Use of a simulation-based advanced resuscitation training curriculum: Impact on cardiopulmonary resuscitation quality and patient outcomes

Abstract

Background

Despite a continued focus on improved cardiopulmonary resuscitation quality, survival remains low from in-hospital cardiac arrest. Advanced Resuscitation Training has been shown to improve survival to hospital discharge and survival with good neurological outcome following in-hospital cardiac arrest at its home institution. We sought to determine if Advanced Resuscitation Training implementation would improve patient outcomes and cardiopulmonary resuscitation quality at our institution.

Methods

This was a prospective, before–after study of adult in-hospital cardiac arrest victims who had cardiopulmonary resuscitation performed. During phase 1, standard institution cardiopulmonary resuscitation training was provided. During phase 2, providers received the same quantity of training, but with emphasis on Advanced Resuscitation Training principles. Primary outcomes were return of spontaneous circulation, survival to hospital discharge, and neurologically favorable survival. Secondary outcomes were cardiopulmonary resuscitation quality parameters.

Results

A total of 156 adult in-hospital cardiac arrests occurred during the study period. Rates of return of spontaneous circulation improved from 58.1 to 86.3% with an adjusted odds ratios of 5.31 (95% CI: 2.23–14.35, P < 0.001). Survival to discharge increased from 26.7 to 41.2%, adjusted odds ratios 2.17 (95% CI: 1.02–4.67, P < 0.05). Survival with a good neurological outcome increased from 24.8 to 35.3%, but was not statistically significant. Target chest compression rate increased from 30.4% of patients in P1 to 65.6% in P2, adjusted odds ratios 4.27 (95% CI: 1.72–11.12, P = 0.002), and target depth increased from 23.2% in P1 to 46.9% in P2, adjusted odds ratios 2.92 (95% CI: 1.16–7.54, P = 0.024).

Conclusions

After Advanced Resuscitation Training implementation, there were significant improvements in cardiopulmonary resuscitation quality and rates of return of spontaneous circulation and survival to discharge.

Keywords

Cardiac arrest cardiopulmonary resuscitation chest compressions simulation

Introduction

Each year, over 200,000 patients suffer an in-hospital cardiac arrest (IHCA) with less than 25% surviving to hospital discharge.^1,2 With such poor outcomes, many medical institutions have strived to maximize the training of providers responding to IHCA. The most common training program is Advanced Cardiovascular Life Support (ACLS) which has been shown to increase 30-day and 1-year IHCA survival rates.³ ACLS guidelines have been revised multiple times over the last two decades, and current ACLS recommendations focus on the importance of good cardiopulmonary resuscitation (CPR) quality, including optimal compression rate, depth, and recoil as well as minimizing pauses in chest compressions.⁴ By focusing on both high-quality, uninterrupted chest compressions and timely defibrillation, hospitals have seen some improvement in outcomes.^5–8 Despite this focused training, evidence indicates that CPR quality remains sub-optimal, and overall outcomes remain poor.^5,9

Recent ACLS training classes have emphasized simulation-based training, as this modality appears to be superior to traditional medical education¹⁰). Although many studies on simulation-based ACLS training have demonstrated improvements in CPR quality and provider performance during cardiac arrest resuscitation, others have found no improvement, and rarely has an effect been seen on patient outcomes.^11–14

Based on this previous data, it would seem that a more focused learning process dedicated to resuscitating IHCA is needed in order to make a significant impact on outcomes. To be maximally effective, this learning activity would focus on the current fundamentals of quality CPR, involve high-fidelity simulation as the primary learning tool, have dedicated debriefing and feedback sessions to focus learner skills, and be repeated multiple times throughout the year to prevent skill and knowledge decay. In 2007, Davis et al.¹⁵ implemented such an optimized training program, Advanced Resuscitation Training (ART), at the University of California at San Diego and demonstrated improvements in overall survival-to-hospital discharge and good neurological outcome as well as a decrease in the incidence of non-intensive care unit (ICU) cardiac arrests.¹⁵

Although ART was successful at its base institution, there have been no further studies to assess its effectiveness. In this analysis, we sought to determine whether ART would produce similar improvements in CPR quality and outcomes at our academic medical center.

