Effect of Amplitude Spectral Area on Termination of Fibrillation and Outcomes in Pediatric Cardiac Arrest

Recurrence of ventricular fibrillation in out-of-hospital cardiac arrest: Clinical evidence and underlying ionic mechanisms

Defibrillation remains the optimal therapy for terminating ventricular fibrillation (VF) in out-of-hospital cardiac arrest (OHCA) patients, with reported shock success rates of ∼90%. A key persistent challenge, however, is the high rate of VF recurrence (∼50-80%) seen during post-shock cardiopulmonary resuscitation (CPR). Studies have shown that the incidence and time spent in recurrent VF are negatively associated with neurologically-intact survival. Recurrent VF also results in the administration of extra shocks at escalating energy levels, which can cause cardiac dysfunction. Unfortunately, the mechanisms underlying recurrent VF remain poorly understood. In particular, the role of chest-compressions (CC) administered during CPR in mediating recurrent VF remains controversial. In this review, we first summarize the available clinical evidence for refibrillation occurring during CPR in OHCA patients, including the postulated contribution of CC and non-CC related pathways. Next, we examine experimental studies highlighting how CC can re-induce VF via direct mechano-electric feedback. We postulate the ionic mechanisms involved by comparison with similar phenomena seen in commotio cordis. Subsequently, the hypothesized contribution of partial cardiac reperfusion (either as a result of CC or CC independent organized rhythm) in re-initiating VF in a globally ischaemic heart is examined. An overview of the proposed ionic mechanisms contributing to VF recurrence in OHCA during CPR from a cellular level to the whole heart is outlined. Possible therapeutic implications of the proposed mechanistic theories for VF recurrence in OHCA are briefly discussed.

Machine learning model to predict evolution of pulseless electrical activity during in-hospital cardiac arrest

Background

During pulseless electrical activity (PEA) the cardiac mechanical and electrical functions are dissociated, a phenomenon occurring in 25-42% of in-hospital cardiac arrest (IHCA) cases. Accurate evaluation of the likelihood of a PEA patient transitioning to return of spontaneous circulation (ROSC) may be vital for the successful resuscitation.

The aim

We sought to develop a model to automatically discriminate between PEA rhythms with favorable and unfavorable evolution to ROSC.

Methods

A dataset of 190 patients, 120 with ROSC, were acquired with defibrillators from different vendors in three hospitals. The ECG and the transthoracic impedance (TTI) signal were processed to compute 16 waveform features. Logistic regression models where designed integrating both automated features and characteristics annotated in the QRS to identify PEAs with better prognosis leading to ROSC. Cross validation techniques were applied, both patient-specific and stratified, to evaluate the performance of the algorithm.

Results

The best model consisted in a three feature algorithm that exhibited median (interquartile range) Area Under the Curve/Balanced accuracy/Sensitivity/Specificity of 80.3(9.9)/75.6(8.0)/ 77.4(15.2)/72.3(16.4) %, respectively.

Conclusions

Information hidden in the waveforms of the ECG and TTI signals, along with QRS complex features, can predict the progression of PEA. Automated methods as the one proposed in this study, could contribute to assist in the targeted treatment of PEA in IHCA.