Automated external defibrillators in the hospital: A case of medical reversal

Automated external defibrillators (AEDs) emerged in the 1980s as an important innovation in pre-hospital emergency cardiac care (ECC). In the years since, the American Heart Association (AHA) and the International Liaison Committee for Resuscitation (ILCOR) have promoted AED technology for use in hospitals as well, resulting in the widespread purchase and use of AED-capable defibrillators. In-hospital use of AEDs now appears to have decreased survival from cardiac arrests. This article will look at the use of AEDs in hospitals as a case of “medical reversal.” Medical reversal occurs when an accepted, widely used treatment is found to be ineffective or even harmful. This article will discuss the issue of AEDs in the hospital using a conceptual framework provided by recent work on medical reversal. It will go on to consider the implications of the reversal for in-hospital resuscitation programs and emergency medicine more generally.     

The role of spatial interactions in creating the dispersion of transmembrane potential by premature electric shocks

Strong electric shocks applied during the refractory period can initiate or terminate cardiac arrhythmias. To elucidate the underlying mechanism, Knisleyet al. used rabbit papillary musclein vitro to scan the refractory period of an action potential with shocks of different strengths. The resulting map of the shock-induced changes in the transmembrane potential (Vm) illustrates the substrate for the creation of rotors. Our study uses computer simulations to reproduce this experimental map. Three models (a space-clamped membrane, a single cell, and a onedimensional fiber) were used to determine whether the observed map was caused by (i) the intrinsic dynamics of the membrane, (ii) the simultaneous depolarization and hyperpolarization of the opposite ends of each cell, or (iii) spatial interactions involving the whole muscle strand. The results show that the membrane and single cell models cannot reproduce the experimental map. The fiber model reproduces the shock-induced changes inVm and demonstrates that they are caused by a propagating disturbance, which, depending on the coupling interval and the shock strength, can be a new action potential or an electrotonus and can arrive from the depolarized end or from both depolarized and hyperpolarized ends of the fiber. These results indicate that the induction of rotors in the heart may not be a direct effect of the electric field.     

Effects of defibrillation shock energy and timing on 3-D computer model of heart

We present computer simulations of electrical defibrillation in a three-dimensional model of the ventricles of the heart. In this model, calledHeartSim, the ventricles are represented by 1473 cubic elements with 3 mm sides. The action potential is described by five discrete states; absolutely refractory, three relatively refractory, and repolarized. Activation is propagated to an element's six orthogonal neighbors with the conduction velocity dependent on the refractory state of the neighbor. Delivery of several extra-stimuli with decrementing coupling intervals results in ventricular fibrillation. Following the onset of ventricular fibrillation, we simulate defibrillation using various electrode configurations, shock energies, and timings. The current density distributions in the heart model resulting from the defibrillation shocks are determined from finite element analysis of the electric fields produced by the delivery of high energy shocks. The simulations suggest that successful defibrillation shocks produce a short period of low activation followed by a complete cessation of activation for a duration of 387±162 ms. In contrast, unsuccessful shocks produce a significantly shorter period of low activation (70±12 ms) after which ventricular fibrillation resumes.HeartSim mimics the experimentally reported, highly variable response to near-threshold shocks — the energy for successful defibrillation varies widely (20.8±20.7 J). In addition, the success rate vs. energy curve has a sigmoidal shape that is consistent with experiments. We demonstrate that this variability in the energy requirement results from dynamic variability in the number of elements made refractory by the shock and the relative distribution of the activation pattern at the time of the shock. Further, we show that it may be possible to lower the defibrillation energy requirements by delivery of two successive low energy pulses. The most efficient timing for the second pulse corresponds to the repolarization of the elements that were excited by the first pulse. Thus, when the interval between the two pulses was 85±18 ms, the defibrillation threshold energy (DFE) is reduced by 30.7±10% with pulses of 10 ms duration, and 62.6±7.9% with pulses of 5 ms duration. Our simulations also show that there is a delicate balance of energy between the two pulses that must be reached in order to achieve energy reduction with double pulse defibrillation. In conclusion,HeartSim serves as a tool for studying the underlying mechanisms of the effects of DF shocks on ventricular arrhythmias, and assists in evaluation of improved strategies for shock delivery.     

