Summary

Setting Up a Stroke Team Algorithm and Conducting Simulation-based Training in the Emergency Department - A Practical Guide

Published: January 15, 2017
doi:

Summary

Every min counts in acute stroke care. This guide shows how to establish a Stroke Team algorithm and enhance its performance with regular simulation training. The principles of Crew Resource Management (CRM) facilitate a straight workflow, reduce door-to-needle times and increase staff satisfaction.

Abstract

Time is of the essence when caring for an acute stroke patient. The ultimate goal is to restore blood flow to the ischemic brain. This can be achieved by either thrombolysis with recombinant tissue-plasminogen activator (rt-PA), the standard therapy for stroke patients who present within the first hours of symptom onset without contraindications, or by an endovascular approach, if a proximal brain vessel occlusion is detected. As the efficacy of both therapies declines over time, every minute saved along the way will improve the patient's outcome.

This critical situation requires thorough work and precise communication with the patient, the family and colleagues from different professions to acquire all relevant information and reach the right decision while carefully monitoring the patient. This is a high fidelity situation. In nonmedical high-fidelity environments such as aviation, Crew Resource Management (CRM) is used to enhance safety and team efficiency.

This guide shows how a Stroke Team algorithm, which is transferable to other hospital settings, was established and how regular simulation-based trainings were performed. It requires determination and endurance to maintain these time-consuming simulation trainings on a regular basis over the course of time. However, the resulting improvement of team spirit and excellent door-to-needle times will benefit both the patients and the work environment in any hospital.

A dedicated Stroke Team of 7 persons who are notified 24/7 by a collective call via speed dial and run a binding algorithm that takes approximately 20 min, was established. To train everybody involved in this algorithm, a simulation-based team training for all new Stroke Team members was conceived and conducted at monthly intervals. This led to a relevant and sustained reduction of the mean door-to-needle time to 25 min, and enhanced the feeling of stroke readiness especially in junior doctors and nurses.

Introduction

The efficacy of thrombolysis with recombinant tissue-plasminogen activator (rt-PA) for acute ischemic stroke is highly time-dependent and decreases over time even in the therapeutic time window of 4.5 hr1. The same has been shown for endovascular stroke therapy2. The additional mechanical recanalization after thrombolysis has been shown to be highly effective in improving outcomes of patients with severe stroke due to Large Vessel Occlusion (LVO)3. This new therapy adds to the complexity and interdisciplinarity of acute stroke care since endovascular therapies require a neurointerventionalist, an anesthetist or neurointensivist and in many cases even the acute onward referral of the patient to a specialized center.

Therefore, concepts are needed to minimize the time to treatment without putting patient safety at risk. Since acute stroke care is delivered by interdisciplinary teams, a standardized algorithm and simulation-based training of technical and nontechnical skills appear to be a straightforward approach. In this context, not only "time is brain" but also "team is brain", since precious min and safety-relevant information can be lost by inefficient communication among team members. In nonmedical high-fidelity situations such as aviation, a concept called Crew Resource Management (CRM) has proven to be highly effective4.

A large share of fatal errors is not due to a lack of knowledge or technical skills, but to deficits in communication, interaction and decision-making. CRM emphasizes the importance of "nontechnical skills" and defines them as cognitive, social and personal resources that complement technical skills. The six key domains comprise clear communication, teamwork, situation awareness, decision-making, leadership and the management of stress5.

This concept has already been successfully implemented in professional cardiovascular life support6. A binding algorithm, a basic education in CRM for all Stroke Team members and regular simulation-based trainings for all new members of the high-fidelity Stroke Team offer ways to improve acute stroke care.

A dedicated Stroke Team of 7 persons who are notified by a collective call via speed dial and have precise tasks within a defined stroke algorithm was established to treat patients within the therapeutic time window. These are the 7 mandatory team members that are summoned to each stroke alarm:

1 resident in neurology from the Stroke Unit (SU)
1 resident in neurology from the Emergency Department (ED)
1 nurse from the ED
1 laboratory technician
1 resident specializing in neuroradiology
1 radiology technician
1 specialist in neurology (senior neurologist of the stroke unit)

Thus, a simulation-based Stroke Team training was conceived, which is conducted at monthly intervals for all new Stroke Team members and as a refresher for permanent staff. The simulation-based training transports the values of CRM and emphasizes the importance of nontechnical skills in an interdisciplinary multiprofessional team. To monitor the effects of this Stroke Team intervention consisting of the algorithm and the regular training, door-to-needle times, thrombolysis-associated complications, staff satisfaction and perceived safety in the emergency room (ER) are recorded continuously.

