Combining Amplitude Spectrum Area with Previous Shock Information Using Neural Networks Improves Prediction Performance of Defibrillation Outcome for Subsequent Shocks in Out-Of-Hospital Cardiac Arrest Patients

Abstract

ObjectiveQuantitative ventricular fibrillation (VF) waveform analysis is a potentially powerful tool to optimize defibrillation. However, whether combining VF features with additional attributes that related to the previous shock could enhance the prediction performance for subsequent shocks is still uncertain.MethodsA total of 528 defibrillation shocks from 199 patients experienced out-of-hospital cardiac arrest were analyzed in this study. VF waveform was quantified using amplitude spectrum area (AMSA) from defibrillator's ECG recordings prior to each shock. Combinations of AMSA with previous shock index (PSI) or/and change of AMSA (ΔAMSA) between successive shocks were exercised through a training dataset including 255shocks from 99patientswith neural networks. Performance of the combination methods were compared with AMSA based single feature prediction by area under receiver operating characteristic curve(AUC), sensitivity, positive predictive value (PPV), negative predictive value (NPV) and prediction accuracy (PA) through a validation dataset that was consisted of 273 shocks from 100patients.ResultsA total of61 (61.0%) patients required subsequent shocks (N = 173) in the validation dataset. Combining AMSA with PSI and ΔAMSA obtained highest AUC (0.904 vs. 0.819, p<0.001) among different combination approaches for subsequent shocks. Sensitivity (76.5% vs. 35.3%, p<0.001), NPV (90.2% vs. 76.9%, p = 0.007) and PA (86.1% vs. 74.0%, p = 0.005)were greatly improved compared with AMSA based single feature prediction with a threshold of 90% specificity.ConclusionIn this retrospective study, combining AMSA with previous shock information using neural networks greatly improves prediction performance of defibrillation outcome for subsequent shocks.

Ventricular fibrillation waveform measures combined with prior shock outcome predict defibrillation success during cardiopulmonary resuscitation

“…Research has suggested that prior rhythm is associated with subsequent likelihood of return of spontaneous circulation following shock, and that this information may improve prediction when combined with waveform measures calculated during CPR-free epochs. 16,17 However little is known about whether ROR from prior shock would improve prediction when combined with waveform measures calculated during CPR. We observed that Prior ROR was associated with the response to a subsequent shock.…”

“…Research has suggested that prior rhythm and perfusion state are associated with subsequent likelihood of return of spontaneous circulation following shock, 16 and that this information in combination with waveform measures may improve prediction of shock success. 17 However, this relationship has not been tested during CPR, challenging real-world implementation of such a strategy. If a combination algorithm could more accurately predict shock success during CPR, then shock delivery might be timed based on a real-time prognostic likelihood calculated during continuous CPR.…”

Application of Entropy-Based Features to Predict Defibrillation Outcome in Cardiac Arrest

“…FuzzEn showed a decrease in BER of 3-5 points with respect to PPA, MdS and/or AMSA. Most likely, shock outcome prediction could be improved by incorporating additional information, such as the relative changes in the predictors between consecutive shocks [59], or other clinical variables, such as end tidal CO2 levels [58].…”

Adult Advanced Life Support: 2020 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science with Treatment Recommendations

“…The evidence updates restricted the search to January 2008 to January 2020 and identified one large RCT conducted in 2013 45 and 20 observational studies. 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 In addition, there is an ongoing multicentre RCT of real-time amplitude spectrum area to guide defibrillation (NCT03237910). Although the VF waveform analyses and outcomes studied were highly heterogeneous, given the amount of data available, an updated SysRev was suggested.…”