Ventricular fibrillation waveform characteristics are different in ischemic heart failure compared with structurally normal hearts

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.

Frequency Variation of Ventricular Fibrillation May Help Predict Successful Defibrillation in a Rat Model of Cardiac Arrest

BACKGROUND

To evaluate whether the frequency variation of ventricular fibrillation (VF) helps to predict successful defibrillation in a rat model of cardiac arrest.

METHODS

VF was induced in rats followed by cardiopulmonary resuscitation and then defibrillation. The electrocardiographic signals of 30 rats with first-shock success were obtained from our previous animal experiments, and 300 rats without first-shock success were selected as control. The VF waveform immediately before the first defibrillation was analyzed.

RESULTS

Eighty-eight percentages of the frequency variations of an electrocardiogram (ECG) record falling in the range -9.5-9.5 Hz was selected with sensitivity of 0.8, specificity of 0.583, and area under curve (AUC) of 0.708. Compared with amplitude spectrum area (AMSA) (sensitivity = 0.767, specificity= 0.547, and AUC = 0.678), combining frequency variation and AMSA significantly increases the predictability with sensitivity of 0.933, specificity of 0.493, and AUC of 0.732 (p = 0.005).

CONCLUSIONS

The frequency variation of VF may serve a useful parameter to predict defibrillation success.

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

Objective

Quantitative 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.

Methods

A 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.

Results

A 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.

Conclusion

In this retrospective study, combining AMSA with previous shock information using neural networks greatly improves prediction performance of defibrillation outcome for subsequent shocks.

Predicting defibrillation success

PURPOSE OF REVIEW

Ventricular fibrillation is the primary rhythm in many cardiac arrest patients. Since the late 1980s, the surface electrocardiogram of ventricular fibrillation has been subjected to analysis to obtain reliable information about the likelihood of successful countershock and to estimate the duration of cardiac arrest. Considerable efforts were made in the past 2 years to further improve the predictive power of rescue shock measures.

RECENT FINDINGS

In a retrospective clinical study, ventricular fibrillation single feature analysis was not able to reliably estimate duration between cardiac arrest onset and initial electrocardiogram. Combining ventricular fibrillation features in the time and frequency domain by employing neural networks did not further improve the best single feature prediction power taken from higher ventricular fibrillation frequency bands. Cardioversion outcome prediction based on the wavelet technique increased the specificity up to 66% at the 95% sensitivity level.

SUMMARY

Recent results question the ventricular fibrillation feature analysis as a reliable tool to estimate the duration of human cardiac arrest. Animal and clinical studies confirmed that ventricular fibrillation waveform analysis contains information to reliably predict the countershock success rate and further improved countershock outcome prediction. Prospective clinical studies are highly warranted to demonstrate that ventricular fibrillation waveform analysis definitely improves survival after cardiac arrest.

The influence of myocardial substrate on ventricular fibrillation waveform: a swine model of acute and postmyocardial infarction

OBJECTIVE

In cardiac arrest resulting from ventricular fibrillation, the ventricular fibrillation waveform may be a clue to its duration and predict the likelihood of shock success. However, ventricular fibrillation occurs in different myocardial substrates such as ischemia, heart failure, and structurally normal hearts. We hypothesized that ventricular fibrillation is altered by myocardial infarction and varies from the acute to postmyocardial infarction periods.

DESIGN

An animal intervention study was conducted with comparison to a control group.

SETTING

This study took place in a university animal laboratory.

SUBJECTS

Study subjects included 37 swine.

INTERVENTIONS

Myocardial infarction was induced by occlusion of the midleft anterior descending artery. Ventricular fibrillation was induced in control swine, acute myocardial infarction swine, and in postmyocardial infarction swine after a 2-wk recovery period.

MEASUREMENTS AND MAIN RESULTS

Ventricular fibrillation was recorded in 11 swine with acute myocardial infarction, ten postmyocardial infarction, and 16 controls. Frequency (mean, median, dominant, and bandwidth) and amplitude-related content (slope, slope-amp [slope divided by amplitude], and amplitude-spectrum area) were analyzed. Frequencies at 5 mins of ventricular fibrillation were altered in both acute myocardial infarction (p < .001 for all frequency characteristics) and postmyocardial infarction swine (p = .015 for mean, .002 for median, .002 for dominant frequency, and <.001 for bandwidth). At 5 mins, median frequency was highest in controls, 10.9 +/- .4 Hz; lowest in acute myocardial infarction, 8.4 +/- .5 Hz; and intermediate in postmyocardial infarction, 9.7 +/- .5 Hz (p < .001 for acute myocardial infarction and p = .002 for postmyocardial infarction compared with control). Slope and amplitude-spectrum area were similar among the three groups with a shallow decline after minute 2, whereas slope-amp remained significantly altered for acute myocardial infarction swine at 5 mins (p = .003).

CONCLUSIONS

Ventricular fibrillation frequencies depend on myocardial substrate and evolve from the acute through healing phases of myocardial infarction. Amplitude related measures, however, are similar among these groups. It is unknown how defibrillation may be affected by relying on the ventricular fibrillation waveform without considering myocardial substrate.

Shock outcome is related to prior rhythm and duration of ventricular fibrillation

BACKGROUND

Several different ventricular fibrillation (VF) analysis features based on ECG have been reported for shock outcome prediction. In this study we investigated the influence of the time from VF onset to shock delivery (VF duration) and the rhythm before onset of VF, on the probability of return of spontaneous circulation (ROSC). We also analysed how these factors relate to the VF analysis feature median slope.

