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Inhospital cardiac arrest — the crucial first 5 min: a simulation study

Abstract

Background

Early recognition and call for help, fast initiation of chest compressions, and early defibrillation are key elements to improve survival after cardiac arrest but are often not achieved. We aimed to investigate what occurs during the initial treatment of unannounced in situ simulated inhospital cardiac arrests and reasons for successful or inadequate initial resuscitation efforts.

Methods

We conducted unannounced full-scale in situ simulated inhospital cardiac arrest followed by a debriefing. Simulations and debriefings were video recorded for subsequent analysis. We analyzed quantitative data on actions performed and time measurements to key actions from simulations and qualitative data from transcribed debriefings.

Results

We conducted 36 simulations. Time to diagnosis of cardiac arrest was 37 (27; 55) s. Time to first chest compression from diagnosis of cardiac arrest was 37 (18; 74) s, time to calling the cardiac arrest team was 144 (71; 180) s, and time to first shock was 221 (181; 301) s. We observed participants perform several actions after diagnosing the cardiac arrest and before initiating chest compressions. Domains emerging from the debriefings were teaming and resources. Teaming included the themes communication, role allocation, leadership, and shared knowledge, which all included facilitators and barriers. Resources included the themes knowledge, technical issues, and organizational resources, of which all included barriers, and knowledge also included facilitators.

Conclusion

Using unannounced in situ simulated cardiac arrests, we found that key elements such as chest compressions, calling the cardiac arrest team, and defibrillation were delayed. Perceived barriers to resuscitation performance were leadership and teaming, whereas experience, clear leadership, and recent training were perceived as important facilitators for treatment progress.

Background

Survival after inhospital cardiac arrest (IHCA) is low at approximately 20–30% [1,2,3,4]. The international resuscitation guidelines emphasize the key elements in the “chain of survival,” i.e., early recognition and alert of emergency services, early initiation of chest compressions, and early defibrillation [5,6,7]. The chance of survival decreases with delays in any or all of these key components [7,8,9,10]. While previous studies report that guideline adherence improves the chance of survival, studies also showed limited guideline adherence and delays in initiation of treatment [11,12,13].

Despite this, several studies have focused on investigating the advanced life support after the arrival of a cardiac arrest team or investigated the quality of chest compressions [14,15,16,17,18]. In contrast, what occurs during the important first few minutes in a clinical setting has received less attention. Due to the unpredictability of IHCAs, it is challenging to investigate the first minutes of a clinical cardiac arrest. However, unannounced in situ simulation is a method to create the opportunity to explore what happens during the initial response to a cardiac arrest. In situ simulations have previously been used to test the emergency response at hospitals and revealed many patient safety threats such as missing or malfunctioning equipment and lack of knowledge [19,20,21,22,23]. Furthermore, in situ simulations have also been shown to improve patient outcomes, teamwork, self-confidence, and decrease time to key elements within resuscitation [24,25,26,27,28,29,30,31,32].

Accordingly, using a mixed-method approach, we aimed to investigate what occurs during the initial treatment of unannounced in situ simulated IHCAs and explore the perceived reasons for successful or inadequate initial resuscitation efforts from debriefings.

Material and methods

Study design

This mixed-method study is a post hoc analysis of a prospective, multicenter, observational, simulation study [33]. Full-scale unannounced in situ simulated cardiac arrests were conducted in different hospital departments and were followed by a debriefing. We present data on the treatment before the cardiac arrest team’s arrival.

Setting

Simulations were conducted during the day shift (9.00–15.00) and the night shift (16.00–00.00) on regular workdays and weekends. Participants were the ward staff (i.e., physicians, nurses, and nurse assistants) and the responding cardiac arrest team to reflect clinical practice. Hospital departments were eligible if the department had access to an automated external defibrillator (AED). Intensive care, cardiology, psychiatric, and pediatric departments were excluded as they had different procedures and/or competences to treat a cardiac arrest and as we only conducted scenarios with adults.

On the day of the simulation, the nurse manager assigned a patient room for the simulation without informing other staff members. Time, location, and scenario were unknown to all other staff. A full-body manikin (Resusci Anne QCPR AED with Airway Head, Laerdal Medical, Stavanger, Norway), dressed in patient garments and with an intravenous access, was placed in a hospital bed. The manikin collected data on the quality of cardiopulmonary resuscitation. Before and during the study period, all hospital staff with the possibility of being involved in a simulation received a written notification to act as in a real situation in case of a simulation. After the simulation and debriefing, participants were sent an e-mail with a questionnaire inquiring demographic information.

Scenario

A nurse/nurse assistant was called to the patient room and briefed with a short patient story: female patient admitted to observation for (illness related to the department), not severely affected but now experiencing chest pain and has called for help. The nurse/nurse assistant was instructed to assess the patient and to act as in a real situation throughout the entire simulation.

The manikin was unconscious with no breathing. The manikin presented with ventricular fibrillation when connected to a defibrillator until the cardiac arrest team had delivered two shocks. At subsequent rhythm analysis, the manikin presented with sinus rhythm. The simulation ended either when sinus rhythm was discovered or 3 min after the rhythm analysis with sinus rhythm.

