Association of amplitude spectral area of the ventricular fibrillation waveform with survival of out-of-hospital ventricular fibrillation cardiac arrest

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.

Amplitude spectral area of ventricular fibrillation can discriminate survival of patients with out-of-hospital cardiac arrest

Background

Evidence of the association between AMplitude Spectral Area (AMSA) of ventricular fibrillation and outcome after out-of-hospital cardiac arrest (OHCA) is limited to short-term follow-up. In this study, we assess whether AMSA can stratify the risk of death or poor neurological outcome at 30 days and 1 year after OHCA in patients with an initial shockable rhythm or with an initial non-shockable rhythm converted to a shockable one.

Methods

This is a multicentre retrospective study of prospectively collected data in two European Utstein-based OHCA registries. We included all cases of OHCAs with at least one manual defibrillation. AMSA values were calculated after data extraction from the monitors/defibrillators used in the field by using a 2-s pre-shock electrocardiogram interval. The first detected AMSA value, the maximum value, the average value, and the minimum value were computed, and their outcome prediction accuracy was compared. Multivariable Cox regression models were run for both 30-day and 1-year deaths or poor neurological outcomes. Neurological cerebral performance category 1-2 was considered a good neurological outcome.

Results

Out of the 578 patients included, 494 (85%) died and 10 (2%) had a poor neurological outcome at 30 days. All the AMSA values considered (first value, maximum, average, and minimum) were significantly higher in survivors with good neurological outcome at 30 days. The average AMSA showed the highest area under the receiver operating characteristic curve (0.778, 95% CI: 0.7-0.8, p < 0.001). After correction for confounders, the highest tertiles of average AMSA (T3 and T2) were significantly associated with a lower risk of death or poor neurological outcome compared with T1 both at 30 days (T2: HR 0.6, 95% CI: 0.4-0.9, p = 0.01; T3: HR 0.6, 95% CI: 0.4-0.9, p = 0.02) and at 1 year (T2: HR 0.6, 95% CI: 0.4-0.9, p = 0.01; T3: HR 0.6, 95% CI: 0.4-0.9, p = 0.01). Among survivors at 30 days, a higher AMSA was associated with a lower risk of mortality or poor neurological outcome at 1 year (T3: HR 0.03, 95% CI: 0-0.3, p = 0.02).

Discussion

Lower AMSA values were significantly and independently associated with the risk of death or poor neurological outcome at 30 days and at 1 year in OHCA patients with either an initial shockable rhythm or a conversion rhythm from non-shockable to shockable. The average AMSA value had the strongest association with prognosis.

Amplitude spectral area of ventricular fibrillation and defibrillation success at low energy in out-of-hospital cardiac arrest

The optimal energy for defibrillation has not yet been identified and very often the maximum energy is delivered. We sought to assess whether amplitude spectral area (AMSA) of ventricular fibrillation (VF) could predict low energy level defibrillation success in out-of-hospital cardiac arrest (OHCA) patients. This is a multicentre international study based on retrospective analysis of prospectively collected data. We included all OHCAs with at least one manual defibrillation. AMSA values were calculated by analyzing the data collected by the monitors/defibrillators used in the field (Corpuls 3 and Lifepak 12/15) and using a 2-s-pre-shock electrocardiogram interval. We run two different analyses dividing the shocks into three tertiles (T1, T2, T3) based on AMSA values. 629 OHCAs were included and 2095 shocks delivered (energy ranging from 100 to 360 J; median 200 J). Both in the "extremes analysis" and in the "by site analysis", the AMSA values of the effective shocks at low energy were significantly higher than those at high energy (p = 0.01). The likelihood of shock success increased significantly from the lowest to the highest tertile. After correction for age, call to shock time, use of mechanical CPR, presence of bystander CPR, sex and energy level, high AMSA value was directly associated with the probability of shock success [T2 vs T1 OR 3.8 (95% CI 2.5-6) p < 0.001; T3 vs T1 OR 12.7 (95% CI 8.2-19.2), p < 0.001]. AMSA values are associated with the probability of low-energy shock success so that they could guide energy optimization in shockable cardiac arrest patients.

Instantaneous amplitude: Association of ventricular fibrillation waveform measures at time of shock with outcome in out-of-hospital cardiac arrest

BACKGROUND

Prompt defibrillation is key to successful resuscitation from ventricular fibrillation out-of-hospital cardiac arrest (VF-OHCA). Preliminary evidence suggests that the timing of shock relative to the amplitude of the VF ECG waveform may affect the likelihood of resuscitation. We investigated whether the VF waveform amplitude at the time of shock (instantaneous amplitude) predicts outcome independent of other validated waveform measures.

METHODS

We conducted a retrospective study of VF-OHCA patients ≥18 old. We evaluated three VF waveform measures for each shock: instantaneous amplitude at the time of shock, and maximum amplitude and amplitude spectrum area (AMSA) over a 3-s window preceding the shock. Linear mixed-effects modeling was used to determine whether instantaneous amplitude was associated with shock-specific return of organized rhythm (ROR) or return of spontaneous circulation (ROSC) independent of maximum amplitude or AMSA.

RESULTS

The 566 eligible patients received 1513 shocks, resulting in ROR of 62.0% (938/1513) and ROSC of 22.3% (337/1513). In unadjusted regression, an interquartile increase in instantaneous amplitude was associated with ROR (Odds ratio [OR] [95% confidence interval] = 1.27 [1.11-1.45]) and ROSC (OR = 1.27 [1.14-1.42]). However, instantaneous amplitude was not associated with ROR (OR = 1.13 [0.97-1.30]) after accounting for maximum amplitude, nor with ROR (OR = 1.00 [0.87-1.15]) or ROSC (OR = 1.05 [0.93-1.18]) after accounting for AMSA. By contrast, AMSA and maximum amplitude remained independently associated with ROR and ROSC.

CONCLUSIONS

We did not observe an independent association between instantaneous amplitude and shock-specific outcomes. Efforts to time shock to the maximal amplitude of the VF waveform are unlikely to affect resuscitation outcome.

