Improving Code Blue Response Through the Use of Simulation :

Expert guidelines advocate defibrillation within 2 minutes after an in-hospital cardiac arrest caused by ventricular arrhythmia. However, empirical data on the prevalence of delayed defibrillation in the United States and its effect on survival are limited. We identified 6789 patients who had cardiac arrest due to ventricular fibrillation or pulseless ventricular tachycardia at 369 hospitals participating in the National Registry of Cardiopulmonary Resuscitation. Using multivariable logistic regression, we identified characteristics associated with delayed defibrillation. We then examined the association between delayed defibrillation (more than 2 minutes) and survival to discharge after adjusting for differences in patient and hospital characteristics. The overall median time to defibrillation was 1 minute (interquartile range, <1 to 3 minutes); delayed defibrillation occurred in 2045 patients (30.1%). Characteristics associated with delayed defibrillation included black race, noncardiac admitting diagnosis, and occurrence of cardiac arrest at a hospital with fewer than 250 beds, in an unmonitored hospital unit, and during after-hours periods (5 p.m. to 8 a.m. or weekends). Delayed defibrillation was associated with a significantly lower probability of surviving to hospital discharge (22.2%, vs. 39.3% when defibrillation was not delayed; adjusted odds ratio, 0.48; 95% confidence interval, 0.42 to 0.54; P<0.001). In addition, a graded association was seen between increasing time to defibrillation and lower rates of survival to hospital discharge for each minute of delay (P for trend <0.001). Delayed defibrillation is common and is associated with lower rates of survival after in-hospital cardiac arrest. Copyright 2008 Massachusetts Medical Society.

This report synthesises a subset of 31 journal articles on high-fidelity simulation-based medical education containing 32 research studies drawn from a larger qualitative review published previously. These studies were selected because they present adequate data to allow for quantitative synthesis. We hypothesised an association between hours of practice in simulation-based medical education and standardised learning outcomes measured as weighted effect sizes. Journal articles were screened using 5 exclusion and inclusion criteria. Response data were extracted and 3 judges independently coded each study. Learning outcomes were standardised using a common metric, the average weighted effect size (AWES), due to the heterogeneity of response measures in individual studies. anova was used to evaluate AWES differences due to hours of practice on a high-fidelity medical simulator cast in 5 categories. The eta squared (eta2) statistic was used to assess the association between AWES and simulator practice hours. There is a strong association (eta2=0.46) between hours of practice on high-fidelity medical simulators and standardised learning outcomes. The association approximates a dose-response relationship. Hours of high-fidelity simulator practice have a positive, functional relationship with standardised learning outcomes in medical education. More rigorous research methods and more stringent journal editorial policies are needed to advance this field of medical education research.

Patient simulators are increasingly used in the education and training of healthcare professionals. This paper describes the history of human patient simulator development, the features of contemporary simulators, the acquisition of basic and advanced clinical skills using patient simulators, and the benefits, cost, limitations and effectiveness of this innovative learning modality. The development of human patient simulators began in the late 1960s, and accelerated in the late 1980s and early 1990s. Several simulator systems are now professionally manufactured, commercially available, and used at hundreds of medical centres, universities and colleges in the USA and throughout the world. Contemporary patient simulators have many clinical features, and look and respond to interventions with ever-increasing degrees of realism because sophisticated physiological and pharmacological models automatically control many features. Simulators are used to teach basic skills, such as respiratory physiology and cardiovascular haemodynamics, and advanced clinical skills, e.g. management of difficult airways, tension pneumothorax, pulmonary embolism and shock. The simulation laboratory offers distinct educational advantages, especially for learning how to recognise and to treat rare, complex, clinical problems. Costs of simulator-based educational programmes include facility, equipment and personnel. Current limitations include clinical realism of the patient manikin and faculty development.