It was possible to retrieve a patient record with a history, standard tests including blood pressure, saturation, blood samples, an electrocardiogram, and chest X-ray. Participants could perform actions as in a real situation, e.g., administration of intravenous medication, and intubation. Participants had to retrieve and use all their normal equipment. ShockLink (Laerdal Medical, Stavanger, Norway) was connected to the defibrillator by a research assistant to allow for defibrillation. Participants received no help nor feedback during simulations except if participants performed an action correctly and asked for the result, e.g., if they checked for breathing, an answer was given, i.e., the patient is not breathing.

Debriefing

We conducted a semi-structured debriefing of 15–25 min for all participants at each simulation. The debriefing guide was based on PEARLS [34, 35]. During the debriefing, we used plus-delta in the analysis phase [36] and strived to use advocacy inquiry [37] when suited. The debriefings were conducted openly without fixed topics to allow for participants’ emerging themes and discussion points.

Ethics

The Regional Committee on Biomedical Research Ethics waived the need for permission and deemed the study exempt from individual consent (141/2017). The study was approved by the Danish Data Protection Agency (1-16-02-367-18). Permission to conduct the study was granted from all hospital administrations. All participants were given written information prior to the study period as well as regularly throughout the study period. Participants were informed that participation was voluntary, that data would be de-identified after analysis, and no information on individual performance would be disclosed. Furthermore, they were informed that refrainment from participating would not be disclosed to the management. Safety precautions were taken to ensure patient safety during simulations, and in case of emergencies/acute patients, simulations were canceled/interrupted [33].

Data collection

The in situ simulations and debriefings were video recorded. Two cameras (GoPro Hero 5 Black, San Mateo, CA, USA) were placed in opposite corners of the room and captured 180-degree video. An additional camera was used to ensure sound quality during debriefings. Data regarding actions performed during the simulation and time measurements were obtained from the video recordings by two independent researchers. Data on the quality of cardiopulmonary resuscitation were retrieved from a SimPad (Laerdal Medical, Stavanger, Norway) wirelessly connected to the manikin. Time zero was considered as the time of diagnosis of cardiac arrest unless otherwise stated. The study was reported in accordance with the STROBE guidelines.

Outcome measures

Outcomes were (A) time to diagnosis of cardiac arrest, (B) time to first chest compression, (C) time to first shock, (D) chest compression fraction, (E) actions performed during delays of other actions, and (F) participants’ motivation for initial actions.

Qualitative analysis

Video recordings of the debriefings were transcribed verbatim and analyzed using a qualitative approach [38]. Three researchers (MS, KGL, KK) independently coded six debriefings inductively to develop a coding framework. A single researcher (MS) coded the remaining debriefings. Furthermore, three debriefings were coded by the same three researchers to ensure continued agreement halfway through. Codes were merged into themes for interpretative thematic analysis [39, 40]. The three researchers (MS, KGL, KK) who performed the analysis are all medical doctors, resuscitation researchers, and simulation instructors. All had experience with using thematic analysis.

Quantitative analyses

Categorical data are presented as percentages (number), normally distributed data are presented as mean (standard deviation), and non-normally distributed data are presented as median (1st quartile; 3rd quartile). Histograms and QQ plots determined normality. To see if there were any differences in actions performed in courses of treatment that were effective and those that were ineffective, we compared the fastest third and slowest third of simulations. Data were analyzed using R-statistics (version 1.4.1717, R Core Team 2021, R Foundation for Statistical Computing, Vienna, Austria). We did not perform a sample size calculation but intended to include 60 in situ simulations.

Results

From July 2018 to December 2020, we conducted 36 in situ simulations (30 complete simulations and 6 interrupted simulations with partial datasets). Furthermore, 30 simulations were canceled (8 due to the COVID-19 pandemic, 7 due to lack of department capacity, 12 due to acute patients, and 3 due to other reasons). The data collection was stopped early due to the COVID-19 pandemic. The simulations included 249 ward staff members. Participant demographics are shown in Table 1.

Table 1 Demographics

Quantitative data

In all 36 in situ simulations, the ward staff initiated basic life support. Cardiac arrest was correctly diagnosed in 67% (n = 24) of simulations according to the European Resuscitation Council Guidelines [5, 41]. In 28% (n = 10) of simulations, participants only examined for unconsciousness, and in 6% (n = 2) of simulations, participants only checked for breathing. Of the 26 simulations where breathing was examined, participants opened the airway in 15% (n = 4) of simulations. Participants performed various actions prior to diagnosing the cardiac arrest (Table 2) as well as prior to initiating chest compressions (Table 3).