Real-time amplitude spectrum area estimation during chest compression from the ECG waveform using a 1D convolutional neural network

Introduction: Amplitude spectrum area (AMSA) is a well-established measure than can predict defibrillation outcome and guiding individualized resuscitation of ventricular fibrillation (VF) patients. However, accurate AMSA can only be calculated during cardiopulmonary resuscitation (CPR) pause due to artifacts produced by chest compression (CC). In this study, we developed a real-time AMSA estimation algorithm using a convolutional neural network (CNN). Methods: Data were collected from 698 patients, and the AMSA calculated from the uncorrupted signals served as the true value for both uncorrupted and the adjacent corrupted signals. An architecture consisting of a 6-layer 1D CNN and 3 fully connected layers was developed for AMSA estimation. A 5-fold cross-validation procedure was used to train, validate and optimize the algorithm. An independent testing set comprised of simulated data, real-life CC corrupted data, and preshock data was used to evaluate the performance. Results: The mean absolute error, root mean square error, percentage root mean square difference and correlation coefficient were 2.182/1.951 mVHz, 2.957/2.574 mVHz, 22.887/28.649% and 0.804/0.888 for simulated and real-life testing data, respectively. The area under the receiver operating characteristic curve regarding predicting defibrillation success was 0.835, which was comparable to that of 0.849 using the true value of the AMSA. Conclusions: AMSA can be accurately estimated during uninterrupted CPR using the proposed method.

Targeted Delivery of Electrical Shocks and Epinephrine, Guided by Ventricular Fibrillation Amplitude Spectral Area, Reduces Electrical and Adrenergic Myocardial Burden, Improving Survival in Swine

Background

We previously reported that resuscitation delivering electrical shocks guided by real‐time ventricular fibrillation amplitude spectral area (AMSA) enabled return of spontaneous circulation (ROSC) with fewer shocks, resulting in less myocardial dysfunction. We now hypothesized that AMSA could also guide delivery of epinephrine, expecting further outcome improvement consequent to less electrical and adrenergic burdens.

Methods and Results

A swine model of ventricular fibrillation was used to compare after 10 minutes of untreated ventricular fibrillation a guidelines‐driven (n=8) resuscitation protocol, delivering shocks every 2 minutes and epinephrine every 4 minutes, with an AMSA‐driven shocks (n=8) protocol, delivering epinephrine every 4 minutes, and with an AMSA‐driven shocks and epinephrine (ADSE; n=8) protocol. For guidelines‐driven, AMSA‐driven shocks, and ADSE protocols, the time to ROSC (mean±SD) was 569±164, 410±111, and 400±80 seconds (P=0.045); the number of shocks (mean±SD) was 5±2, 3±1, and 3±2 (P=0.024) with ADSE fewer than guidelines‐driven (P=0.03); and the doses of epinephrine (median [interquartile range]) were 2.0 (1.3–3.0), 1.0 (1.0–2.8), and 1.0 (0.3–3.0) (P=0.419). The ROSC rate was similar, yet survival after ROSC favored AMSA‐driven protocols (guidelines‐driven, 3/6; AMSA‐driven shocks, 6/6; and ADSE, 7/7; P=0.019 by log‐rank test). Left ventricular function and survival after ROSC correlated inversely with electrical burden (ie, cumulative unsuccessful shocks, J/kg; P=0.020 and P=0.046) and adrenergic burden (ie, total epinephrine doses, mg/kg; P=0.042 and P=0.002).

Conclusions

Despite similar ROSC rates achieved with all 3 protocols, AMSA‐driven shocks and ADSE resulted in less postresuscitation myocardial dysfunction and better survival, attributed to attaining ROSC with less electrical and adrenergic myocardial burdens.

The effect of the localisation of an underlying ST-elevation myocardial infarction on the VF-waveform: A multi-centre cardiac arrest study

INTRODUCTION

In cardiac arrest, ventricular fibrillation (VF) waveform characteristics such as amplitude spectrum area (AMSA) are studied to identify an underlying myocardial infarction (MI). Observational studies report lower AMSA-values in patients with than without underlying MI. Moreover, experimental studies with 12-lead ECG-recordings show lowest VF-characteristics when the MI-localisation matches the ECG-recording direction. However, out-of-hospital cardiac arrest (OHCA)-studies with defibrillator-derived VF-recordings are lacking.

METHODS

Multi-centre (Amsterdam/Nijmegen, the Netherlands) cohort-study on the association between AMSA, ST-elevation MI (STEMI) and its localisation. AMSA was calculated from defibrillator pad-ECG recordings (proxy for lead II, inferior vantage point); STEMI-localisation was determined using ECG/angiography/autopsy findings.

RESULTS

We studied AMSA-values in 754 OHCA-patients. There were statistically significant differences between no STEMI, anterior STEMI and inferior STEMI (Nijmegen: no STEMI 13.0mVHz [7.9-18.6], anterior STEMI 7.5mVHz [5.6-13.8], inferior STEMI 7.5mVHz [5.4-11.8], p = 0.006. Amsterdam: 11.7mVHz [5.0-21.9], 9.6mVHz [4.6-17.2], and 6.9mVHz [3.2-16.0], respectively, p = 0.001). Univariate analyses showed significantly lower AMSA-values in inferior STEMI vs. no STEMI; there was no significant difference between anterior and no STEMI. After correction for confounders, adjusted absolute AMSA-values were numerically lowest for inferior STEMI in both cohorts, and the relative differences in AMSA between inferior and no STEMI was 1.4-1.7 times larger than between anterior and no STEMI.

CONCLUSION

This multi-centre VF-waveform OHCA-study showed significantly lower AMSA in case of underlying STEMI, with a more pronounced difference for inferior than for anterior STEMI. Confirmative studies on the impact of STEMI-localisation on the VF-waveform are warranted, and might contribute to earlier diagnosis of STEMI during VF.

[Adult advanced life support]

These European Resuscitation Council Advanced Life Support guidelines are based on the 2020 International Consensus on Cardiopulmonary Resuscitation Science with Treatment Recommendations. This section provides guidelines on the prevention of and ALS treatments for both in-hospital cardiac arrest and out-of-hospital cardiac arrest.

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

Background

Amplitude spectral area (AMSA) predicts termination of fibrillation (TOF) with return of spontaneous circulation (ROSC) and survival in adults but has not been studied in pediatric cardiac arrest. We characterized AMSA during pediatric cardiac arrest from a Pediatric Resuscitation Quality Collaborative and hypothesized that AMSA would be associated with TOF and ROSC.