Methods

Clinical setting

Data were obtained from a suburban 263-bed private academic medical center with an average of 12,101 annual admissions during the study period. There is a round-the-clock resuscitation team which responds to all cardiac arrest events, except for those in the operating room and in the emergency department (ED). The resuscitation team is comprised of a critical care attending and fellow, internal medicine residents, a pharmacist, a respiratory therapist, and ACLS-trained nurses. A second-year internal medicine resident is assigned to lead all resuscitations; however, it is estimated that the resident leads less than 5% of ICU arrests and approximately 30% of arrests occurring outside of the ICU. The critical care attending leads the remainder of resuscitation attempts and provides direct supervision to the residents when they are leading.

Throughout the study period, CPR quality was monitored using a Food and Drug Administration-approved monitor–defibrillator (R-series; ZOLL Medical, Chelmsford, MA, USA). The monitor provides real-time audio-visual feedback (RTAVF) and records CPR quality. The defibrillator pads incorporate accelerometer-based technology to measure CPR metrics including compression rate, depth, fraction, and pre-shock pause. During resuscitation attempts, a numerical display on the monitor provides dynamic compression-to-compression rate and depth measurements. When the rate falls below 80 compressions per minute (cpm) or the compression depth falls below 51 mm (2 in.), the respective numerical display is highlighted in a red box; for inadequate compression depth, this visual cue is accompanied by an audio prompt instructing the compressor to “push harder.”

This study was part of an ongoing cardiac resuscitation quality improvement (QI) program and was determined to satisfy the requirements of minimal risk research. Waiver of the requirement to obtain informed consent and HIPAA authorization were approved by the Mayo Clinic Institutional Review Board (IRB application number 17-001659).

Study design

This was a prospective, before–after study of consecutive adult patients who experienced an IHCA and had CPR performed. Out-of-hospital cardiac arrests presenting to the ED, cardiac arrests occurring in the ED, and patients with a Do Not Resuscitate order were excluded from the study.

During the 34-month phase 1 (P1; August 1, 2013–May 31, 2016) of the study, hour-long twice monthly CPR training sessions were made available to the internal medicine residents. These training sessions included didactics, hands-on components, and high-fidelity simulation. It is estimated that each resident attended approximately six training sessions per year. The rest of the members of the code team (critical care physicians, nurses, pharmacists, and respiratory technicians) underwent standard ACLS training during the P1 period. Additionally, in situ mock codes were held on an every-other-month basis. During these in situ events, on-call code team providers performed their initial resuscitation and were debriefed by resuscitation educators immediately afterward. In situ events were limited to 20 min, given the ongoing patient care responsibilities. These mock codes are a multidisciplinary exercise, involving all members of the actual code team.

An educational pilot program, termed Advanced Resuscitation Training, was provided across the institution from January 2016 through May 2016. ART education was accomplished through a series of sessions conducted exclusively by the ART program originator. These sessions focused on ART principles including early recognition of the deteriorating patient and preventing arrest, the importance of early defibrillation, the impact of high-quality chest compressions including increased chest compression fraction (CCF) (i.e. the percentage of resuscitation time dedicated to doing chest compressions), minimizing the pre-shock pause in chest compressions prior to defibrillation, the use of RTAVF including end-tidal CO₂, and crucial post-arrest care. The program not only utilizes standardized didactics and hands-on skills stations, but also a significant amount of high-fidelity simulation for training. The target audiences for ART education were code team members, resuscitation educators, and ICU/Progressive Care Unit nurses.

