In situ simulation comparing in-hospital first responder sudden cardiac arrest resuscitation using semiautomated defibrillators and automated external defibrillators.

INTRODUCTION: Multifaceted approaches using simulation and human factors methods may optimize in-hospital sudden cardiac arrest (SCA) response. The Arrhythmia Simulation/Cardiac Event Nursing Training-Automated External Defibrillator phase (ASCENT-AED) study used in situ medical simulation to compare traditional and AED-supplemented SCA first-responder models. METHODS: The study was conducted at an academic 719-bed hospital with institutional review board approval. Two simulation scenarios were developed and featured either respiratory arrest with perfusing bradycardia or ventricular fibrillation (VF) arrest. Study floors were equipped with either a semiautomated defibrillator (SD) only (control) or with both SD and AED (experimental); subjects functioned as solitary first responders and did not receive resuscitation training. RESULTS: Fifty nurses were enrolled on control (n=25) and experimental (n=25) floors. The groups' nonblinded performances exhibited the following differences during VF scenario: slower calls for help by the control group [mean time to completion of 25+/-17 seconds versus 18+/-11 seconds for the experimental group (P<0.05)] and fewer subjects in the control group performing chest compressions [44.0% versus experimental group's 95.8% (P<0.001)]. Eighty-eight percent of the control group defibrillated the manikin at an average of 155+/-59 seconds, with 32.0% of those subjects using semiautomated rhythm analysis; 100% (not significant [NS]) of experimental group defibrillated at 154+/-72 seconds (NS) with 100% AED analysis (P<0.001). Fewer control group subjects (28.0%) were observed during the bradycardia scenarios to perform inappropriate chest compressions than the AED-supplemented subjects [69.6% (P=0.01)]; nonindicated defibrillation was delivered during these scenarios by a single subject in the control group. Twenty-eight percent and 72% of VF scenarios were managed appropriately by control and experimental groups, respectively; bradycardia scenarios were managed without severe adverse event by 64% of control group and 28% of experimental group. CONCLUSIONS: In situ simulation can provide useful information, both anticipated and unexpected, to guide decisions about proposed defibrillation technologies and SCA response models for in-hospital resuscitation system design and education before implementation.

Comparison of sudden cardiac arrest resuscitation performance data obtained from in-hospital incident chart review and in situ high-fidelity medical simulation.

INTRODUCTION: High-fidelity medical simulation of sudden cardiac arrest (SCA) presents an opportunity for systematic probing of in-hospital resuscitation systems. Investigators developed and implemented the SimCode program to evaluate simulation's ability to generate meaningful data for system safety analysis and determine concordance of observed results with institutional quality data. METHODS: Resuscitation response performance data were collected during in situ SCA simulations on hospital medical floors. SimCode dataset was compared with chart review-based dataset of actual (live) in-hospital resuscitation system performance for SCA events of similar acuity and complexity. RESULTS: 135 hospital personnel participated in nine SimCode resuscitations between 2006 and 2008. Resuscitation teams arrived at 2.5+/-1.3 min (mean+/-SD) after resuscitation initiation, started bag-valve-mask ventilation by 2.8+/-0.5 min, and completed endotracheal intubations at 11.3+/-4.0 min. CPR was performed within 3.1+/-2.3 min; arrhythmia recognition occurred by 4.9+/-2.1 min, defibrillation at 6.8+/-2.4 min. Chart review data for 168 live in-hospital SCA events during a contemporaneous period were extracted from institutional database. CPR and defibrillation occurred later during SimCodes than reported by chart review, i.e., live: 0.9+/-2.3 min (p<0.01) and 2.1+/-4.1 min (p<0.01), respectively. Chart review noted fewer problems with CPR performance (simulated: 43% proper CPR vs. live: 98%, p<0.01). Potential causes of discrepancies between resuscitation response datasets included sample size and data limitations, simulation fidelity, unmatched SCA scenario pools, and dissimilar determination of SCA response performance by complementary reviewing methodologies. CONCLUSION: On-site simulations successfully generated SCA response measurements for comparison with live resuscitation chart review data. Continued research may refine simulation's role in quality initiatives, clarify methodologic discrepancies and improve SCA response.