Methods and Results

Children aged <18 years with cardiac arrest and ventricular fibrillation were studied. AMSA was calculated for 2 seconds before shock and averaged for each subject (AMSA‐avg). TOF was defined as termination of ventricular fibrillation 10 seconds after defibrillation to any non‐ventricular fibrillation rhythm. ROSC was defined as >20 minutes without chest compressions. Univariate and multivariable logistic regression analyses controlling for weight, current, and illness category were performed. Primary end points were TOF and ROSC. Secondary end points were 24‐hour survival and survival to discharge. Between 2015 and 2019, 50 children from 14 hospitals with 111 shocks were identified. In univariate analyses AMSA was not associated with TOF and AMS‐Aavg was not associated with ROSC. Multivariable logistic regression showed no association between AMSA and TOF but controlling for defibrillation average current and illness category, there was a trend to significant association between AMSA‐avg and ROSC (odds ratio, 1.10 [1.00‒1.22] P=0.058). There was no significant association between AMSA‐avg and 24‐hour survival or survival to hospital discharge.

Conclusions

In pediatric patients, AMSA was not associated with TOF, whereas AMSA‐avg had a trend to significance for association in ROSC, but not 24‐hour survival or survival to hospital discharge.

Registration

URL: https://www.clinicaltrials.gov; Unique identifier: NCT02708134.

Optimizing defibrillation during cardiac arrest

PURPOSE OF REVIEW

Current cardiac arrest guidelines are based on a fixed, time-based defibrillation strategy. Rhythm analysis and shock delivery (if indicated) are repeated every 2 min requiring cyclical interruptions of chest compressions. This approach has several downsides, such as the need to temporarily stop cardiopulmonary resuscitation (CPR) for a variable amount of time, thus reducing myocardial perfusion and decreasing the chance of successful defibrillation. A tailored defibrillation strategy should identify treatment priority for each patient, that is chest compressions (CCS) or defibrillation, minimize CCs interruptions, speed up the delivery of early effective defibrillation and reduce the number of ineffective shocks.

RECENT FINDINGS

Real-time ECG analysis (using adaptive filters, new algorithms robust to chest compressions artifacts and shock-advisory algorithms) is an effective strategy to correctly identify heart rhythm during CPR and reduce the hands-off time preceding a shock. Similarly, ventricular fibrillation waveform analysis, that is amplitude spectrum area (AMSA) represents a well established approach to reserve defibrillation in patients with high chance of shock success and postpone it when ventricular fibrillation termination is unlikely. Both approaches demonstrated valuable results in improving cardiac arrest outcomes in experimental and observational study.

SUMMARY

Real-time ECG analysis and AMSA have the potential to predict ventricular fibrillation termination, return of spontaneous circulation and even survival, with discretely high confidence. Prospective studies are now necessary to validate these new approaches in the clinical scenario.

Electrocardiographic recording direction impacts ventricular fibrillation waveform measurements: A potential pitfall for VF-waveform guided defibrillation protocols

Aim

In cardiac arrest, ventricular fibrillation (VF) waveform analysis has identified the amplitude spectrum area (AMSA) as a key predictor of defibrillation success and favorable neurologic survival. New resuscitation protocols are under investigation, where prompt defibrillation is restricted to cases with a high AMSA. Appreciating the variability of in-field pad placement, we aimed to assess the impact of recording direction on AMSA-values, and the inherent defibrillation advice.

Methods

Prospective VF-waveform study on 12-lead surface electrocardiograms (ECGs) obtained during defibrillation testing in ICD-recipients (2010–2017). AMSA-values (mVHz) of simultaneous VF-recordings were calculated and compared between all limb leads, with lead II as reference (proxy for in-field pad position). AMSA-differences between leads I and II were quantified using Bland-Altman analysis. Moreover, we investigated differences between these adjacent leads regarding classification into high (≥15.5), intermediate (6.5–15.5) or low (≤6.5) AMSA-values.

Results

In this cohort (n = 243), AMSA-values in lead II (10.2 ± 4.8) differed significantly from the other limb leads (I: 8.0 ± 3.4; III: 12.9 ± 5.6, both p < 0.001). The AMSA-value in lead I was, on average, 2.24 ± 4.3 lower than in lead II. Of the subjects with high AMSA-values in lead II, only 15% were classified as high if based on assessments of lead I. For intermediate and low AMSA-values, concordances were 66% and 72% respectively.

Conclusions

ECG-recording direction markedly affects the result of VF-waveform analysis, with 20–30% lower AMSA-values in lead I than in lead II. Our data suggest that electrode positioning may significantly impact shock guidance by ‘smart defibrillators’, especially affecting the advice for prompt defibrillation.

Estimating the amplitude spectrum area of ventricular fibrillation during cardiopulmonary resuscitation using only ECG waveform

Background

Amplitude spectrum area (AMSA) calculated from ventricular fibrillation (VF) can be used to monitor the effectiveness of chest compression (CC) and optimize the timing of defibrillation. However, reliable AMSA can only be obtained during CC pause because of artifacts. In this study, we sought to develop a method for estimating AMSA during cardiopulmonary resuscitation (CPR) using only the electrocardiogram (ECG) waveform.

Methods

Intervals of 8 seconds ECG and CC-related references, including 4 seconds during CC and an adjacent 4 seconds without CC, were collected before 1,008 defibrillation shocks from 512 out-of-hospital cardiac arrest patients. Signal quality was analyzed based on the irregularity of autocorrelation of VF. If signal quality index (SQI) was high, AMSA would be calculated from the original signal. Otherwise, CC-related artifacts would be constructed and suppressed using the least mean square filter from VF before calculation of AMSA. The algorithm was optimized using 480 training shocks and evaluated using 528 independent testing shocks.

Results

Overall, CC resulted in lower SQI [0.15 (0.04-0.61) with CC vs. 0.75 (0.61-0.83) without CC, P<0.01] and higher AMSA [11.2 (7.7-16.2) with CC vs. 7.2 (4.9-10.6) mVHz without CC, P<0.01] values. The predictive accuracy (49.2% vs. 66.5%, P<0.01) and area under the receiver operating characteristic curve (AUC) (0.647 vs. 0.734, P<0.01) were significantly decreased during CC. Using the proposed method, the estimated AMSA was 7.1 (5.0-15.2) mVHz, the predictive accuracy was 67.0% and the AUC was 0.713, which were all comparable with those calculated without CC.