During the 12-month phase 2 (P2; June 1, 2016–May 31, 2017), providers received the same quantity of CPR training, but with emphasis on the ART principles. Throughout the entire study period, code team leaders received debriefings regarding their individual cases of arrest management. Details of the resuscitation, including specific CPR quality data and opportunities for improvement, were presented in a single-page written format. Visual CPR metric data were created using Code Review (ZOLL Medical, Chelmsford, MA, USA) software.

The member composition of the code team was kept the same throughout both phases of data collection, consisting of a critical care attending and fellow, a junior and senior internal medicine resident, a pharmacist, a respiratory therapist, and ACLS-trained nurses.

Data collection and processing

Members of the resuscitation committee extracted Utstein-style demographic,¹⁶ code response, and outcome data from paper code forms (filled out in real-time by a dedicated nurse) and the electronic medical record on a monthly basis. The data were then entered manually into MIDAS + (Conduent, Florham Park, NJ, USA) for ongoing QI. CPR quality data were downloaded by pharmacists not involved in the arrest events and who were otherwise tasked with restocking the used code carts. From the beginning of the study period through 8/01/15, this process was performed using a data card. Subsequent to 8/01/15, this process was streamlined by wireless technology, but still required a manual process which continued to be performed by a pharmacist. Compliance was variable in both study periods, and it improved after implementation of wireless technology.

The primary outcomes were successful return of spontaneous circulation (ROSC), survival to hospital discharge, and neurologically favorable survival, defined as a Cerebral Performance Category (CPC) score of 1 or 2. CPCs were classified as follows: (1) good cerebral performance, (2) moderate cerebral disability, (3) severe cerebral disability, (4) coma, and (5) death.^16,17 Secondary outcomes were CPR quality measures, which included compression rate of 100–120/min, depth of 2.5 in. or greater, CCF greater than 80%, and pre-shock pause less than five seconds. A depth of 2.5 in. was used, given previous data regarding accelerometers also detecting mattress displacement.¹⁸ Confounders and risk factors considered were: age, sex, location of arrest, initial arrest rhythm, and cause of arrest as determined by resuscitation committee chart review.

Statistical analysis

Summary data are presented as median with interquartile range for continuous data or as number and percentages for categorical data. The chi-square test or Fisher’s exact test and the Wilcoxon rank-sum test were used for univariate comparisons between P1 and P2 for categorical and continuous patient characteristics, respectively. Associations of phase with patient outcomes and CPR quality outcomes were evaluated using logistic regression models; odds ratios (P2 vs. P1) and 95% confidence intervals are reported. In an attempt to control for confounding, multivariable logistic regression models were performed for each patient outcome and included established confounders as well as patient characteristics that differed between the two phases (P < 0.05) as covariates in the model allowing no more than one variable in the model for every 10 patients who experienced the less frequent outcome. All tests were two-sided and performed at the 0.05 significance level without adjustment for multiple testing. Analyses were performed using SAS version 9.4 (SAS Institute Inc., Cary, NC).

Results

Learner data

A total of 314 learners underwent the initial ART education. This included 31 internal medicine residents (post-graduate years 1, 2, and 3), four critical care fellows, five critical care attendings, 206 nurses, two pharmacists, and 65 respiratory therapists and rapid response team members. All learners underwent a 1-h didactic session and between one and three hours of hands-on training and practice using high-fidelity simulation.

Patient demographics

A total of 156 (105 P1, 51 P2) consecutive adult IHCAs occurred during the study period. CPR quality data were available for 56 cases in P1 and 32 in P2. The median age of study subjects was 66 years and 65.4% were male. The most common causes of arrest were loss of blood pressure in 63 patients (40.4%), followed by respiratory distress in 32 patients (20.5%), and arrhythmia in 28 patients (17.9%). The initial arrest rhythm was shockable in 36 patients (23.1%), PEA in 91 patients (58.3%), and asystole in 29 patients (18.6%). Overall, ROSC was achieved in 105 of 156 patients (67.3%) and 49 patients (31.4%) survived to hospital discharge.