Table 2 Actions performed from the beginning of simulation until diagnosis of cardiac arrest
Table 3 Actions performed from diagnosis of cardiac arrest until initiation of chest compressions

Time from start of the simulation to diagnosis of cardiac arrest was 37 (27; 55) s. Time to first chest compression was 37 (18; 74) s from diagnosis of cardiac arrest, time to calling the cardiac arrest team was 144 (71; 180) s, and time to first shock was 221 (181; 301) s from diagnosis of cardiac arrest. Other time measurements and examples of actions performed are presented in Fig. 1. The mean chest compression depth was 47 (9) mm (recommendation 50–60 mm [5]), mean chest compression rate was 105 (15) compressions per minute (recommendation 100–120 per minute [5]), and mean chest compression fraction was 0.73 (0.15).

Fig. 1
figure 1

Timelines. A Median times for actions. Data presented as median (Q1; Q3). B Example of an effective timeline. C Example of an ineffective timeline. AED, automated external defibrillator; CAT, cardiac arrest team; CPR, cardiopulmonary resuscitation

Qualitative data

From the debriefings, we identified two domains related to the initial treatment of the simulated cardiac arrests: (A) teaming and (B) resources. The domain teaming (ad hoc formation of teams and teamwork [42]) consisted of four themes: communication, role allocation, leadership, and shared knowledge, which all included barriers and facilitators. The domain resources consisted of three themes: knowledge, technical issues, and organizational resources. Knowledge included both barriers and facilitators, whereas technical issues and organizational resources themes were only brought up in the debriefings when they were barriers.

Within teaming, it was described how communication, role allocation, leadership, and shared knowledge affected the ad hoc formation of the team and the teamwork (Table 4). Clear and audible communication had an overall facilitating effect. Closed-loop communication was often mentioned as a facilitator, whereas lack of communication was a barrier. When it came to role allocation, it was described how an effective role allocation contributed as a facilitator to treatment progress. Role allocation was facilitated by explicit verbalization and when the leadership role was undertaken early in the scenario. Furthermore, initial leadership and task allocation by a nurse or nurse assistant was perceived to optimize the treatment. In contrast, lack of early leadership resulted in confusion about which tasks to be addressed and who would address them. Those participants who arrived later in the scenario found it difficult to determine their role when there was no leader assigning tasks and overseeing the entire situation. Participants frequently stated it was challenging to assume a leadership position when they perceived that they lacked clinical experience/experience with cardiac arrest. Finally, it was described how assumptions about what had been done by other team members led to the omission of important tasks such as calling the cardiac arrest team. In contrast, summaries, verbalization of actions, and general audible and clear communication were facilitators contributing to shared knowledge (Table 4).

Table 4 Themes related to teaming

For the theme knowledge, it was clear that (recent) experience with resuscitation either in training sessions or clinical events was a facilitator to treatment progress. Moreover, the use of cognitive aids, e.g., a leaflet with treatment algorithms or action cards, was described as helpful tools. In contrast, lack of practical training or clinical encounters was a barrier. Technical issues were described to cause delays, irritation, and confusion. These issues were related to, e.g., issues activating the internal alarm at the department to alert the nearest colleagues of an emergency. Furthermore, issues with operating the elevators were described. Additionally, it was described that organizational resources, i.e., both too few and too many people, were a barrier to resuscitation by making teaming difficult. This was especially difficult as overcrowding and understaffing were not handled in resuscitation training where instead a fixed number of people were present, each having a specific task (Table 5).

Table 5 Themes related to resources

Discussion

Using unannounced, full-scale in situ simulations, we identified delays diagnosing cardiac arrest, initiating chest compressions, and calling the cardiac arrest team. These delays were related to the insertion of unnecessary actions, knowledge deficits, and lacking skills within treatment algorithm and teaming.

Previous studies of inhospital basic life support have focused on the quality of chest compressions or time to first shock [17, 18, 43, 44]. We were able to study deficiencies in diagnosing cardiac arrest and calling the cardiac arrest team as well as exploring the reasons for these delays by using unannounced in situ simulations followed by a debriefing. In one-third of simulations, the diagnosis was incorrect as defined in the criteria by the European Resuscitation Council, and many unnecessary actions were performed before diagnosing the cardiac arrest [5]. Most participants checking for breathing did not open the airway. Thus, it may be argued that the number of participants correctly diagnosing cardiac arrest is even lower. Notably, studies have shown that laypersons, healthcare professionals, and even basic life support instructors struggle to diagnose cardiac arrest correctly [45, 46]. While contemporary strategies to improve basic life support training focus on chest compression skills [17, 18, 47], our findings highlight that the diagnosis of cardiac arrest should be emphasized in future training and research as this is essential to initiate an early call for help and early chest compressions.

We found delays in executing key components in resuscitation, such as initiation of chest compressions, calling the cardiac arrest team, and delivering the first shock. Notably, time to first compression and time to rhythm check are considerably worse in our study compared to previous reports from cardiac arrest registries [2, 48]. This is likely due to inaccuracies in the registries that may be prone to biased time estimates [49, 50]. Chest compressions were not initiated within the first minute after diagnosing the cardiac arrest in more than 25% of cases. While delivering the first shock depends on collecting a defibrillator, which takes time, chest compressions can and should be initiated immediately and only requires a single person to start compressing the chest. These initial actions of diagnosing cardiac arrest, calling for help, and starting compressions are known to be the most important links in the “chain of survival” in terms of improving survival outcome [7,8,9,10, 43]. Therefore, emphasis on these elements in training should be prioritized to make the greatest impact on survival [51].