Conclusions

Using the signal quality-based artifact suppression method, AMSA can be reliably estimated and continuously monitored during CPR.

European Resuscitation Council Guidelines 2021: Adult advanced life support

These European Resuscitation Council Advanced Life Support guidelines, are based on the 2020 International Consensus on Cardiopulmonary Resuscitation Science with Treatment Recommendations. This section provides guidelines on the prevention of and ALS treatments for both in-hospital cardiac arrest and out-of-hospital cardiac arrest.

Time boundaries of the three-phase time-sensitive model for ventricular fibrillation cardiac arrest

Aim

Ventricular fibrillation (VF) cardiac arrest may consist of three time-sensitive phases: electrical, circulatory, and metabolic. However, the time boundaries of these phases are unclear. We aimed to determine the time boundaries of the three-phase model for VF cardiac arrest.

Methods

We reviewed 20,741 out-of-hospital cardiac arrest cases with initial VF and presumed cardiac origin from the All-Japan Utstein-style registry between 2013 and 2017. The study endpoint was 1-month neurologically intact survival. The collapse-to-shock interval was defined as the time from collapse to the first shock delivery by emergency medical service personnel. The patients were divided into the bystander cardiopulmonary resuscitation (CPR, n = 11,606) and non-bystander CPR (n = 9135) groups.

Results

In the bystander CPR group, the collapse-to-shock times that were associated with increased adjusted 1-month neurologically intact survival, compared with those in the non-bystander CPR group, ranged from 7 min (42.9% [244/4999] vs. 26.0% [119/458], adjusted odds ratio [aOR], 1.95; 95% confidence interval [CI], 1.44-2.63; P < 0.0001) to 17 min (17.1% [70/410] vs. 7.3% [21/288], aOR, 2.82; 95% CI, 1.62-4.91; P = 0.0002). However, the neurologically intact survival rate of the bystander CPR group was statistically insignificant compared with that of the non-bystander CPR group when the collapse-to-shock time was outside this range.

Conclusions

The time boundaries of the three-phase time-sensitive model for VF cardiac arrest may be defined as follows: electrical phase, from collapse to <7 min; circulatory phase, from 7 to 17 min; and metabolic phase, from >17 min onward.

MLWAVE: A novel algorithm to classify primary versus secondary asphyxia-associated ventricular fibrillation

INTRODUCTION/HYPOTHESIS

The outcome of cardiopulmonary resuscitation (CPR) depends on timely recognition of the underlying cause of cardiac arrest. Ventricular fibrillation (VF) waveform analysis to differentiate primary VF from secondary asphyxia-associated VF may allow tailoring of therapies to improve cardiac arrest outcomes. Therefore, the primary goal of this investigation was to develop a novel technique utilizing wavelet synchrosqueezed transform (WSST) and decision-tree classifier that was specifically adapted to discriminate between these two incidents of VF.

METHODS

Secondary analytical investigation of electrocardiography (ECG) data obtained from swine models of either primary VF (n=18) or secondary asphyxia-associated VF (7min of asphyxia prior to VF induction; n=12). In the primary analysis, WSST technique was applied to the first 35s of the VF ECG signal to identify the most differentiating characteristics of the signal for use as features to develop a machine learning algorithm to classify the arrest as either primary VF vs. secondary asphyxia-associated VF. The performance of this new interactive Machine Learning algorithm with Wavelet Energy features of ECG (MLWAVE) was assessed using both classification accuracy and area under the receiver operating characteristic curve (AUCROC). To evaluate the validity of the new technique, the amplitude spectrum area (AMSA)-based technique, a well-established defibrillation classification method, was also applied to the same ECG signals. The classification accuracy and AUCROC were then compared between the two techniques.

RESULTS

For the primary analysis evaluating the first 35s of the VF waveform, the MLWAVE technique classified the type of VF with high accuracy (28/28 [100%], AUCROC: 1.00). The MLWAVE technique performed better than the AMSA technique across all comparisons, but given the small sample sizes, differences were not statistically significant (accuracy: 100% vs. 85.7%; p=0.24; AUCROC: 1.00 vs. 0.82; p=0.24).

CONCLUSION

This analytical investigation illustrates the advantages of the MLWAVE signal processing method which was associated with 100% accuracy in classifying the type of VF waveform: primary vs. asphyxia-associated. Such classification could lead to personalized tailoring of resuscitation (e.g., immediate defibrillation vs. continued CPR and treatment of reversible cardiac arrest causes before defibrillation) to improve outcomes for cardiac arrest.

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

This 2020 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science With Treatment Recommendations for advanced life support includes updates on multiple advanced life support topics addressed with 3 different types of reviews. Topics were prioritized on the basis of both recent interest within the resuscitation community and the amount of new evidence available since any previous review. Systematic reviews addressed higher-priority topics, and included double-sequential defibrillation, intravenous versus intraosseous route for drug administration during cardiac arrest, point-of-care echocardiography for intra-arrest prognostication, cardiac arrest caused by pulmonary embolism, postresuscitation oxygenation and ventilation, prophylactic antibiotics after resuscitation, postresuscitation seizure prophylaxis and treatment, and neuroprognostication. New or updated treatment recommendations on these topics are presented. Scoping reviews were conducted for anticipatory charging and monitoring of physiological parameters during cardiopulmonary resuscitation. Topics for which systematic reviews and new Consensuses on Science With Treatment Recommendations were completed since 2015 are also summarized here. All remaining topics reviewed were addressed with evidence updates to identify any new evidence and to help determine which topics should be the highest priority for systematic reviews in the next 1 to 2 years.

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

This 2020 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science With Treatment Recommendations for advanced life support includes updates on multiple advanced life support topics addressed with 3 different types of reviews. Topics were prioritized on the basis of both recent interest within the resuscitation community and the amount of new evidence available since any previous review. Systematic reviews addressed higher-priority topics, and included double-sequential defibrillation, intravenous versus intraosseous route for drug administration during cardiac arrest, point-of-care echocardiography for intra-arrest prognostication, cardiac arrest caused by pulmonary embolism, postresuscitation oxygenation and ventilation, prophylactic antibiotics after resuscitation, postresuscitation seizure prophylaxis and treatment, and neuroprognostication. New or updated treatment recommendations on these topics are presented. Scoping reviews were conducted for anticipatory charging and monitoring of physiological parameters during cardiopulmonary resuscitation. Topics for which systematic reviews and new Consensuses on Science With Treatment Recommendations were completed since 2015 are also summarized here. All remaining topics reviewed were addressed with evidence updates to identify any new evidence and to help determine which topics should be the highest priority for systematic reviews in the next 1 to 2 years.