Patient characteristics for all patients and differences in patient characteristics between the two study periods are presented in Table 1. Compared to patients in P1, patients in P2 tended to be younger (median: 62 vs. 67 years, P = 0.023) and were more likely to arrest in the ICU (76.5% vs. 54.3%, P = 0.008).

Table 1.

Characteristics of all patients and separately according to study phase.

Characteristic	All patients (N = 156)	Phase 1 (n = 105)	Phase 2 (n = 51)	P value
Median age (IQR) (year)	66 (57, 75)	67 (58, 78)	62 (56, 70)	0.023
Male sex (n (%))	102 (65.4%)	65 (61.9%)	37 (72.5%)	0.19
ICU (n (%))	96 (61.5%)	57 (54.3%)	39 (76.5%)	0.008
Cause of arrest (n (%))				0.53
Respiratory distress	32 (20.5%)	24 (22.9%)	8 (15.7%)
Loss of blood pressure	63 (40.4%)	43 (41.0%)	20 (39.2%)
Loss of blood pressure—arrhythmia	28 (17.9%)	16 (15.2%)	12 (23.5%)
Other	33 (21.2%)	22 (21.0%)	11 (21.6%)
Initial rhythm (n (%))				0.32
Asystole	29 (18.6%)	22 (21.0%)	7 (13.7%)
Pulseless electrical activity	91 (58.3%)	62 (59.0%)	29 (56.9%)
VF/VT	36 (23.1%)	21 (20.0%)	15 (29.4%)

P values for comparisons of patient characteristics between phases 1 and 2 result from the Wilcoxon rank sum test for age and either the chi-square test or Fisher’s exact test for categorical variables.

IQR: interquartile range; ICU: intensive care unit; VF: ventricular fibrillation; VT: ventricular tachycardia.

Patient outcomes

Associations of phase with patient outcomes are shown in Table 2. Incidence of ROSC significantly improved from 58.1% in P1 (61/105) to 86.3% (44/51) in P2 with an adjusted odds ratio (aOR) of 5.31 (95% CI: 2.23–14.35, P < 0.001) after controlling for age, location of arrest, and an initial shockable rhythm. Survival to discharge increased from 26.7% (28/105) in P1 to 41.2% (21/51) in P2; aOR 2.17 (95% CI: 1.02–4.67, P < 0.05). Survival with a favorable neurological outcome (i.e. CPC 1–2) increased from 24.8% in P1 (26/105) to 35.3% in P2 (18/51), but was not statistically significant; aOR 1.91 (95% CI: 0.87–4.22), P = 0.11.

Table 2.

Associations of study phase with patient outcomes.

Patient outcomes	Phase 1	Phase 2	Single variable		Multivariable
Patient outcomes	Fraction (%)	Fraction (%)	OR (95% CI)	P value	OR (95% CI)	P value
Return of spontaneous circulation	61/105 (58.1%)	44/51 (86.3%)	4.53 (1.97–11.86)	<0.001	5.31 (2.23–14.35)	<0.001
Neurologically favorable survival	26/105 (24.8%)	18/51 (35.3%)	1.66 (0.80–3.42)	0.17	1.91 (0.87–4.22)	0.11
Survived to discharge	28/105 (26.7%)	21/51 (41.2%)	1.93 (0.95–3.91)	0.069	2.17 (1.02–4.67)	0.046

The fraction and percentage of patients with each outcome are given separately for phases 1 and 2. Neurologically favorable survival was defined as a Cerebral Performance Category score of 1 (good cerebral performance) or 2 (moderate cerebral disability). Odds ratios (OR) and 95% confidence intervals (CIs) for the association of study phase (phase 2 vs. phase 1) with patient outcomes result from single variable and multivariable logistic regression models. Multivariable logistic regression models included patient age, patient located in the intensive care unit at the time of arrest (yes vs. no), and an initial arrest rhythm of ventricular tachycardia or ventricular fibrillation (yes vs. no).

