Optimizing defibrillation during cardiac arrest

Combining Ventricular Fibrillation Features With Defibrillation Waveform Parameters Improves the Ability to Predict Shock Outcomes in a Rabbit Model of Cardiac Arrest

BACKGROUND

Quantitative ventricular fibrillation (VF) analysis has the potential to optimize defibrillation by predicting shock outcomes, but its performance remains unsatisfactory. This study investigated whether combining VF features with defibrillation parameters could enhance the ability of shock outcome prediction.

METHODS

VF was electrically induced and left untreated for 30 to 180 seconds in 55 New Zealand rabbits. A defibrillatory shock was applied with 1 of 9 biphasic waveforms with different tilts and durations. A 4-step up-and-down protocol was used to maintain the success rate near 50% for each waveform. Ten features and 10 parameters were obtained from the recorded VF and defibrillation waveforms. Logistic regression and a convolutional neural network were used to combine VF features with defibrillation parameters.

RESULTS

The area under the curve value for the combination of a single VF feature and a single defibrillation parameter (0.725 [95% CI, 0.676-0.775] versus 0.644 [95% CI, 0.589-0.699]; P=0.002) was significantly greater than that for the optimal VF feature. The area under the curve value for the combination of multiple VF features and multiple defibrillation parameters (0.752 [95% CI, 0.704-0.800] versus 0.657 [95% CI, 0.602-0.712]; P<0.001) was significantly greater than that the combination of multiple VF features. The area under the curve for the combination of the raw VF waveform and raw defibrillation waveform (0.781 [95% CI, 0.734-0.828] versus 0.685 [95% CI, 0.632-0.738]; P=0.007) was significantly greater than that for the raw VF waveform.

CONCLUSIONS

In this animal model, combining VF features with defibrillation parameters greatly enhanced the ability of shock outcome prediction, whether it was based on extracted features/parameters or directly using raw waveforms with machine learning methods.

Analysis during chest compressions in out-of-hospital cardiac arrest patients, a cross/sectional study: The DEFI 2022 study

AIMS

During out-of-hospital cardiac arrest (OHCA), an automatic external defibrillator (AED) analyzes the cardiac rhythm every two minutes; however, 80% of refibrillations occur within the first minute post-shock. We have implemented an algorithm for Analyzing cardiac rhythm While performing chest Compression (AWC). When AWC detects a shockable rhythm, it shortens the time between analyses to one minute. We investigated the effect of AWC on cardiopulmonary resuscitation quality.

METHOD

In this cross-sectional study, we compared patients treated in 2022 with AWC, to a historical cohort from 2017. Inclusion criteria were OHCA patients with a shockable rhythm at the first analysis. Primary endpoint was the chest compression fraction (CCF). Secondary endpoints were cardiac rhythm evolution and survival, including survival analysis of non-prespecified subgroups.

RESULTS

In 2017 and 2022, 355 and 377 OHCAs met the inclusion criteria, from which we analyzed the 285 first consecutive cases in each cohort. CCF increased in 2022 compared to 2017 (77% [72-80] vs 72% [67-76]; P < 0.001) and VF recurrences were shocked more promptly (53 s [32-69] vs 117 s [90-132]). Survival did not differ between 2017 and 2022 (adjusted hazard-ratio 0.96 [95% CI, 0.78-1.18]), but was higher in 2022 within the sub-group of OHCAs that occurred in a public place and within a short time from call to AED switch-on (adjusted hazard ratio 0.85[0.76-0.96]).

CONCLUSIONS

OHCA patients treated with AWC had higher CCF, shorter time spent in ventricular fibrillation, but no survival difference, except for OHCA that occurred in public places with short intervention time.

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.

The Lancet Commission to reduce the global burden of sudden cardiac death: a call for multidisciplinary action

Despite major advancements in cardiovascular medicine, sudden cardiac death (SCD) continues to be an enormous medical and societal challenge, claiming millions of lives every year. Efforts to prevent SCD are hampered by imperfect risk prediction and inadequate solutions to specifically address arrhythmogenesis. Although resuscitation strategies have witnessed substantial evolution, there is a need to strengthen the organisation of community interventions and emergency medical systems across varied locations and health-care structures. With all the technological and medical advances of the 21st century, the fact that survival from sudden cardiac arrest (SCA) remains lower than 10% in most parts of the world is unacceptable. Recognising this urgent need, the Lancet Commission on SCD was constituted, bringing together 30 international experts in varied disciplines. Consistent progress in tackling SCD will require a completely revamped approach to SCD prevention, with wide-sweeping policy changes that will empower the development of both governmental and community-based programmes to maximise survival from SCA, and to comprehensively attend to survivors and decedents' families after the event. International collaborative efforts that maximally leverage and connect the expertise of various research organisations will need to be prioritised to properly address identified gaps. The Commission places substantial emphasis on the need to develop a multidisciplinary strategy that encompasses all aspects of SCD prevention and treatment. The Commission provides a critical assessment of the current scientific efforts in the field, and puts forth key recommendations to challenge, activate, and intensify efforts by both the scientific and global community with new directions, research, and innovation to reduce the burden of SCD worldwide.

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.

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.