• Carleen R Spitzer, Kimberly Evans, Jeri Buehler, Naeem A Ali, and Beth Y Besecker.
• Division of Pulmonary, Critical Care, and Sleep Medicine, 201 Davis Heart & Lung Research Institute, 473 W. 12th Avenue, Columbus, OH 43210, United States. Electronic address: carleen.spitzer@osumc.edu.
• Resuscitation. 2019 Oct 1; 143: 158-164.

BackgroundMortality from in-hospital cardiac arrests remains a large problem world-wide. In an effort to improve in-hospital cardiac arrest mortality, there is a renewed focus on team training and operations. Here, we describe the implementation of a "pit crew" model to provide in-hospital resuscitation care.MethodsIn order to improve our institution's code team organization, we implemented a pit crew resuscitation model. The model was introduced through computer-based modules and lectures and was reemphasized at our institution-based ACLS training and mock code events. To assess the effect of our model, we reviewed pre- and post-pit crew implementation data from five sources: defibrillator downloads, a centralized hospital database, mock codes, expert-led debriefings, and confidential surveys. Data with continuous variables and normal distribution were analyzed using a standard two-sample t-test. For yes/no categorical data either a Z-test for difference between proportions or Chi-square test was used.ResultsThere were statistically significant improvements in compression rates post-intervention (mean rate 133.5 pre vs. 127.9 post, two-tailed, p = 0.02) and in adequate team communication (33% pre vs. 100% post; p = 0.05). There were also trends toward a reduction in the number of shockable rhythms that were not defibrillated (32.7% pre vs. 18.4% post), average time to shock (mean 1.96 min pre vs. 1.69 min post), and overall survival to discharge (31% pre vs. 37% post), though these did not reach statistical significance.ConclusionImplementation of an in-hospital, pit crew resuscitation model is feasible and can improve both code team communication as well as key ACLS metrics.Copyright © 2019 Elsevier B.V. All rights reserved.

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