CPR quality

Association of study phase with the target range for each CPR quality measure is shown in Table 3. A target rate of 100–120 compressions/min increased from 30.4% of patients (17/56) in P1 to 65.6% of patients (21/32) in P2 with an OR of 4.27 (95% CI: 1.72–11.12, P = 0.002). A target depth of 2.5 in. or more increased from 23.2% of patients (13/56) in P1 to 46.9% of patients (15/32) in P2 with an unadjusted OR of 2.92 (95% CI: 1.16–7.54, P = 0.024). The percentage of patients with a CCF > 80% was high in P1 (92.7%, 51/55) and P2 (90.6%, 29/32) and did not differ significantly between phases (P = 0.73). A target pre-shock pause of five seconds or less occurred in 61.9% of patients in P1 (13/21) and 69.2% of patients in P2 (9/12) with an unadjusted OR of 1.39 (95% CI: 0.32–6.03, P = 0.66).

Table 3.

Association of study phase with CPR quality outcomes.

CPR quality outcomes	Phase 1	Phase 2	Single variable
CPR quality outcomes	Fraction (%)	Fraction (%)	OR (95% CI)	P value
Compression rate 100–120/min	17/56 (30.4%)	21/32 (65.6%)	4.27 (1.72–11.12)	0.002
Depth ≥ 2.5 in	13/56 (23.2%)	15/32 (46.9%)	2.92 (1.16–7.54)	0.024
Chest compression fraction > 80%	51/55 (92.7%)	29/32 (90.6%)	0.76 (0.16–4.07)	0.73
Pre-shock pause ≤ 5 s	13/21 (61.9%)	9/13 (69.2%)	1.39 (0.32–6.03)	0.66

The fraction and percentage of patients with each outcome is given separate for phases 1 and 2. OR and 95% confidence intervals (CIs) for the association of study phase (phase 2 vs. phase 1) with CPR quality outcomes result from single variable logistic regression models.

CIs: confidence intervals; OR: odds ratios.

Median chest compression rate was 125 compressions per minute in P1 (IQR, 117–130) and 118 compressions per minute in P2 (IQR, 112–125). The median depth was 2.4 in. in P1 (IQR, 2.1–2.5) and 2.5 in. in P2 (IQR, 2.1–2.9). The median CCF was 91% in P1 (IQR, 86–93%) and 93% in P2 (IQR, 89–94%). The median pre-shock pause was 4.0 s in P1 (IQR, 3.0–6.5) and 4.0 s in P2 (IQR, 3.0–6.0).

Discussion

Our study suggests that implementation of the more focused IHCA resuscitation program, ART, may have a significant impact on both CPR quality and patient outcomes when compared to traditional ACLS training. Despite the high quality of CPR in P1, we were still able to show significant improvements in the frequency of compressions in the target range for rate (30.4% vs. 65.6%, P = 0.002) and depth (23.2% vs. 46.9%, P = 0.024) after the implementation of ART. One possible reason for this significant difference is that unlike ACLS, which focuses on multiple cardiac arrest algorithms, medications, and data interpretation, ART focuses on two key factors: early defibrillation and uninterrupted, high-quality chest compressions. By directing the focus of IHCA resuscitation to these factors, ART steps away from ACLS’s concept of knowing the basics of many things to total immersion in the two concepts that have been linked to improved patients outcomes.^5–8 A second possible reason that ART was more successful in teaching learners target rate and depth was its core philosophy of required repetition of multiple learning platforms (i.e. standard didactics, hands-on skill stations, and high-fidelity simulation). Because all learners do not learn the same, using multiple teaching modalities to learn the core concepts of ART may reach more learners than standard didactic teaching. Moreover, requiring standardized repetition of the core concepts of ART likely leads to better retention and perhaps also contributed to our findings. Previous studies have shown that ACLS training is usually a 1–2 day exercise, and a decay of skills can be seen as early as three months after training, with less than 85% knowledge retention at 1 year.^19,20 This may be why hospital survival to discharge rates after IHCA has improved with ACLS efforts over the last 15 years, but the overall impact has been minimal with the best reported survival rates typically less than 30%.^1,2,21

Our study improves upon the original findings of Davis et al. as we were able to compare pre- and post-intervention CPR quality, something that Davis et al were not able to do.¹⁵ Despite improvements in compressions within the target range, we did not show improvements in means for the chest compression quality metrics. This could be because of our strong means in phase 1 and also shows the inadequacy of using means to describe CPR quality.

