Best Practices for Education and Training of Resuscitation Teams for In-Hospital Cardiac Arrest

Ward-based in situ simulation: lessons learnt from a UK District General Hospital

INTRODUCTION

In situ simulation (ISS) enables multiprofessional healthcare teams to train for real emergencies in their own working environment and identify latent patient safety threats. This study aimed to determine ISS impact on teamwork, technical skill performance, healthcare staff perception and latent error identification during simulated medical emergencies.

MATERIALS AND METHODS

Unannounced ISS sessions (n=14, n=75 staff members) using a high-fidelity mannequin were conducted in medical, paediatric and rehabilitation wards at Stepping Hill Hospital (Stockport National Health Service Foundation Trust, UK). Each session encompassed a 15 min simulation followed by a 15 min faculty-led debrief.

RESULTS

The clinical team score revealed low overall teamwork performances during simulated medical emergencies (mean±SEM: 4.3±0.5). Linear regression analysis revealed that overall communication (r=0.9, p<0.001), decision-making (r=0.77, p<0.001) and overall situational awareness (r=0.73, p=0.003) were the strongest statistically significant predictors of overall teamwork performance. Neither the number of attending healthcare professionals, their professional background, age, gender, degree of clinical experience, level of resuscitation training or previous simulation experience statistically significantly impacted on overall teamwork performance. ISS positively impacted on healthcare staff confidence and clinical training. Identified safety threats included unknown location of intraosseous kits, poor/absent airway management, incomplete A-E assessments, inability to activate the major haemorrhage protocol, unknown location/dose of epinephrine for anaphylaxis management, delayed administration of epinephrine and delayed/absence of attachment of pads to the defibrillator as well as absence of accessing ALS algorithms, poor chest compressions and passive behaviour during simulated cardiac arrests.

CONCLUSION

Poor demonstration of technical/non-technical skills mandate regular ISS interventions for healthcare professionals of all levels. ISS positively impacts on staff confidence and training and drives identification of latent errors enabling improvements in workplace systems and resources.

A novel structured debriefing program for consensus determinations of in-hospital cardiac arrest predictability and preventability

AIM

Hospital rapid response systems aim to stop preventable cardiac arrests, but defining preventability is a challenge. We developed a multidisciplinary consensus-based process to determine in-hospital cardiac arrest (IHCA) preventability based on objective measures.

METHODS

We developed an interdisciplinary ward IHCA debriefing program at an urban quaternary-care academic hospital. This group systematically reviewed all IHCAs weekly, reaching consensus determinations of the IHCA's cause and preventability across three mutually exclusive categories: 1) unpredictable (no evidence of physiologic instability < 1 h prior to and within 24 h of the arrest), 2) predictable but unpreventable (meeting physiologic instability criteria in the setting of either a poor baseline prognosis or a documented goals of care conversation) or 3) potentially preventable (remaining cases).

RESULTS

Of 544 arrests between 09/2015 and 11/2023, 339 (61%) were deemed predictable by consensus, with 235 (42% of all IHCAs) considered potentially preventable. Potentially preventable arrests disproportionately occurred on nights and weekends (70% vs 55%, p = 0.002) and were more frequently respiratory than cardiac in etiology (33% vs 15%, p < 0.001). Despite similar rates of ROSC across groups (67-70%), survival to discharge was highest in arrests deemed unpredictable (31%), followed by potentially preventable (21%), and then those deemed predictable but unpreventable which had the lowest survival rate (16%, p = 0.007).

CONCLUSIONS

Our IHCA debriefing procedures are a feasible and sustainable means of determining the predictability and potential preventability of ward cardiac arrests. This approach may be useful for improving quality benchmarks and care processes around pre-arrest clinical activities.

Effects of Prebriefing Using Online Team-Based Learning in Advanced Life Support Education for Nurses

An effective prebriefing strategy is needed that can improve the learning outcomes of nurses in advanced life support education. This study aimed to identify the effects of prebriefing with online team-based learning on hospital nurses' knowledge, performance, and self-efficacy in advanced life support education. A nonequivalent control group pretest-posttest design was adopted. Nurses in the experimental group (n = 26) participated in prebriefing using online team-based learning followed by self-directed learning, whereas nurses in the control group (n = 27) experienced only self-directed learning before advanced life support education. Wilcoxon signed-ranks tests were used to identify the posttest-pretest differences of the study variables in each group. Both groups showed improved knowledge, individual performance, and self-efficacy after the education. Nurses in the experimental group reported higher self-efficacy scores compared with those in the control group. There were no differences between the experimental and control groups in knowledge, individual performances, or team performance. Online team-based learning as a prebriefing modality resulted in greater improvements in self-efficacy in advanced life support education.

Data-driven resuscitation training using pose estimation

BACKGROUND

Cardiopulmonary resuscitation (CPR) training improves CPR skills while heavily relying on feedback. The quality of feedback can vary between experts, indicating a need for data-driven feedback to support experts. The goal of this study was to investigate pose estimation, a motion detection technology, to assess individual and team CPR quality with the arm angle and chest-to-chest distance metrics.

METHODS

After mandatory basic life support training, 91 healthcare providers performed a simulated CPR scenario in teams. Their behaviour was simultaneously rated based on pose estimation and by experts. It was assessed if the arm was straight at the elbow, by calculating the mean arm angle, and how close the distance between the team members was during chest compressions, by calculating the chest-to-chest distance. Both pose estimation metrics were compared with the expert ratings.

RESULTS

The data-driven and expert-based ratings for the arm angle differed by 77.3%, and based on pose estimation, 13.2% of participants kept the arm straight. The chest-to-chest distance ratings by expert and by pose estimation differed by 20.7% and based on pose estimation 63.2% of participants were closer than 1 m to the team member performing compressions.

CONCLUSIONS

Pose estimation-based metrics assessed learners' arm angles in more detail and their chest-to-chest distance comparably to expert ratings. Pose estimation metrics can complement educators with additional objective detail and allow them to focus on other aspects of the simulated CPR training, increasing the training's success and the participants' CPR quality.

TRIAL REGISTRATION

Not applicable.

Updating the model for Risk-Standardizing survival for In-Hospital cardiac arrest to facilitate hospital comparisons

BACKGROUND

Risk-standardized survival rates (RSSR) for in-hospital cardiac arrest (IHCA) have been widely used for hospital benchmarking and research. The novel coronavirus 2019 (COVID-19) pandemic has led to a substantial decline in IHCA survival as COVID-19 infection is associated with markedly lower survival. Therefore, there is a need to update the model for computing RSSRs for IHCA given the COVID-19 pandemic.

METHODS

Within Get With The Guidelines®-Resuscitation, we identified 53,922 adult patients with IHCA from March, 2020 to December, 2021 (the COVID-19 era). Using hierarchical logistic regression, we derived and validated an updated model for survival to hospital discharge and compared the performance of this updated RSSR model with the previous model.

RESULTS

The survival rate was 21.0% and 20.8% for the derivation and validation cohorts, respectively. The model had good discrimination (C-statistic 0.72) and excellent calibration. The updated parsimonious model comprised 13 variables-all 9 predictors in the original model as well as 4 additional predictors, including COVID-19 infection status. When applied to data from the pre-pandemic period of 2018-2019, there was a strong correlation (r = 0.993) between RSSRs obtained from the updated and the previous models.

CONCLUSION

We have derived and validated an updated model to risk-standardize hospital rates of survival for IHCA. The updated model yielded RSSRs that were similar to the initial model for IHCAs in the pre-pandemic period and can be used for supporting ongoing efforts to benchmark hospitals and facilitate research that uses data from either before or after the emergence of COVID-19.