Computerized Analysis of the Ventricular Fibrillation Waveform Allows Identification of Myocardial Infarction: A Proof-of-Concept Study for Smart Defibrillator Applications in Cardiac Arrest

Background

In cardiac arrest, computerized analysis of the ventricular fibrillation (VF) waveform provides prognostic information, while its diagnostic potential is subject of study. Animal studies suggest that VF morphology is affected by prior myocardial infarction (MI), and even more by acute MI. This experimental in‐human study reports on the discriminative value of VF waveform analysis to identify a prior MI. Outcomes may provide support for in‐field studies on acute MI.

Methods and Results

We conducted a prospective registry of implantable cardioverter defibrillator recipients with defibrillation testing (2010–2014). From 12‐lead surface ECG VF recordings, we calculated 10 VF waveform characteristics. First, we studied detection of prior MI with lead II, using one key VF characteristic (amplitude spectrum area [AMSA]). Subsequently, we constructed diagnostic machine learning models: model A, lead II, all VF characteristics; model B, 12‐lead, AMSA only; and model C, 12‐lead, all VF characteristics. Prior MI was present in 58% (119/206) of patients. The approach using the AMSA of lead II demonstrated a C‐statistic of 0.61 (95% CI, 0.54–0.68). Model A performance was not significantly better: 0.66 (95% CI, 0.59–0.73), P=0.09 versus AMSA lead II. Model B yielded a higher C‐statistic: 0.75 (95% CI, 0.68–0.81), P<0.001 versus AMSA lead II. Model C did not improve this further: 0.74 (95% CI, 0.67–0.80), P=0.66 versus model B.

Conclusions

This proof‐of‐concept study provides the first in‐human evidence that MI detection seems feasible using VF waveform analysis. Information from multiple ECG leads rather than from multiple VF characteristics may improve diagnostic accuracy. These results require additional experimental studies and may serve as pilot data for in‐field smart defibrillator studies, to try and identify acute MI in the earliest stages of cardiac arrest.

Long-Lasting Ventricular Fibrillation in Humans ECG Characteristics and Effect of Radiofrequency Ablation

BACKGROUND

Studies of ventricular fibrillation (VF) in humans are limited because of the short available duration. We sought to study surface ECG waveforms and effect of ablation in long-lasting VF in patients with left assist devices.

METHODS

Continuous 12-lead ECG of 5 episodes of long-lasting VF occurring in 3 patients with left ventricular assist device were analyzed. Spectral analysis (dominant frequency) and quantification of waveform amplitude, regularity (Unbiased Regularity Index), and complexity (Nondipolar Index) were performed over a median of 24 minutes of VF. Radiofrequency ablation was performed during VF in 2 patients.

RESULTS

There was a significant increase in dominant frequency between VF onset and termination but none of the other parameters significantly changed. Some VF parameters varied from patient to patient and from lead to lead. Dominant frequency decreased after radiofrequency ablation in both cases and VF terminated spontaneously shortly after ablation in one case. The previously incessant VFs in these 2 patients did not recur afterward.

CONCLUSIONS

VF rate increases over time in patients with left ventricular assist devices and is lowered by ablation. Long-lasting VF may be modified or even terminated by ablation.

Invasive Management of Out of Hospital Cardiac Arrest

Out of hospital cardiac arrest (OHCA) is a major cause of morbidity and mortality worldwide. Clinical decision making is extremely difficult in this understudied patient population with high prevalence of neurological injury and inexorable shock states. As such, there are uncertain benefits from therapies available in the cardiac catheterization laboratory. Fear of futility and public reporting often affects decision making and can result in risk aversion. This review focuses on invasive management in OHCA care, with particular focus on coronary angiography, coronary revascularization, and mechanical support. Guidelines recommend emergency coronary angiography in patients with ST-segment elevations on ECG after OHCA, while the role of coronary angiography in patients without ST-segment elevations is less clear. Similar uncertainty remains in the appropriate revascularization strategy in these patients. As in other areas of cardiology, there is a growing interest in the role of mechanical circulatory support after OHCA, though the available literature shows mixed results. The many uncertainties associated with treating the patient with OHCA highlight the importance of clinical decision support tools and treatment algorithms in the care of this population. This review focuses on invasive management in OHCA care, with particular focus on coronary angiography, coronary revascularization, and mechanical support.

Value of capnography to predict defibrillation success in out-of-hospital cardiac arrest

BACKGROUND AND AIM

Unsuccessful defibrillation shocks adversely affect survival from out-of-hospital cardiac arrest (OHCA). Ventricular fibrillation (VF) waveform analysis is the tool-of-choice for the non-invasive prediction of shock success, but surrogate markers of perfusion like end-tidal CO2 (EtCO2) could improve the prediction. The aim of this study was to evaluate EtCO2 as predictor of shock success, both individually and in combination with VF-waveform analysis.

MATERIALS AND METHODS

In total 514 shocks from 214 OHCA patients (75 first shocks) were analysed. For each shock three predictors of defibrillation success were automatically calculated from the device files: two VF-waveform features, amplitude spectrum area (AMSA) and fuzzy entropy (FuzzyEn), and the median EtCO2 (MEtCO2) in the minute before the shock. Sensitivity, specificity, receiver operating characteristic (ROC) curves and area under the curve (AUC) were calculated, for each predictor individually and for the combination of MEtCO2 and VF-waveform predictors. Separate analyses were done for first shocks and all shocks.

RESULTS

MEtCO2 in first shocks was significantly higher for successful than for unsuccessful shocks (31mmHg/25mmHg, p<0.05), but differences were not significant for all shocks (32mmHg/29mmHg, p>0.05). MEtCO2 predicted shock success with an AUC of 0.66 for first shocks, but was not a predictor for all shocks (AUC 0.54). AMSA and FuzzyEn presented AUCs of 0.76 and 0.77 for first shocks, and 0.75 and 0.75 for all shocks. For first shocks, adding MEtCO2 improved the AUC of AMSA and FuzzyEn to 0.79 and 0.83, respectively.