Our study additionally found a significant difference in ROSC in P2 (58.1% vs. 86.3%, P = 0.002) as well as a significant increase in rate of survival to hospital discharge (26.7% vs. 41.2%, P < 0.05). One possible reason leading to this improvement is the focus on early defibrillation with ART. Our study identified a shockable rhythm was present in 23.1% of the study patients. These patients received immediate defibrillation, which may have increased the likelihood of ROSC. Equally important, early identification of those patients without a shockable rhythm and focussing on continuous high-quality chest compressions as opposed to multiple chest compression pauses for rhythm checks may have benefited these non-shockable patients, accounting for their improvement in ROSC and survival. There was also a promising trend toward survival with good neurologic outcome (28.4 % vs. 35.5%, P = 0.17), but unfortunately, our study was underpowered to detect statistical significance in this latter outcome. As other locations within our system implement this training, further data may be available to assess its impact on neurologic outcomes.

Previous studies have demonstrated the impact of high quality CPR and uninterrupted cerebral and myocardial blood flow during resuscitation.^21–31 Our study indicates that ART may be an optimal way of teaching high quality CPR over previously studied modalities. Furthermore, our study shows that implementation of ART resulted insignificant improvements in achieving target compression rate and depth, two qualities that multiple previous studies have repeatedly linked to improved outcomes.^{5,22–24,27–32} While our study did not show a significant improvement in target chest compression fraction and pre-shock pause, our measured quality for these factors met national recommendations prior to implementation of ART.

Our study is not without limitations. First, our study was conducted at a single academic tertiary-care referral center which may impact the generalizability of our findings. Second, we had a somewhat small sample size which may limit our ability to detect clinically meaningful associations; therefore, the possibility of a type II error (i.e. false-negative finding) should be considered. Third, the P1 and P2 groups were not the same. Our P2 group was younger (median: 62 vs. 67 years, P = 0.023) and more likely to arrest in the ICU (76.5% vs. 54.3%, P = 0.008). One possible explanation for this may be that another component of ART focuses on early recognition and intervention to prevent IHCA, which may have contributed this difference, as patients were transferred to the ICU earlier in the course of their illness. We, however, controlled for these factors. Additionally, the learners of our P1 group only included internal medicine residents, where the P2 group also included senior physicians, nurses, and other code team members. Although the P1 learning group consisted of only internal medicine residents, all hospital staff involved in any resuscitation event prior to the initiation of ART was still required to go through standard ACLS training. Finally, we had a significant amount of missing CPR quality data; we did not have data on compression rate, depth, and fraction for 49 patients (46.7%) of the P1 group and 19 patients (37.3%) in the P2 group, and we did not have data on pre-shock pauses for 15 patients (41.7%) in the P1 ventricular fibrillation/tachycardia group and eight patients (38.1%) in the P2 ventricular fibrillation/tachycardia group. We have no reason to believe that this introduced bias given that the pharmacists responsible for downloading the defibrillator data had no awareness of the actual arrest events.

Conclusion

We found that following ART implementation there was a significant improvement in both CPR quality and in the rates of return of spontaneous circulation and survival to hospital discharge. We also found a nonsignificant trend toward increased neurologically favorable survival to discharge. Further studies powered to find significance of the neurological outcomes should be conducted. Based on our findings, other institutions should consider ART implementation in order to improve both CPR quality and IHCA outcomes.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

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