Abstract
BackgroundThe use of real-time feedback systems to guide rescuers during cardiopulmonary resuscitation (CPR) significantly contributes to improve adherence to published resuscitation guidelines. Recently, we designed a novel method for computing depth and rate of chest compressions relying solely on the spectral analysis of chest acceleration. That method was extensively tested in a simulated manikin scenario. The purpose of this study is to report the results of this method as tested in human out-of-hospital cardiac arrest (OHCA) cases.Materials and methodsThe algorithm was evaluated retrospectively with seventy five OHCA episodes recorded by monitor-defibrillators equipped with a CPR feedback device. The acceleration signal and the compression signal computed by the CPR feedback device were stored in each episode. The algorithm was continuously applied to the acceleration signals. The depth and rate values estimated every 2-s from the acceleration data were compared to the reference values obtained from the compression signal. The performance of the algorithm was assesed in terms of the sensitivity and positive predictive value (PPV) for detecting compressions and in terms of its accuracy through the analysis of measurement error.ResultsThe algorithm reported a global sensitivity and PPV of 99.98% and 99.79%, respectively. The median (P75) unsigned error in depth and rate was 0.9 (1.7) mm and 1.0 (1.7) cpm, respectively. In 95% of the analyzed 2-s windows the error was below 3.5 mm and 3.1 cpm, respectively.ConclusionsThe CPR feedback algorithm proved to be reliable and accurate when tested retrospectively with human OHCA episodes. A new CPR feedback device based on this algorithm could be helpful in the resuscitation field.
Highlights
According to current resuscitation guidelines, high quality cardiopulmonary resuscitation (CPR) is essential to increasing survival from out-of-hospital cardiac arrest (OHCA) [1, 2]
The algorithm was evaluated retrospectively with seventy five OHCA episodes recorded by monitor-defibrillators equipped with a CPR feedback device
A new CPR feedback device based on this algorithm could be helpful in the resuscitation field
Summary
According to current resuscitation guidelines, high quality cardiopulmonary resuscitation (CPR) is essential to increasing survival from out-of-hospital cardiac arrest (OHCA) [1, 2]. During CPR, the early and persistent application of chest compressions and ventilations to the patient artificially maintains a minimal flow of oxygenated blood This delays brain damage and generates myocardial blood flow, essential to the restoration of a perfusing rhythm. Monitoring CPR using feedback devices to guide rescuers during resuscitation attempts has been increasingly investigated in recent years. These devices measure in real-time core parameters such as compression depth and rate, helping the rescuer to correct the technique if necessary. The use of real-time feedback systems to guide rescuers during cardiopulmonary resuscitation (CPR) significantly contributes to improve adherence to published resuscitation guidelines. The purpose of this study is to report the results of this method as tested in human out-of-hospital cardiac arrest (OHCA) cases
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