Participants in this study performed several other actions before starting chest compressions, e.g., moving the bed or collecting equipment. Our qualitative data showed that participants with limited knowledge and/or experience described doubt as to which actions to perform and in which order. Many of the “unnecessary” actions performed were actions that should be performed at a later stage, e.g., moving the bed and removing the headboard to optimize the access to ventilate the patient or collecting equipment for later administration of medication. While these actions are part of the ward staff’s responsibilities, their timing and prioritization were wrong. The lack of correct task performance and prioritization may be a symptom of cognitive overload in a stressful situation, thus suggesting the need for training in prioritization and delegation of tasks to initiate basic life support in the clinical setting.

The amount and frequency of resuscitation training seem to be a key factor for performing the correct algorithm and prioritizing tasks. Our participants described resuscitation experience as a facilitator to performance. However, nurses and nurse assistants in Denmark are most often only offered a basic life support course and only every other year [52]. In contrast, studies have shown that resuscitation skills decay after as little as 3–6 months. Accordingly, evidence support the use of low-dose but high-frequency training with a focus on contextualized team training [29, 53,54,55,56,57,58].

Cognitive aids could be used to remember the correct algorithm, prioritization, and allocation of tasks. Our participants found a leaflet summarizing the algorithm useful and suggested how cognitive aids may be helpful to allocate tasks and roles. Similarly, the International Liaison Committee on Resuscitation suggests cognitive aids could be beneficial for advanced life support among healthcare professionals but recommend against the use of cognitive aids for laypeople performing basic life support as it may increase time to first compression [59]. No studies exist on cognitive aids for basic life support provided by healthcare professionals. However, the risk of increased time to first compression must be considered versus the potential benefit of avoiding delays in instances where care providers are unsure of how to proceed.

We found several barriers related to teaming, such as communication and leadership skills, as well as the ability to allocate specific roles and tasks effectively. The term “teaming” covers the ad hoc formation of a team, including the role allocation as well as the otherwise teamwork [42]. Not only did we observe the lack of these skills to ensure effective progress of treatment, but our participants also described how lack of communication and leadership were barriers, whereas audible and closed-loop communication, effective role allocation, and good leadership were mentioned as facilitators. The ward staff in our study were mainly trained in hospital basic life support courses, primarily focusing on the technical training in performing cardiopulmonary resuscitation without specific training of nontechnical skills. Today, there is a focus on e-learning and skill stations for basic resuscitation training [60,61,62,63,64,65]. While this may be a feasible solution to practice chest compressions or to learn some elements, e.g., how to alert the cardiac arrest team, our findings show the importance of contextualized training to allow for training in nontechnical skills and familiarization with local conditions, e.g., location of equipment, which should be combined with training in communication and leadership, and team training. This should be offered for healthcare professional basic life support providers instead of the current focus on mainly individual cardiopulmonary resuscitation skills.

Our findings suggest a necessity for more frequent training emphasizing teaming, communication, and leadership in the basic life support curriculum for healthcare providers. Today, these essential nontechnical skills are reserved for the advanced life support curriculum, neglecting the importance of how to organize a group of staff members and treatment components in an emergency before the arrival of the cardiac arrest team. The future training should be contextualized using team training with equipment equal to the clinical equipment and in a setting equivalent to the clinical setting. Furthermore, the training should focus on time-sensitive key components, e.g., emphasizing the importance of early cardiac arrest diagnosis and chest compression initiation.

Limitations

The study was simulation based. However, the study was designed to mimic the clinical setting using full-scale, unannounced in situ simulations with the normally available equipment and staffing. Despite being unannounced, clinical staff may have detected the research team entering the patient rooms in some cases. We cannot exclude a Hawthorne effect as participants were informed that a research study was being conducted. Thus, real-life clinical performance may be worse than what we found. However, we believe such an effect is limited as participants did not know time, location, or content of the simulations. During the data collection in the period of COVID-19, it was mandatory to use surgical face masks and to disinfect hands before entering the simulation. This may have caused slight delays in the arrival of subsequent providers. However, this reflected the clinical setting in that period. The study was an observational study and not based on a sample size calculation. Our sample size was limited, and the COVID-19 pandemic caused us to stop the data collection prematurely.

Conclusion

During unannounced in situ simulated cardiac arrests, key elements such as chest compressions, calling the cardiac arrest team, and defibrillation were delayed. Several actions were performed before these key elements. Perceived barriers to resuscitation performance were leadership and teaming, whereas experience, clear leadership, and recent training were perceived as important facilitators for treatment progress.

Availability of data and materials

The datasets generated and/or analyzed during the current study are not publicly available due to data protection requirements but are available from the corresponding author on reasonable request.