长时程室颤先行心肺复苏对复苏效果的影响及机制研究

目的 比较7 min室颤先行心肺复苏2 min后除颤与直接除颤的复苏效果,并探讨其机制.方法 建立猪闭胸电诱发室颤模型,CPR First组优先心肺复苏2 min后连续三次除颤,Shock First组直接予连续三次除颤,观察冠脉灌注压、室颤波频率和振幅变化,计算除颤成功率和自主循环恢复率.结果 CPR First组先行心肺复苏2 min后可提高初次除颤前的冠脉灌注压、室颤波的频率和振幅, CPR First组比Shock First组有高的除颤成功率和自主循环恢复率(P＜0.05).结论 7 min室颤除颤前先行胸外按压和人工呼吸可明显提高复苏成功率,其机制与增加冠脉灌注,改善心脏能量储备,提高室颤波的频率和振幅有关.     

The impact of manual defibrillation technique on no-flow time during simulated cardiopulmonary resuscitation

INTRODUCTION: Rapid defibrillation is the most effective strategy for establishing return of spontaneous circulation following cardiac arrest due to ventricular fibrillation. The aim of this study is to measure the delay due to of charging the defibrillator during chest compression in an attempt to reduce the duration of the pre-shock pause in between cessation of chest compressions and shock delivery as advocated by the American Heart Association (AHA) guidelines compared to charging the defibrillator immediately following rhythm analysis without resuming chest compressions as recommended by the European Resuscitation Council (ERC). METHODS: This was a randomised controlled cross over trial comparing pre-shock pause times when defibrillation was performed on a manikin according to the AHA and ERC guidelines using paddles and hands free defibrillation systems. RESULTS: The pre-shock pause between cessation of chest compression and shock delivery was significantly different between techniques (Friedman test, P<0.0001). ERC paddles technique had the greatest pre-shock pause (7.4 s [6.7-11.2]) followed by ERC hands free (7.0 s [6.5-8.5]) and AHA paddles (1.6 s [1.1-2.3]). AHA hands free took the least amount of time (1.5 s [0.8-1.5]). Extrapolating these data to older defibrillators with longer charge times saw pre-shock pause intervals of 9 s (Codemaster XL) and 12 s (Lifepak 20) with the ERC approach. CONCLUSION: This study demonstrated clinically significant delays to defibrillation by analysing and charging the defibrillator without performing concurrent chest compressions. In a simulated scenario, charging the defibrillator whilst performing chest compressions was perceived as safe and significantly reduced the pre-shock pause between cessation of chest compression and shock delivery.     

Feasibility of automated rhythm assessment in chest compression pauses during cardiopulmonary resuscitation

Aim

To demonstrate the feasibility of doing a reliable rhythm analysis in the chest compression pauses (e.g. pauses for two ventilations) during cardiopulmonary resuscitation (CPR).

Methods

We extracted 110 shockable and 466 nonshockable segments from 235 out-of-hospital cardiac arrest episodes. Pauses in chest compressions were already annotated in the episodes. We classified pauses as ventilation or non-ventilation pause using the transthoracic impedance. A high-temporal resolution shock advice algorithm (SAA) that gives a shock/no-shock decision in 3 s was launched once for every pause longer than 3 s. The sensitivity and specificity of the SAA for the analyses during the pauses were computed.

Results

We identified 4476 pauses, 3263 were ventilation pauses and 2183 had two ventilations. The median of the mean duration per segment of all pauses and of pauses with two ventilations were 6.1 s (4.9–7.5 s) and 5.1 s (4.2–6.4 s), respectively. A total of 91.8% of the pauses and 95.3% of the pauses with two ventilations were long enough to launch the SAA. The overall sensitivity and specificity were 95.8% (90% low one-sided CI, 94.3%) and 96.8% (CI, 96.2%), respectively. There were no significant differences between the sensitivities (P = 0.84) and the specificities (P = 0.18) for the ventilation and the non-ventilation pauses.

Conclusion

Chest compression pauses are frequent and of sufficient duration to launch a high-temporal resolution SAA. During these pauses rhythm analysis was reliable. Pre-shock pauses could be minimised by analysing the rhythm during ventilation pauses when CPR is delivered at 30:2 compression:ventilation ratio.     

Decoding twitter: Surveillance and trends for cardiac arrest and resuscitation communication

Aim of the study

Twitter has over 500 million subscribers but little is known about how it is used to communicate health information. We sought to characterize how Twitter users seek and share information related to cardiac arrest, a time-sensitive cardiovascular condition where initial treatment often relies on public knowledge and response.

Methods

Tweets published April–May 2011 with keywords cardiac arrest, CPR, AED, resuscitation, heart arrest, sudden death and defib were identified. Tweets were characterized by content, dissemination, and temporal trends. Tweet authors were further characterized by: self-identified background, tweet volume, and followers.

