Simulation-based assessment of trainee's performance in post-cardiac arrest resuscitation

Afrah A Ali; Wan-Tsu W Chang; Ali Tabatabai; Melissa B Pergakis; Camilo A Gutierrez; Benjamin Neustein; Gregory E Gilbert; Jamie E Podell; Gunjan Parikh; Neeraj Badjatia; Melissa Motta; David P Lerner; Nicholas A Morris

doi:10.1016/j.resplu.2022.100233

Simulation-based assessment of trainee's performance in post-cardiac arrest resuscitation

Resusc Plus. 2022 Apr 28:10:100233. doi: 10.1016/j.resplu.2022.100233. eCollection 2022 Jun.

Authors

Afrah A Ali¹, Wan-Tsu W Chang^{1

2}, Ali Tabatabai^{3

2}, Melissa B Pergakis^{4

2}, Camilo A Gutierrez⁴, Benjamin Neustein², Gregory E Gilbert⁵, Jamie E Podell^{4

2}, Gunjan Parikh^{4

2}, Neeraj Badjatia^{4

2}, Melissa Motta^{4

2}, David P Lerner⁶, Nicholas A Morris^{4

2}

Affiliations

¹ Department of Emergency Medicine, University of Maryland School of Medicine, Baltimore, MD, USA.
² Program in Trauma, University of Maryland School of Medicine, Baltimore, MD, USA.
³ Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA.
⁴ Department of Neurology, University of Maryland School of Medicine, Baltimore, MD, USA.
⁵ ΣigmaΣtats Consulting©, LLC, Charleston, SC, USA.
⁶ Department of Neurology, Lahey Hospital and Medical Center, Burlington, MA, USA.

Abstract

Objectives: To assess trainees' performance in managing a patient with post-cardiac arrest complicated by status epilepticus.

Methods: In this prospective, observational, single-center simulation-based study, trainees ranging from sub interns to critical care fellows evaluated and managed a post cardiac arrest patient, complicated by status epilepticus. Critical action items were developed by a modified Delphi approach based on American Heart Association guidelines and the Neurocritical Care Society's Emergency Neurological Life Support protocols. The primary outcome measure was the critical action item sum score. We sought validity evidence to support our findings by including attending neurocritical care physicians and comparing performance across four levels of training.

Results: Forty-nine participants completed the simulation. The mean sum of critical actions completed by trainees was 10/21 (49%). Eleven (22%) trainees verbalized a differential diagnosis for the arrest. Thirty-two (65%) reviewed the electrocardiogram, recognized it as abnormal, and consulted cardiology. Forty trainees (81%) independently decided to start temperature management, but only 20 (41%) insisted on it when asked to reconsider. There was an effect of level of training on critical action checklist sum scores (novice mean score [standard deviation (SD)] = 4.8(1.8) vs. intermediate mean score (SD) = 10.4(2.1) vs. advanced mean score (D) = 11.6(3.0) vs. expert mean score (SD) = 14.7(2.2)).

Conclusions: High-fidelity manikin-based simulation holds promise as an assessment tool in the performance of post-cardiac arrest care.

Keywords: ANOVA, Analysis of variance; CI, Confidence Intervals; CT, Computed tomography; Critical Care; ECG, Electrocardiography; EEG, Electroencephalogram; ENLS, Emergency Neurological Life Support; Hypothermia; ICC, Intra-class correlation; IQR, Interquartile ranges; Induced; OHCA, Out of Hospital Cardiac Arrest; Out of Hospital Cardiac Arrest; PGY, Post graduate year; SD, Standard Deviation; Simulation; Status Epilepticus; cEEG, Continuous EEG.