Abbreviations

AED:

Automated external defibrillator

CAT:

Cardiac arrest team

CPR:

Cardiopulmonary resuscitation

IHCA:

Inhospital cardiac arrest

References

  1. Nolan JP, Soar J, Smith GB, Gwinnutt C, Parrott F, Power S, et al. Incidence and outcome of in-hospital cardiac arrest in the United Kingdom National Cardiac Arrest Audit. Resuscitation. 2014;85(8):987–92.

    Article  PubMed  Google Scholar 

  2. Andersen LW, Holmberg MJ, Lofgren B, Kirkegaard H, Granfeldt A. Adult in-hospital cardiac arrest in Denmark. Resuscitation. 2019;140:31–6.

    Article  PubMed  Google Scholar 

  3. Andersen LW, Holmberg MJ, Berg KM, Donnino MW, Granfeldt A. In-hospital cardiac arrest: a review. Jama. 2019;321(12):1200–10.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Kolte D, Khera S, Aronow WS, Palaniswamy C, Mujib M, Ahn C, et al. Regional variation in the incidence and outcomes of in-hospital cardiac arrest in the United States. Circulation. 2015;131(16):1415–25.

    Article  PubMed  Google Scholar 

  5. Olasveengen TM, Semeraro F, Ristagno G, Castren M, Handley A, Kuzovlev A, et al. European Resuscitation Council Guidelines 2021: basic life support. Resuscitation. 2021;161:98–114.

    Article  PubMed  Google Scholar 

  6. Soar J, Böttiger BW, Carli P, Couper K, Deakin CD, Djärv T, et al. European Resuscitation Council Guidelines 2021: adult advanced life support. Resuscitation. 2021;161:115–51.

    Article  PubMed  Google Scholar 

  7. Nolan J, Soar J, Eikeland H. The chain of survival. Resuscitation. 2006;71(3):270–1.

    Article  PubMed  Google Scholar 

  8. Semeraro F, Greif R, Böttiger BW, Burkart R, Cimpoesu D, Georgiou M, et al. European Resuscitation Council Guidelines 2021: systems saving lives. Resuscitation. 2021;161:80–97.

    Article  PubMed  Google Scholar 

  9. Holmberg M, Holmberg S, Herlitz J. Incidence, duration and survival of ventricular fibrillation in out-of-hospital cardiac arrest patients in sweden. Resuscitation. 2000;44(1):7–17.

    Article  CAS  PubMed  Google Scholar 

  10. Sandroni C, Ferro G, Santangelo S, Tortora F, Mistura L, Cavallaro F, et al. In-hospital cardiac arrest: survival depends mainly on the effectiveness of the emergency response. Resuscitation. 2004;62(3):291–7.

    Article  PubMed  Google Scholar 

  11. Hessulf F, Herlitz J, Rawshani A, Aune S, Israelsson J, Södersved-Källestedt M-L, et al. Adherence to guidelines is associated with improved survival following in-hospital cardiac arrest. Resuscitation. 2020;155:13–21.

    Article  PubMed  Google Scholar 

  12. Crowley CP, Salciccioli JD, Kim EY. The association between ACLS guideline deviations and outcomes from in-hospital cardiac arrest. Resuscitation. 2020;153:65–70.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Sutton RM, French B, Niles DE, Donoghue A, Topjian AA, Nishisaki A, et al. 2010 American Heart Association recommended compression depths during pediatric in-hospital resuscitations are associated with survival. Resuscitation. 2014;85(9):1179–84.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Andersen LW, Isbye D, Kjærgaard J, Kristensen CM, Darling S, Zwisler ST, et al. Effect of vasopressin and methylprednisolone vs placebo on return of spontaneous circulation in patients with in-hospital cardiac arrest: a randomized clinical trial. Jama. 2021;326(16):1586–94.

    Article  CAS  PubMed  Google Scholar 

  15. Andersen LW, Granfeldt A, Callaway CW, Bradley SM, Soar J, Nolan JP, et al. Association between tracheal intubation during adult in-hospital cardiac arrest and survival. Jama. 2017;317(5):494–506.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Spitzer CR, Evans K, Buehler J, Ali NA, Besecker BY. Code blue pit crew model: a novel approach to in-hospital cardiac arrest resuscitation. Resuscitation. 2019;143:158–64.

    Article  PubMed  Google Scholar 

  17. Abella BS, Alvarado JP, Myklebust H, Edelson DP, Barry A, O'Hearn N, et al. Quality of cardiopulmonary resuscitation during in-hospital cardiac arrest. Jama. 2005;293(3):305–10.

    Article  CAS  PubMed  Google Scholar 

  18. Abella BS, Sandbo N, Vassilatos P, Alvarado JP, O'Hearn N, Wigder HN, et al. Chest compression rates during cardiopulmonary resuscitation are suboptimal: a prospective study during in-hospital cardiac arrest. Circulation. 2005;111(4):428–34.

    Article  PubMed  Google Scholar 

  19. Wheeler DS, Geis G, Mack EH, LeMaster T, Patterson MD. High-reliability emergency response teams in the hospital: improving quality and safety using in situ simulation training. BMJ Qual Saf. 2013;22(6):507–14.

