Abstract
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 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.
Highlights
Cardiac arrest remains a major public health problem with more than 275,000 out-of-hospital cardiac arrest (OHCA) cases treated yearly in Europe [1], and survival rates below 10% [2,3].Prompt treatment is crucial because the probability of survival decreases by 10% for every minute treatment is delayed [4,5]
Similar results were obtained for tw = 2 min, with area under receiver operating characteristics curve (AUROC) in the range of 53.7 to 66.2 and area under precision–recall curve (AUPRC) in the range of
Compared to the random forest (RF) based exclusively on heart rate variability (HRV) features that we proposed in [14], the combination of HRV and ECG features improved the AUROC and AUPRC in 2 points when we increased the number of patients in the dataset by 65%
Summary
Cardiac arrest remains a major public health problem with more than 275,000 out-of-hospital cardiac arrest (OHCA) cases treated yearly in Europe [1], and survival rates below 10% [2,3].Prompt treatment is crucial because the probability of survival decreases by 10% for every minute treatment is delayed [4,5]. Cardiac arrest remains a major public health problem with more than 275,000 out-of-hospital cardiac arrest (OHCA) cases treated yearly in Europe [1], and survival rates below 10% [2,3]. Current cardiopulmonary resuscitation (CPR) guidelines define chain of survival to ensure a prompt OHCA treatment, with five important links [6]: early recognition of the arrest, CPR with chest compressions and ventilations, rapid defibrillation, basic/advanced emergency medical treatment, and post-cardiac arrest care. The final aim of the treatment provided by the emergency medical services is to achieve the return of spontaneous circulation (ROSC), and to proceed to the last link of the chain of survival, post-arrest treatment, and transportation to hospital.
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