Results

Of 62,163 tweets (15,324, 25%) included resuscitation/cardiac arrest-specific information. These tweets referenced specific cardiac arrest events (1130, 7%), CPR performance or AED use (6896, 44%), resuscitation-related education, research, or news media (7449, 48%), or specific questions about cardiac arrest/resuscitation (270, 2%). Regarding dissemination (1980, 13%) of messages were retweeted. Resuscitation specific tweets primarily occurred on weekdays. Most users (10,282, 93%) contributed three or fewer tweets during the study time frame. Users with more than 15 resuscitation-specific tweets in the study time frame had a mean 1787 followers and most self-identified as having a healthcare affiliation.

Conclusion

Despite a large volume of tweets, Twitter can be filtered to identify public knowledge and information seeking and sharing about cardiac arrest. To better engage via social media, healthcare providers can distil tweets by user, content, temporal trends, and message dissemination. Further understanding of information shared by the public in this forum could suggest new approaches for improving resuscitation related education.     

Early cardiopulmonary resuscitation and use of Automated External Defibrillators by laypersons in out-of-hospital cardiac arrest using an SMS alert service

Aim

To evaluate an SMS service (SMS = short message service = text message) with which laypersons are alerted to go to patients with suspected out-of-hospital cardiac arrest and perform early cardiopulmonary resuscitation (CPR) and use an Automated External Defibrillator (AED). This study is the first to report on a program in which an emergency medical service (EMS) is able to alert citizens by sending them SMS messages on their mobile phone.

Methods

Web-based questionnaires were completed by laypersons who were sent an alert by the AED-Alert system between February 1, 2010 and April 30, 2010. Questions concerned the process of training, receiving alerts, actions taken and follow-up care.

Results

AED-Alert was activated for 52 patients suspected of cardiac arrest, sending 3227 alerts to 2287 laypersons. Out of 2168 eligible laypersons 1679 (77%) completed 2098 questionnaires, one for each alert. Action was taken in only 579 alerts. Laypersons were not in the patient's vicinity (41%), noticed alerts too late (35%), or other reasons (24%). In 298 alerts laypersons faced problems with retrieving AEDs (51%), finding addresses (29%), traffic (5%), or other (15%). Aid was provided in 75 alerts, involving 47 patients. Laypersons started early CPR and defibrillation (49%), assisted EMS personnel (52%), or took care of family (39%). Laypersons arrived before EMS personnel in 21 patients, started CPR and defibrillation in 18, and assisted EMS personnel in 9 patients.

Conclusion

Improvements of the SMS alert service by laypersons, the EMS, and through technical adjustments, could increase the number of laypersons who provide early aid.     

Performance of an automated external defibrillator during simulated rotor-wing critical care transports

Objective

This study aimed to evaluate whether an automated external defibrillator (AED) was accurate enough to analyze the heart rhythm during a simulated rotor wing critical care transport. We hypothesized that AED analysis of the simulated rhythms during a helicopter flight would result in significant errors (i.e., inappropriate shocks, analysis delay).

Methods

Three commercial AEDs were tested for analyzing the heart rhythm in a helicopter using a manikin and a human volunteer. Ventricular fibrillation (VF), sinus rhythm, and asystole were simulated by using an arrhythmia simulator of the manikin. The intervals from analysis to shock recommendation were collected on a stationary and in-motion helicopter. Sensitivity and specificity of three AEDs were also calculated. Vibration intensities were measured with a digital vibration meter placed on the chest of the manikin/human volunteer both on the stretcher and on the floor of the helicopter.

Results

All AEDs correctly recommended shock delivery for the cardiac rhythms of the manikin. Sensitivity for VF was 100.0% (95% CI 91.2-100.0) and specificity for sinus rhythm and asystole were 100.0% (95% CI 91.2-100.0). Although the recorded ECG rhythms of the volunteer in an in-motion helicopter showed baseline artifacts, all AEDs analyzed the cardiac rhythm of the volunteer correctly and did not recommend shock delivery. On the floor of the helicopter, the median measured vibration intensity was 6.6 m/s² (IQR 5.5-7.7 m/s²) with significantly less vibrations transmitted to the manikin/human volunteer chest (manikin median 3.1 m/s², IQR 2.2-4.0 m/s²; human volunteer median 0.95 m/s², IQR 0.65-1.25 m/s²).

Conclusion

This study suggested that current AEDs could analyze the heart rhythm correctly during simulated helicopter transport. Further studies using an animal model would be needed before applying to patients.