Carleen R Spitzer1, Kimberly Evans2, Jeri Buehler3, Naeem A Ali4, Beth Y Besecker5. 1. 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. 2. Quality & Patient Safety, 630 Ackerman Rd., 2nd Floor, Rm F2050, Columbus, OH 43202, United States. Electronic address: kimberly.evans@osumc.edu. 3. Education, Development and Resources, 660 Ackerman Rd., Columbus, OH 43218, United States. Electronic address: jeri.buehler@osumc.edu. 4. University Hospital, Division of Pulmonary, Critical Care, and Sleep Medicine, 168 Doan Hall, 410 W 10th Avenue, Columbus, OH 43210, United States. Electronic address: Naeem.ali@osumc.edu. 5. 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: beth.besecker@osumc.edu.
Abstract
BACKGROUND: Mortality 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. METHODS: In 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. RESULTS: There 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. CONCLUSION: Implementation of an in-hospital, pit crew resuscitation model is feasible and can improve both code team communication as well as key ACLS metrics.
BACKGROUND: Mortality 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. METHODS: In 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. RESULTS: There 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. CONCLUSION: Implementation of an in-hospital, pit crew resuscitation model is feasible and can improve both code team communication as well as key ACLS metrics.
Authors: Ayanna Walker; Adam Oswald; Jessica Wanthal; Christine Van Dillen; Cherian Plamoottil; Parth Patel; Maria Tassone; Latha Ganti Journal: Health Psychol Res Date: 2022-07-28