METHODS

ECG recordings from 221 cardiac arrest patients from previously published prospective studies on the quality of CPR were used. VF duration and prior rhythm were determined when VF occurred during the episode. Median slope before each shock was calculated.

RESULTS

The median VF duration was shorter in shocks producing ROSC, 24 seconds (s) versus 70s (P<0.001). VF duration shorter than 30s resulted in 27% ROSC versus 10% for those longer than 30s (OR=3.5 [95% CI: 2.2-5.4]). The prior rhythm influenced the probability of ROSC, with perfusing rhythm being superior, followed by PEA, asystole, and "poor" PEA (broad complexes and/or irregular/very slow rate), respectively. The probability of ROSC corresponded well with the average median slope value for each group, but the correlation between median slope and VF duration was very poor (r2=0.05).

CONCLUSIONS

Based on our findings, detection of VF during ongoing chest compressions might be valuable because VF of short duration was associated with ROSC. Further, the rhythm before VF affects shock outcome with a perfusing rhythm giving the best prospect. The median slope can be used for shock outcome prediction, but not for determining VF duration. A combination could be beneficial and warrants further studies.

Ventricular fibrillation frequency characteristics are altered in acute myocardial infarction

OBJECTIVE

Future automated external defibrillators are being designed to direct rescue efforts (chest compressions first vs. defibrillation) by inferring the duration of ventricular fibrillation based on its waveform characteristics such as frequency content. This approach assumes that the ventricular fibrillation waveform is an appropriate surrogate for ventricular fibrillation duration and is not affected by structural heart disease. We hypothesized that an acute myocardial infarction may alter the frequency content of ventricular fibrillation.

DESIGN

Animal intervention study with comparison to control group.

SETTING

University animal laboratory.

SUBJECTS

Twenty-seven swine.

INTERVENTIONS

Acute myocardial infarction was induced by occlusion of the mid-left anterior descending artery. Ventricular fibrillation was induced in swine with acute myocardial infarction and control swine.

MEASUREMENTS AND MAIN RESULTS

Ventricular fibrillation was induced in 11 swine with an acute myocardial infarction and in 16 control swine. Ventricular fibrillation waveforms were analyzed for mean, median, and dominant frequency, as well as bandwidth and amplitude. All frequency characteristics were significantly (p < .001) altered in swine with acute myocardial infarction compared with controls. Specifically, these characteristics were significantly depressed and varied little over time in swine with acute myocardial infarction compared with controls.

CONCLUSIONS

These data establish that ventricular fibrillation during an acute myocardial infarction has an altered frequency content and time evolution compared with ventricular fibrillation without coronary obstruction. Frequency characteristics such as mean, median, dominant, and bandwidth show little variation in time after an acute myocardial infarction and are not suitable surrogates for ventricular fibrillation duration. These findings have important implications for the development of "smart" automated external defibrillators designed to determine duration of ventricular fibrillation from the waveform characteristics.

Chronic health conditions and survival after out-of-hospital ventricular fibrillation cardiac arrest

OBJECTIVE

To investigate whether chronic clinical comorbidity, as collected from emergency medical services (EMS) reports, influences survival after out-of-hospital ventricular fibrillation (VF) cardiac arrest.

METHODS

In this observational retrospective cohort study in King County, Washington, USA 1043 people who suffered out-of-hospital VF arrest due to heart disease between 1 January 1999 and 31 December 2003 were studied. Chronic conditions were ascertained and tallied from EMS reports using a uniform abstraction form by people blinded to outcome status. The outcome was survival to hospital discharge.

RESULTS

75% (776/1043) of patients had at least one chronic health condition and 51% (529/1043) had prior clinically recognised heart disease. Overall, the increasing count of chronic conditions was inversely associated with the odds of survival to hospital discharge after adjustment for potential confounders (OR 0.84 (95% CI 0.74 to 0.95) for each additional chronic condition). The chronic condition-outcome association tended to be more prominent among those with longer EMS response intervals (p = 0.07 for interaction term between condition count and response interval). For example, the OR of survival was 0.72 (95% CI 0.59 to 0.88) for each additional chronic condition when the EMS response interval was 8 min compared with an OR of 0.95 (95% CI 0.79 to 1.14) when the EMS response interval was 3 min.

CONCLUSION

In this cohort, an increasing burden of clinical comorbidity based on a review of EMS reports was associated with a lower odds of survival after VF arrest. This finding suggests that chronic conditions influence arrest pathophysiology and in turn could help guide resuscitation care.

Analysis of the ventricular fibrillation waveform in refibrillation

OBJECTIVES

Frustrating outcomes are driving investigation of alternative resuscitation protocols. Previous analysis of the ventricular fibrillation (VF) waveform has focused on guiding whether to shock immediately or to delay for delivery of cardiopulmonary resuscitation in the case of presenting VF. The same issues emerge in the case of refibrillation.

MEASUREMENTS AND MAIN RESULTS

All cases of witnessed VF cardiac arrest in the Rochester, MN, area in a 9-yr period were analyzed. Rochester rescuers employed an early defibrillation protocol during the study period. A summary measure of the VF waveform before the shock delivered in 35 incidents of refibrillation was compared with the time elapsed from the initial shock, the intervening electrocardiographic rhythm, ambulance response time, and call-to-shock time for prediction of early return of spontaneous circulation and of neurologically intact survival. VF waveform analysis separated patients with good outcomes when treated with early defibrillation of refibrillation from those without good outcomes more clearly than other predictors.

CONCLUSIONS

Analysis of VF waveform offers promise for real-time guidance of resuscitation efforts on the basis of individual patient characteristics, in refibrillation and in the initial shock. It has advantages over guidance based on individual or aggregate system response times.