Protocol

1. Prenotification of the ED After the ED nurse hears an alarm, go to the computer screen immediately. Check the information above the incoming patient via the online platform (e.g., IVENA Ehealth)7. Find that the system announces a 66 year-old male patient with the tentative diagnosis of a stroke within the time window (stroke <6 hr) with the estimated time of arrival. Pre-notify the resident of the SU via phone call. As the SU resident hears the mob…

Representative Results

Effect on the door-to-needle times and thrombolysis rate The implementation of the Stroke Team algorithm in 2012 accompanied by regular simulation-based Stroke Team trainings led to a relevant increase in the patients treated with a door-to-needle time below 30 and 60 min and to an increase in our thrombolysis rate. Figure 1: Stroke Team Algorithm of t…

Discussion

A binding stroke team algorithm and regular simulation-based stroke team trainings can lead to a long-term reduction of the door-to-needle time as the key benchmark process time for acute stroke treatment. Excellent examples of a set of measures that improve the acute stroke work flow, which also inspired our algorithm, have been described by the Helsinki group14,15. Another very innovative approach to shorten the time interval from symptom onset to thrombolysis are mobile stroke units such as the pioneering S…

Divulgations

The authors have nothing to disclose.

Acknowledgements

The Stroke Team training was supported by a research grant of Boehringer Ingelheim to WP.

Materials

Drug
Alteplase (rtPA) Boehringer Ingelheim, Ingelheim am Rhein, Germany Licensed drug, which has proven effectiveness for acute ischemic stroke
Urapidil 50 mg/10 ml Takeda Pharma, Berlin, Germany Licensed drug, antihypertensive
Granisetron 3 mg/3 ml Hameln Pharma, Hameln, Germany Licensed drug, antiemetic
Lorazepam 2 mg/1 ml Pfizer, Berlin, Germany Licensed drug, sedative
Iopromid 300 mg/ml Bayer Vital GmbH, Leverkusen, Germany Licensed drug, non-ionic contrast agent for computed tomography
Name Company Catalog Number Comments
Device
S-Monovette citrate 3 ml Sarstedt, Nürnbrecht, Germany to collect blood for coagulation assays
S-Monovette EDTA 1.6 ml Sarstedt, Nürnbrecht, Germany to collect blood for hematology assays
S-Monovette lithium heparinate 7.5 ml Sarstedt, Nürnbrecht, Germany to collect blood for clinical chemistry assays
ACL Top 500  Instrumentation Laboratory, Kirchheim, Germany Automated hemostasis analyzer
Sysmex XE 2100 Sysmex Corporation, Norderstedt, Germany Automated hematology analyzser
Cobas 6000 Roche Diagnostics, Mannheim, Germany Automated clinical chemistry analyzser
Resusci Anne Skillreporter Laerdal, Stavanger, Norway remote-controlled manikin
Ingenuity 128 Philips, Hamburg, Germany CT-scanner
MEDRAD Stellant Bayer Radiology, Leverkusen Germany Contrast agent delivery system
Universal 320 R Hettich, Tuttlingen, Germany Centrifuge
Perfusor fm Braun, Melsungen, Germany Infusion pump
Infinity Gamma Dräger, Hamburg, Germany Monitor
Ivena ehealth mainis IT-Service GmbH, Offenbach, Germany online prenotification platform
Braun ThermoScan PRO 4000 Welch Allyn, Hechingen, Germany ear thermometer

References

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Tahtali, D., Bohmann, F., Rostek, P., Wagner, M., Steinmetz, H., Pfeilschifter, W. Setting Up a Stroke Team Algorithm and Conducting Simulation-based Training in the Emergency Department – A Practical Guide. J. Vis. Exp. (119), e55138, doi:10.3791/55138 (2017).

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