    Article  PubMed  Google Scholar 

  20. Lighthall GK, Poon T, Harrison TK. Using in situ simulation to improve in-hospital cardiopulmonary resuscitation. Jt Comm J Qual Patient Saf. 2010;36(5):209–16.

    PubMed  Google Scholar 

  21. Ullman E, Kennedy M, Di Delupis FD, Pisanelli P, Burbui AG, Cussen M, et al. The Tuscan mobile simulation program: a description of a program for the delivery of in situ simulation training. Intern Emerg Med. 2016;11(6):837–41.

    Article  PubMed  Google Scholar 

  22. Patterson MD, Geis GL, Falcone RA, LeMaster T, Wears RL. In situ simulation: detection of safety threats and teamwork training in a high risk emergency department. BMJ Qual Saf. 2013;22(6):468–77.

    Article  PubMed  Google Scholar 

  23. Petrosoniak A, Fan M, Hicks CM, White K, McGowan M, Campbell D, et al. Trauma resuscitation using in situ simulation team training (TRUST) study: latent safety threat evaluation using framework analysis and video review. BMJ Qual Saf. 2021;30(9):739–46.

    Article  PubMed  Google Scholar 

  24. Andreatta P, Saxton E, Thompson M, Annich G. Simulation-based mock codes significantly correlate with improved pediatric patient cardiopulmonary arrest survival rates. Pediatr Crit Care Med. 2011;12(1):33–8.

    Article  PubMed  Google Scholar 

  25. Josey K, Smith ML, Kayani AS, Young G, Kasperski MD, Farrer P, et al. Hospitals with more-active participation in conducting standardized in-situ mock codes have improved survival after in-hospital cardiopulmonary arrest. Resuscitation. 2018;133:47–52.

    Article  PubMed  Google Scholar 

  26. Delac K, Blazier D, Daniel L, D NW. Five alive: using mock code simulation to improve responder performance during the first 5 minutes of a code. Crit Care Nurs Q. 2013;36(2):244–50.

    Article  PubMed  Google Scholar 

  27. Herbers MD, Heaser JA. Implementing an in situ mock code quality improvement program. Am J Crit Care. 2016;25(5):393–9.

    Article  PubMed  Google Scholar 

  28. Huseman KF. Improving code blue response through the use of simulation. J Nurses Staff Dev. 2012;28(3):120–4.

    Article  PubMed  Google Scholar 

  29. Sullivan NJ, Duval-Arnould J, Twilley M, Smith SP, Aksamit D, Boone-Guercio P, et al. Simulation exercise to improve retention of cardiopulmonary resuscitation priorities for in-hospital cardiac arrests: a randomized controlled trial. Resuscitation. 2015;86:6–13.

    Article  PubMed  Google Scholar 

  30. Rubio-Gurung S, Putet G, Touzet S, Gauthier-Moulinier H, Jordan I, Beissel A, et al. In situ simulation training for neonatal resuscitation: an RCT. Pediatrics. 2014;134(3):e790–7.

    Article  PubMed  Google Scholar 

  31. Tofil NM, Lee White M, Manzella B, McGill D, Zinkan L. Initiation of a pediatric mock code program at a children’s hospital. Med Teach. 2009;31(6):e241–e7.

    Article  PubMed  Google Scholar 

  32. van Schaik SM, Plant J, Diane S, Tsang L, O'Sullivan P. Interprofessional team training in pediatric resuscitation: a low-cost, in situ simulation program that enhances self-efficacy among participants. Clin Pediatr. 2011;50(9):807–15.

    Article  Google Scholar 

  33. Stærk M, Lauridsen KG, Niklassen J, Nielsen RP, Krogh K, Løfgren B. Barriers and facilitators for successful AED usage during in-situ simulated in-hospital cardiac arrest. Resuscitation Plus. 2022;10:100257.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Eppich W, Cheng A. Promoting excellence and reflective learning in simulation (PEARLS): development and rationale for a blended approach to health care simulation debriefing. Simul Healthc. 2015;10(2):106–15.

    Article  PubMed  Google Scholar 

  35. Eppich WJ, Mullan PC, Brett-Fleegler M, Cheng A. “Let’s talk about it”: translating lessons from health care simulation to clinical event debriefings and coaching conversations. Clin Pediatric Emerg Med. 2016;17(3):200–11.

    Article  Google Scholar 

  36. Fanning RM, Gaba DM. The role of debriefing in simulation-based learning. Simul Healthcare. 2007;2(2):115–25.

    Article  Google Scholar 

  37. Rudolph JW, Simon R, Dufresne RL, Raemer DB. There’s no such thing as “nonjudgmental” debriefing: a theory and method for debriefing with good judgment. Simul Healthc. 2006;1(1):49–55.

    Article  PubMed  Google Scholar 

  38. Gadamer HG, Weinsheimer J, Marshall DG. Elements of a theory of hermeneutic experience. In: Truth and methods. 2. rev. ed. New York: Continuum; 1989. p. 268–306.

