Towards the prediction of rearrest during out-of-hospital cardiac arrest

Andoni Elola, Elisabete Aramendi, Enrique Rueda, Unai Irusta, Henry Wang, Ahamed Idris

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


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.

Original languageEnglish (US)
Article number758
Issue number7
StatePublished - Jul 2020


  • Electrocardiogram (ECG)
  • Heart rate variability (HRV)
  • Out-of-hospital cardiac arrest (OHCA)
  • Random forest (RF)
  • Rearrest

ASJC Scopus subject areas

  • Information Systems
  • Mathematical Physics
  • Physics and Astronomy (miscellaneous)
  • Electrical and Electronic Engineering


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