearrest. A random forest classifier based on 21 heart rate variability (HRV) and electrocardiogram (ECG) features was designed. An analysis interval of 2 min after recovery of spontaneous circulation was used to compute the features. The model was trained and tested using a repeated cross-validation procedure, on a cohort of 162 OHCA patients (55 with rearrest). The median (interquartile range) sensitivity (rearrest) and specificity (no-rearrest) of the model were 67.3% (9.1%) and 67.3% (10.3%), respectively, with median areas under the receiver operating characteristics and the precision–recall curves of 0.69 and 0.53, respectively. This is the first machine learning model to predict rearrest, and would provide clinically valuable information to the clinician in an automated way. View Full-Text
Keywords: out-of-hospital cardiac arrest (OHCA); rearrest; electrocardiogram (ECG); heart rate variability (HRV); random forest (RF) out-of-hospital cardiac arrest (OHCA); rearrest; electrocardiogram (ECG); heart rate variability (HRV); random forest (RF)
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MDPI and ACS Style

Elola, A.; Aramendi, E.; Rueda, E.; Irusta, U.; Wang, H.; Idris, A. Towards the Prediction of Rearrest during Out-of-Hospital Cardiac Arrest. Entropy 2020, 22, 758. https://doi.org/10.3390/e22070758

AMA Style

Elola A, Aramendi E, Rueda E, Irusta U, Wang H, Idris A. Towards the Prediction of Rearrest during Out-of-Hospital Cardiac Arrest. Entropy. 2020; 22(7):758. https://doi.org/10.3390/e22070758

Chicago/Turabian Style

Elola, Andoni; Aramendi, Elisabete; Rueda, Enrique; Irusta, Unai; Wang, Henry; Idris, Ahamed. 2020. "Towards the Prediction of Rearrest during Out-of-Hospital Cardiac Arrest" Entropy 22, no. 7: 758. https://doi.org/10.3390/e22070758

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