CONCLUSIONS

MEtCO2 predicted defibrillation success only for first shocks. Adding MEtCO2 to VF-waveform analysis in first shocks improved prediction of shock success. VF-waveform features and MEtCO2 were automatically calculated from the device files, so these methods could be introduced in current defibrillators adding only new software.

Fuzzy and Sample Entropies as Predictors of Patient Survival Using Short Ventricular Fibrillation Recordings during out of Hospital Cardiac Arrest

Optimal defibrillation timing guided by ventricular fibrillation (VF) waveform analysis would contribute to improved survival of out-of-hospital cardiac arrest (OHCA) patients by minimizing myocardial damage caused by futile defibrillation shocks and minimizing interruptions to cardiopulmonary resuscitation. Recently, fuzzy entropy (FuzzyEn) tailored to jointly measure VF amplitude and regularity has been shown to be an efficient defibrillation success predictor. In this study, 734 shocks from 296 OHCA patients (50 survivors) were analyzed, and the embedding dimension (m) and matching tolerance (r) for FuzzyEn and sample entropy (SampEn) were adjusted to predict defibrillation success and patient survival. Entropies were significantly larger in successful shocks and in survivors, and when compared to the available methods, FuzzyEn presented the best prediction results, marginally outperforming SampEn. The sensitivity and specificity of FuzzyEn were 83.3% and 76.7% when predicting defibrillation success, and 83.7% and 73.5% for patient survival. Sensitivities and specificities were two points above those of the best available methods, and the prediction accuracy was kept even for VF intervals as short as 2s. These results suggest that FuzzyEn and SampEn may be promising tools for optimizing the defibrillation time and predicting patient survival in OHCA patients presenting VF.

Early prognostic value of an Algorithm based on spectral Variables of Ventricular fibrillAtion from the EKG of patients with suddEn cardiac death: A multicentre observational study (AWAKE)

OBJECTIVE

Ventricular fibrillation (VF)-related sudden cardiac death (SCD) is a leading cause of mortality and morbidity. Current biological and imaging parameters show significant limitations on predicting cerebral performance at hospital admission. The AWAKE study (NCT03248557) is a multicentre observational study to validate a model based on spectral ECG analysis to early predict cerebral performance and survival in resuscitated comatose survivors.

METHODS

Data from VF ECG tracings of patients resuscitated from SCD will be collected using an electronic Case Report Form. Patients can be either comatose (Glasgow Coma Scale - GCS - ≤8) survivors undergoing temperature control after return of spontaneous circulation (RoSC), or those who regain consciousness (GCS=15) after RoSC; all admitted to Intensive Cardiac Care Units in 4 major university hospitals. VF tracings prior to the first direct current shock will be digitized and analyzed to derive spectral data and feed a predictive model to estimate favorable neurological performance (FNP). The results of the model will be compared to the actual prognosis.

RESULTS

The primary clinical outcome is FNP during hospitalization. Patients will be categorized into 4 subsets of neurological prognosis according to the risk score obtained from the predictive model. The secondary clinical outcomes are survival to hospital discharge, and FNP and survival after 6 months of follow-up. The model-derived categorisation will be also compared with clinical variables to assess model sensitivity, specificity, and accuracy.

CONCLUSIONS

A model based on spectral analysis of VF tracings is a promising tool to obtain early prognostic data after SCD.

Effects of intra-resuscitation antiarrhythmic administration on rearrest occurrence and intra-resuscitation ECG characteristics in the ROC ALPS trial

BACKGROUND

Intra-resuscitation antiarrhythmic drugs may improve resuscitation outcomes, in part by avoiding rearrest, a condition associated with poor out-of-hospital cardiac arrest (OHCA) outcomes. However, antiarrhythmics may also alter defibrillation threshold. The objective of this study was to investigate the relationship between rearrest and intra-resuscitation antiarrhythmic drugs in the context of the Resuscitation Outcomes Consortium (ROC) amiodarone, lidocaine, and placebo (ALPS) trial.

HYPOTHESIS

Rearrest rates would be lower in cases treated with amiodarone or lidocaine, versus saline placebo, prior to first return of spontaneous circulation (ROSC). We also hypothesized antiarrhythmic effects would be quantifiable through analysis of the prehospital electrocardiogram.

METHODS

We conducted a secondary analysis of the ROC ALPS trial. Cases that first achieved prehospital ROSC after randomized administration of study drug were included in the analysis. Rearrest, defined as loss of pulses following ROSC, was ascertained from emergency medical services records. Rearrest rate was calculated overall, as well as by ALPS treatment group. Multivariable logistic regression models were constructed to assess the association between treatment group and rearrest, as well as rearrest and both survival to hospital discharge and survival with neurologic function. Amplitude spectrum area, median slope, and centroid frequency of the ventricular fibrillation (VF) ECG were calculated and compared across treatment groups.

RESULTS

A total of 1144 (40.4%) cases with study drug prior to first ROSC were included. Rearrest rate was 44.0% overall; 42.9% for placebo, 45.7% for lidocaine, and 43.0% for amiodarone. In multivariable logistic regression models, ALPS treatment group was not associated with rearrest, though rearrest was associated with poor survival and neurologic outcomes. AMSA and median slope measures of the first available VF were associated with rearrest case status, while median slope and centroid frequency were associated with ALPS treatment group.

CONCLUSION

Rearrest rates did not differ between antiarrhythmic and placebo treatment groups. ECG waveform characteristics were correlated with treatment group and rearrest. Rearrest was inversely associated with survival and neurologic outcomes.

Real-Time Ventricular Fibrillation Amplitude-Spectral Area Analysis to Guide Timing of Shock Delivery Improves Defibrillation Efficacy During Cardiopulmonary Resuscitation in Swine

Background

The ventricular fibrillation amplitude spectral area (AMSA) predicts whether an electrical shock could terminate ventricular fibrillation and prompt return of spontaneous circulation. We hypothesized that AMSA can guide more precise timing for effective shock delivery during cardiopulmonary resuscitation.