  39. Braun V, Clarke V. Using thematic analysis in psychology. Qual Res Psychol. 2006;3(2):77–101.

    Article  Google Scholar 

  40. Fereday J, Muir-Cochrane E. Demonstrating rigor using thematic analysis: a hybrid approach of inductive and deductive coding and theme development. Int J Qual Methods. 2006;5(1):80–92.

    Article  Google Scholar 

  41. Perkins GD, Handley AJ, Koster RW, Castren M, Smyth MA, Olasveengen T, et al. European Resuscitation Council Guidelines for Resuscitation 2015: section 2. Adult basic life support and automated external defibrillation. Resuscitation. 2015;95:81–99.

    Article  PubMed  Google Scholar 

  42. Edmondson AC, Schein EH. Teaming how organizations learn, innovate, and compete in the knowledge economy. 1st ed. San Francisco: Jossey-Bass; 2012.

    Google Scholar 

  43. Chan PS, Krumholz HM, Nichol G, Nallamothu BK. Delayed time to defibrillation after in-hospital cardiac arrest. N Engl J Med. 2008;358(1):9–17.

    Article  CAS  PubMed  Google Scholar 

  44. Chan PS, Nichol G, Krumholz HM, Spertus JA, Nallamothu BK. Hospital variation in time to defibrillation after in-hospital cardiac arrest. Arch Intern Med. 2009;169(14):1265–73.

    Article  PubMed  Google Scholar 

  45. Stærk M, Vammen L, Andersen CF, Krogh K, Løfgren B. Basic life support skills can be improved among certified basic life support instructors. Resuscitation Plus. 2021;6:100120.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Ruppert M, Reith MW, Widmann JH, Lackner CK, Kerkmann R, Schweiberer L, et al. Checking for breathing: evaluation of the diagnostic capability of emergency medical services personnel, physicians, medical students, and medical laypersons. Ann Emerg Med. 1999;34(6):720–9.

    Article  CAS  PubMed  Google Scholar 

  47. Singletary EM, Soar J, Olasveengen TM, Greif R, Liley HG, Zideman D, et al. 2021 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science With Treatment Recommendations: summary from the basic life support; advanced life support; neonatal life support; education, implementation, and teams; first aid task forces; and the COVID-19 working group. Resuscitation. 2021;169:229–311.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Bradley SM, Liu W, Chan PS, Nallamothu BK, Grunwald GK, Self A, et al. Defibrillation time intervals and outcomes of cardiac arrest in hospital: retrospective cohort study from Get With The Guidelines-Resuscitation registry. BMJ. 2016;353:i1653–i.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Kaye W, Mancini ME, Truitt TL. When minutes count--the fallacy of accurate time documentation during in-hospital resuscitation. Resuscitation. 2005;65(3):285–90.

    Article  PubMed  Google Scholar 

  50. Stewart JA. Problems with time-interval data from In-hospital resuscitation records. Int J Cardiol. 2016;223:879–80.

    Article  PubMed  Google Scholar 

  51. Deakin CD. The chain of survival: not all links are equal. Resuscitation. 2018;126:80–2.

    Article  PubMed  Google Scholar 

  52. Schmidt AS, Lauridsen KG, Adelborg K, Løfgren B. Hospital implementation of resuscitation guidelines and review of CPR training programmes: a nationwide study. Eur J Emerg Med. 2016;23(3):232–4.

    Article  PubMed  Google Scholar 

  53. Riggs M, Franklin R, Saylany L. Associations between cardiopulmonary resuscitation (CPR) knowledge, self-efficacy, training history and willingness to perform CPR and CPR psychomotor skills: a systematic review. Resuscitation. 2019;138:259–72.

    Article  PubMed  Google Scholar 

  54. Smith KK, Gilcreast D, Pierce K. Evaluation of staff’s retention of ACLS and BLS skills. Resuscitation. 2008;78(1):59–65.

    Article  PubMed  Google Scholar 

  55. Woollard M, Whitfeild R, Smith A, Colquhoun M, Newcombe RG, Vetteer N, et al. Skill acquisition and retention in automated external defibrillator (AED) use and CPR by lay responders: a prospective study. Resuscitation. 2004;60(1):17–28.

    Article  PubMed  Google Scholar 

  56. Lauridsen KG, Løfgren B, Brogaard L, Paltved C, Hvidman L, Krogh K. Cardiopulmonary resuscitation training for healthcare professionals: a scoping review. Simul Healthc. 2022;17(3):170–82. https://doi.org/10.1097/SIH.0000000000000608.

    Article  Google Scholar 

  57. Sutton RM, Niles D, Meaney PA, Aplenc R, French B, Abella BS, et al. Low-dose, high-frequency CPR training improves skill retention of in-hospital pediatric providers. Pediatrics. 2011;128(1):e145–51.

    Article  PubMed  PubMed Central  Google Scholar 

  58. Hsieh MJ, Chiang WC, Jan CF, Lin HY, Yang CW, Ma MH. The effect of different retraining intervals on the skill performance of cardiopulmonary resuscitation in laypeople-a three-armed randomized control study. Resuscitation. 2018;128:151–7.

