Code Team Training: Demonstrating Adherence to AHA Guidelines During Pediatric Code Blue Activations

Effects of communication team training on clinical competence in Korean Advanced Life Support: A randomized controlled trial

We conducted a randomized controlled trial to study the effects of interprofessional communication team training on clinical competence in the Korean Advanced Life Support provider course using a team communication framework. Our study involved 73 residents and 42 nurses from a tertiary hospital in Seoul. The participants were randomly assigned to the intervention or control group, forming 10 teams per group. The intervention group underwent interprofessional communication team training with a cardiac arrest simulation and standardized communication tools. The control group completed the Korean Advanced Life Support provider course. All participants completed a communication clarity self-reporting questionnaire. Clinical competence was assessed using a clinical competency scale comprising technical and nontechnical tools. Blinding was not possible due to the educational intervention. Data were analyzed using a Mann-Whitney U test and a multivariate Kruskal-Wallis H test. While no significant differences were observed in communication clarity between the two groups, there were significant differences in clinical competence. Therefore, the study confirmed that the intervention can enhance the clinical competence of patient care teams in cardiopulmonary resuscitation.

Pediatic code blue event anaylsis: Performance of non-acute health-care providers

In-hospital pediatric cardiopulmonary arrest is rare. With more than 50% of patients not surviving to discharge following cardiopulmonary arrest, it is important that health-care providers (HCPs) respond appropriately to deteriorating patients. Our study evaluated the performance of basic life support skills using non-acute HCPs during pediatric inpatient resuscitation events. We conducted a retrospective chart review of all code blue team (CBT) activations in non-acute care areas of a tertiary care children's hospital from 2008 to 2017. The main outcomes were frequency of life support algorithmic assessments and interventions (critical actions) performed by non-acute HCPs prior to the arrival of CBT. CBT activation and outcome data were summarized descriptively. Logistic regression was used to assess for an association of outcomes with the presence of established leadership. A total of 60 CBT activations were retrieved, 48 of which had data available on isolated non-acute HCP performance. Most children (93%) survived to discharge. Critical action performance review revealed that an airway, breathing and pulse assessment was documented to have occurred in 33%, 69% and 29% of cases, respectively. A full primary assessment was documented in 6% of cases. The presence of established leadership was associated with the performance of a partial ABC assessment. Our results suggest that resuscitation performance of pediatric inpatient non-acute HCPs often does not adhere to standard life support guidelines. These results highlight the need to reconsider the current approaches used for non-acute HCP resuscitation training.

Performance of a Clinical Decision Support Tool to Identify PICU Patients at High Risk for Clinical Deterioration

OBJECTIVES

To evaluate the translation of a paper high-risk checklist for PICU patients at risk of clinical deterioration to an automated clinical decision support tool.

DESIGN

Retrospective, observational cohort study of an automated clinical decision support tool, the PICU Warning Tool, adapted from a paper checklist to predict clinical deterioration events in PICU patients within 24 hours.

SETTING

Two quaternary care medical-surgical PICUs-The Children's Hospital of Philadelphia and Cincinnati Children's Hospital Medical Center.

PATIENTS

The study included all patients admitted from July 1, 2014, to June 30, 2015, the year prior to the initiation of any focused situational awareness work at either institution.

INTERVENTIONS

We replicated the predictions of the real-time PICU Warning Tool by retrospectively querying the institutional data warehouse to identify all patients that would have flagged as high-risk by the PICU Warning Tool for their index deterioration.

MEASUREMENTS AND MAIN RESULTS

The primary exposure of interest was determination of high-risk status during PICU admission via the PICU Warning Tool. The primary outcome of interest was clinical deterioration event within 24 hours of a positive screen. The date and time of the deterioration event was used as the index time point. We evaluated the sensitivity, specificity, positive predictive value, and negative predictive value of the performance of the PICU Warning Tool. There were 6,233 patients evaluated with 233 clinical deterioration events experienced by 154 individual patients. The positive predictive value of the PICU Warning Tool was 7.1% with a number needed to screen of 14 patients for each index clinical deterioration event. The most predictive of the individual criteria were elevated lactic acidosis, high mean airway pressure, and profound acidosis.

CONCLUSIONS

Performance of a clinical decision support translation of a paper-based tool showed inferior test characteristics. Improved feasibility of identification of high-risk patients using automated tools must be balanced with performance.

Code blue pit crew model: A novel approach to in-hospital cardiac arrest resuscitation

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.

No more pediatric code blues on the floor: evolution of pediatric rapid response teams and situational awareness plans

Reducing or eliminating code blues that occur on the inpatient, noncritical care units of children's hospitals is a challenging yet achievable goal. The mechanism to accomplish this involves several levels of effort. The implementation of effective pediatric rapid response teams is a well identified part of the process. Rapid response teams can allow for appropriate clinical interventions for deteriorating patients and may ultimately result in a reduction in hospital-wide mortality as well as efficient transfer to the pediatric intensive care unit (PICU) when necessary. The timely deployment of rapid response teams is dependent upon the appropriate recognition of patients at risk for deterioration. This recognition can be optimized by relying on assessments as simple as utilization of parental intuition to those as complex as big data models which utilize multiple predictor variables extracted from the electronic medical record. Ultimately, the goal to proactively identify patients at risk of deterioration may allow for prevention of clinical decline via appropriate and timely interventions, and if unsuccessful at that level, may allow for improved outcomes via optimized resuscitation care in the PICU.