Methods and Results

Three shock delivery protocols were compared in 12 pigs each after electrically induced ventricular fibrillation, with the duration of untreated ventricular fibrillation evenly stratified into 6, 9, and 12 minutes: AMSA‐Driven (AD), guided by an AMSA algorithm; Guidelines‐Driven (GD), according to cardiopulmonary resuscitation guidelines; and Guidelines‐Driven/AMSA‐Enabled (GDAE), as per GD but allowing earlier shocks upon exceeding an AMSA threshold. Shocks delivered using the AD, GD, and GDAE protocols were 21, 40, and 62, with GDAE delivering only 2 AMSA‐enabled shocks. The corresponding 240‐minute survival was 8/12, 6/12, and 2/12 (log‐rank test, P=0.035) with AD exceeding GDAE (P=0.026). The time to first shock (seconds) was (median [Q1–Q3]) 272 (161–356), 124 (124–125), and 125 (124–125) (P<0.001) with AD exceeding GD and GDAE (P<0.05); the average coronary perfusion pressure before first shock (mm Hg) was 16 (9–30), 10 (6–12), and 3 (−1 to 9) (P=0.002) with AD exceeding GDAE (P<0.05); and AMSA preceding the first shock (mV·Hz, mean±SD) was 13.3±2.2, 9.0±1.6, and 8.6±2.0 (P<0.001) with AD exceeding GD and GDAE (P<0.001). The AD protocol delivered fewer unsuccessful shocks (ie, less shock burden) yielding less postresuscitation myocardial dysfunction and higher 240‐minute survival.

Conclusions

The AD protocol improved the time precision for shock delivery, resulting in less shock burden and less postresuscitation myocardial dysfunction, potentially improving survival compared with time‐fixed, guidelines‐driven, shock delivery protocols.

Does Antiarrhythmic Drug During Cardiopulmonary Resuscitation Improve the One-month Survival: The SOS-KANTO 2012 Study

BACKGROUND

Antiarrhythmic drugs (AAD) are often used for fatal ventricular arrhythmias during cardiopulmonary resuscitation (CPR). However, the efficacy of initial AAD administration during CPR in improving long-term prognosis remains unknown. This study retrospectively evaluated the effect of AAD administration during CPR on 1-month prognosis in the SOS-KANTO 2012 study population.

METHODS AND RESULTS

Of the 16,164 out-of-hospital cardiac arrest cases, 1350 shock-refractory patients were included: 747 patients not administered AAD and 603 patients administered AAD. Statistical adjustment for potential selection bias was performed using propensity score matching, yielding 1162 patients of whom 792 patients were matched (396 pairs). The primary outcome was 1-month survival. The secondary outcome was the proportion of patients with favorable neurological outcome at 1 month. Logistic regression with propensity scoring demonstrated an odds ratio (OR) for 1-month survival in the AAD group of 1.92 (P < 0.01), whereas the OR for favorable neurological outcome at 1 month was 1.44 (P = 0.26).

CONCLUSIONS

Significantly greater 1-month survival was observed in the AAD group compared with the non-AAD group. However, the effect of ADD on the likelihood of a favorable neurological outcome remains unclear. The findings of the present study may indicate a requirement for future randomized controlled trials evaluating the effect of ADD administration during CPR on long-term prognosis.

Predictive value of amplitude spectrum area of ventricular fibrillation waveform in patients with acute or previous myocardial infarction in out-of-hospital cardiac arrest

BACKGROUND

Amplitude spectrum area (AMSA) of ventricular fibrillation (VF) has been associated with survival from out-of-hospital cardiac arrest (OHCA). Ischemic heart disease has been shown to change AMSA. We studied whether the association between AMSA and survival changes with acute ST-elevation myocardial infarction (STEMI) as cause of the OHCA and/or previous MI.

METHODS

Multivariate logistic regression with log-transformed AMSA of first artifact-free VF segment was used to assess the association between AMSA and survival, according to presence of STEMI or previous MI, adjusting for resuscitation characteristics, medication use and comorbidities.

RESULTS

Of 716 VF-patients included from an OHCA-registry in the Netherlands, 328 (46%) had STEMI as cause of OHCA. Previous MI was present in 186 (26%) patients. Survival was 66%; neither previous MI (P=0.11) nor STEMI (P=0.78) altered survival. AMSA was a predictor of survival (ORadj: 1.52, 95%-CI: 1.28-1.82). STEMI was associated with lower AMSA (8.4mV-Hz [3.7-16.5] vs. 12.3mV-Hz [5.6-23.0]; P<0.001), but previous MI was not (9.5mV-Hz [3.9-18.0] vs 10.6mV-Hz [4.6-19.3]; P=0.27). When predicting survival, there was no interaction between previous MI and AMSA (P=0.14). STEMI and AMSA had a significant interaction (P=0.002), whereby AMSA was no longer a predictor of survival (ORadj: 1.03, 95%-CI: 0.77-1.37) in STEMI-patients. In patients without STEMI, higher AMSA was associated with higher survival rates (ORadj: 1.80, 95%-CI: 1.39-2.35).

CONCLUSIONS

The prognostic value of AMSA is altered by the presence of STEMI: while AMSA has strong predictive value in patients without STEMI, AMSA is not a predictor of survival in STEMI-patients.

Novel CPR system that predicts return of spontaneous circulation from amplitude spectral area before electric shock in ventricular fibrillation

AIM

Amplitude spectral area (AMSA), an index for analysing ventricular fibrillation (VF) waveforms, is thought to predict the return of spontaneous circulation (ROSC) after electric shocks, but its validity is unconfirmed. We developed an equation to predict ROSC, where the change in AMSA (ΔAMSA) is added to AMSA measured immediately before the first shock (AMSA1). We examine the validity of this equation by comparing it with the conventional AMSA1-only equation.

METHOD

We retrospectively investigated 285 VF patients given prehospital electric shocks by emergency medical services. ΔAMSA was calculated by subtracting AMSA1 from last AMSA immediately before the last prehospital electric shock. Multivariate logistic regression analysis was performed using post-shock ROSC as a dependent variable.

RESULTS

Analysis data were subjected to receiver operating characteristic curve analysis, goodness-of-fit testing using a likelihood ratio test, and the bootstrap method. AMSA1 (odds ratio (OR) 1.151, 95% confidence interval (CI) 1.086-1.220) and ΔAMSA (OR 1.289, 95% CI 1.156-1.438) were independent factors influencing ROSC induction by electric shock. Area under the curve (AUC) for predicting ROSC was 0.851 for AMSA1-only and 0.891 for AMSA1+ΔAMSA. Compared with the AMSA1-only equation, the AMSA1+ΔAMSA equation had significantly better goodness-of-fit (likelihood ratio test P<0.001) and showed good fit in the bootstrap method.