    Article  PubMed  Google Scholar 

  59. Gilfoyle E, Duff J, Bhanji F, Scholefield B, Bray J, Bigham B, et al. Cognitive aids in resuscitation training consensus on science with treatment recommendations. 2020. Available from: https://costr.ilcor.org/document/cognitive-aids-in-resuscitation-eit-629-systematic-review. Accessed 3 Mar 2022.

  60. Mpotos N, De Wever B, Cleymans N, Raemaekers J, Valcke M, Monsieurs KG. Efficiency of short individualised CPR self-learning sessions with automated assessment and feedback. Resuscitation. 2013;84(9):1267–73.

    Article  PubMed  Google Scholar 

  61. Mpotos N, Lemoyne S, Calle PA, Deschepper E, Valcke M, Monsieurs KG. Combining video instruction followed by voice feedback in a self-learning station for acquisition of basic life support skills: a randomised non-inferiority trial. Resuscitation. 2011;82(7):896–901.

    Article  PubMed  Google Scholar 

  62. Panchal AR, Norton G, Gibbons E, Buehler J, Kurz MC. Low dose- high frequency, case based psychomotor CPR training improves compression fraction for patients with in-hospital cardiac arrest. Resuscitation. 2020;146:26–31.

    Article  PubMed  Google Scholar 

  63. Dudzik LR, Heard DG, Griffin RE, Vercellino M, Hunt A, Cates A, et al. Implementation of a low-dose, high-frequency cardiac resuscitation quality improvement program in a community hospital. Jt Comm J Qual Patient Saf. 2019;45(12):789–97.

    PubMed  Google Scholar 

  64. Krogh LQ, Bjornshave K, Vestergaard LD, Sharma MB, Rasmussen SE, Nielsen HV, et al. E-learning in pediatric basic life support: a randomized controlled non-inferiority study. Resuscitation. 2015;90:7–12.

    Article  PubMed  Google Scholar 

  65. Hsieh MJ, Bhanji F, Chiang WC, Yang CW, Chien KL, Ma MH. Comparing the effect of self-instruction with that of traditional instruction in basic life support courses-a systematic review. Resuscitation. 2016;108:8–19.

    Article  PubMed  Google Scholar 

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Acknowledgements

We thank all participating hospitals, departments, and staff. A special thanks to Rikke Højbjerg, RN; Mette Qvortrup, RN; and Rasmus Philip Nielsen, MD, for their contribution to include departments and conduct simulations. We thank Julie Niklassen, MD; Josephine Johnsen, MD; Signe Hedebo, MB; Christian Gansted, MB; Cecilie Budolfsen, MB; Kathrine Mackenhauer; Marlice Zwanenburg, MB; Maria Høybye, MB; Stine Holst Bjerre Pedersen, RN; Marine Sølling Ramsing, MB; Alexandra Amalie Uglebjerg Pedersen, MD; Sophie Fromholt; Martin Thomsen, MB; Isabella Hangaard Rüdiger, MB; Ida Norup, MD; Marie Egebjerg Jensen, MB; Louise Bentzen, MD; Kenneth Lavrsen, Reshaabi Srinanthalogen, MD; Amalie Nicolaisen; and Mette Vold Hansen, MB, for their assistance in conducting simulations.

Funding

The following generously funded the study: Randers Regional Hospital, Aarhus University, Laerdal Foundation, the A. P. Møller Foundation for the Advancement of Medical Science (Fonden til Lægevidenskabens Fremme), the Health Foundation (Helsefonden), and Health Research Foundation of Central Denmark Region. We thank Physio-Control/Stryker for providing defibrillation electrodes for the study. The funding bodies had no role in the design of the study, data collection, analysis, nor interpretation of data or in writing the manuscript.

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Authors and Affiliations

Authors

Contributions

MS, conceptualization, methodology, resources, investigation, analysis, writing — original draft, writing — review and editing, and project administration. KGL, conceptualization, methodology, investigation, writing — review and editing, and supervision. CTS, investigation, analysis, and writing — review and editing. DTN, investigation, analysis, and writing — review and editing. BL, conceptualization, methodology, writing — review and editing, supervision, and funding. KK, conceptualization, methodology, investigation, analysis, writing — review and editing, and supervision. The authors read and approved the final manuscript.

Corresponding author

Correspondence to Kristian Krogh.

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Ethics approval and consent to participate

The Regional Committee on Biomedical Research Ethics waived the need for permission and deemed the study exempt from individual consent (141/2017). The study was approved by the Danish Data Protection Agency (1-16-02-367-18). Permission to conduct the study was granted from all hospital administrations.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

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Stærk, M., Lauridsen, K.G., Støtt, C.T. et al. Inhospital cardiac arrest — the crucial first 5 min: a simulation study. Adv Simul 7, 29 (2022). https://doi.org/10.1186/s41077-022-00225-0

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Keywords

  • Inhospital cardiac arrest
  • Basic life support
  • In situ simulation