CONCLUSIONS

Post-shock ROSC was accurately predicted by adding ΔAMSA to AMSA1. AMSA-based ROSC prediction enables application of electric shock to only those patients with high probability of ROSC, instead of interrupting chest compressions and delivering unnecessary shocks to patients with low probability of ROSC.

Does the choice of definition for defibrillation and CPR success impact the predictability of ventricular fibrillation waveform analysis?

BACKGROUND

Quantitative analysis of ventricular fibrillation (VF), such as amplitude spectral area (AMSA), predicts shock outcomes. However, there is no uniform definition of shock/cardiopulmonary resuscitation (CPR) success in out-of-hospital cardiac arrest (OHCA). The objective of this study is to investigate post-shock rhythm variations and the impact of shock/CPR success definition on the predictability of AMSA.

METHODS

A total of 554 shocks from 257 OHCA patients with VF as initial rhythm were analyzed. Post-shock rhythms were analyzed every 5s up to 120s and annotated as VF, asystole (AS) and organized rhythm (OR) at serial time intervals. Three shock/CPR success definitions were used to evaluate the predictability of AMSA: (1) termination of VF (ToVF); (2) return of organized electrical activity (ROEA); (3) return of potentially perfusing rhythm (RPPR).

RESULTS

Rhythm changes occurred after 54.5% (N=302) of shocks and 85.8% (N=259) of them occurred within 60s after shock delivery. The observed post-shock rhythm changes were (1) from AS to VF (24.9%), (2) from OR to VF (16.1%), and (3) from AS to OR (12.1%). The area under the receiver operating characteristic curve (AUC) for AMSA as a predictor of shock/CPR success reached its maximum 60s post-shock. The AUC was 0.646 for ToVF, 0.782 for ROEA, and 0.835 for RPPR (p<0.001) respectively.

CONCLUSIONS

Post-shock rhythm is unstable in the first minute after the shock. The predictability of AMSA varies depending on the definition of shock/CPR success and performs best with the return of potentially perfusing rhythm endpoint for OHCA.

Short ECG segments predict defibrillation outcome using quantitative waveform measures

AIM

Quantitative waveform measures of the ventricular fibrillation (VF) electrocardiogram (ECG) predict defibrillation outcome. Calculation requires an ECG epoch without chest compression artifact. However, pauses in CPR can adversely affect survival. Thus the potential use of waveform measures is limited by the need to pause CPR. We sought to characterize the relationship between the length of the CPR-free epoch and the ability to predict outcome.

METHODS

We conducted a retrospective investigation using the CPR-free ECG prior to first shock among out-of-hospital VF cardiac arrest patients in a large metropolitan region (n=442). Amplitude Spectrum Area (AMSA) and Median Slope (MS) were calculated using ECG epochs ranging from 5s to 0.2s. The relative ability of the measures to predict return of organized rhythm (ROR) and neurologically-intact survival was evaluated at different epoch lengths by calculating the area under the receiver operating characteristic curve (AUC) using the 5-s epoch as the referent group.

RESULTS

Compared to the 5-s epoch, AMSA performance declined significantly only after reducing epoch length to 0.2s for ROR (AUC 0.77-0.74, p=0.03) and with epochs of ≤0.6s for neurologically-intact survival (AUC 0.72-0.70, p=0.04). MS performance declined significantly with epochs of ≤0.8s for ROR (AUC 0.78-0.77, p=0.04) and with epochs ≤1.6s for neurologically-intact survival (AUC 0.72-0.71, p=0.04).

CONCLUSION

Waveform measures predict defibrillation outcome using very brief ECG epochs, a quality that may enable their use in current resuscitation algorithms designed to limit CPR interruption.

Combining multiple ECG features does not improve prediction of defibrillation outcome compared to single features in a large population of out-of-hospital cardiac arrests

Introduction

Quantitative electrocardiographic (ECG) waveform analysis provides a noninvasive reflection of the metabolic milieu of the myocardium during resuscitation and is a potentially useful tool to optimize the defibrillation strategy. However, whether combining multiple ECG features can improve the capability of defibrillation outcome prediction in comparison to single feature analysis is still uncertain.

Methods

A total of 3828 defibrillations from 1617 patients who experienced out-of-hospital cardiac arrest were analyzed. A 2.048-s ECG trace prior to each defibrillation without chest compressions was used for the analysis. Sixteen predictive features were optimized through the training dataset that included 2447 shocks from 1050 patients. Logistic regression, neural network and support vector machine were used to combine multiple features for the prediction of defibrillation outcome. Performance between single and combined predictive features were compared by area under receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and prediction accuracy (PA) on a validation dataset that consisted of 1381 shocks from 567 patients.

Results

Among the single features, mean slope (MS) outperformed other methods with an AUC of 0.876. Combination of complementary features using neural network resulted in the highest AUC of 0.874 among the multifeature-based methods. Compared to MS, no statistical difference was observed in AUC, sensitivity, specificity, PPV, NPV and PA when multiple features were considered.

Conclusions

In this large dataset, the amplitude-related features achieved better defibrillation outcome prediction capability than other features. Combinations of multiple electrical features did not further improve prediction performance.

Predict Defibrillation Outcome Using Stepping Increment of Poincare Plot for Out-of-Hospital Ventricular Fibrillation Cardiac Arrest

Early cardiopulmonary resuscitation together with early defibrillation is a key point in the chain of survival for cardiac arrest. Optimizing the timing of defibrillation by predicting the possibility of successful electric shock can guide treatments between defibrillation and cardiopulmonary resuscitation and improve the rate of restoration of spontaneous circulation. Numerous methods have been proposed for predicting defibrillation success based on quantification of the ventricular fibrillation waveform during past decades. To date, however, no analytical technique has been widely accepted for clinical application. In the present study, we investigate whether median stepping increment that is calculated from the Euclidean distance of consecutive points in Poincare plot could be used to predict the likelihood of successful defibrillation. Electrocardiographic recordings of out-of-hospital cardiac arrest patients were obtained from the external defibrillators. The performance of the proposed method was evaluated by receiver operating characteristic curve and compared with the results of other established features. The results indicated that median stepping increment has comparable performance to the established methods in predicting the likelihood of successful defibrillation.