Optimal paramedic numbers in resuscitation of patients with out-of-hospital cardiac arrest: A randomized controlled study in a simulation setting.

BACKGROUND: The effect of paramedic crew size in the resuscitation of patients with out-of-hospital cardiac arrest (OHCA) remains inconclusive. We hypothesised that teams with a larger crew size have better resuscitation performance including chest compression fraction (CCF), advanced life support (ALS), and teamwork performance than those with a smaller crew size.
METHODS: We conducted a randomized controlled study in a simulation setting. A total of 140 paramedics from New Taipei City were obtained by stratified sampling and were randomly allocated to 35 teams with crew sizes of 2, 3, 4, 5, and 6 (i.e. 7 teams in every paramedic crew size). A scenario involving an OHCA patient who experienced ventricular fibrillation and was attached to a cardiopulmonary resuscitation (CPR) machine was simulated. The primary outcome was the overall CCF; the secondary outcomes were the CCF in manual CPR periods, time from the first dose of epinephrine until the accomplishment of intubation, and teamwork performance. Tasks affecting the hands-off time during CPR were also analysed.
RESULTS: In all 35 teams with crew sizes of 2, 3, 4, 5, and 6, the overall CCFs were 65.1%, 64.4%, 70.7%, 72.8%, and 71.5%, respectively (P = 0.148). Teams with a crew size of 5 (58.4%, 61.8%, 68.9%, 72.4%, and 68.7%, P<0.05) had higher CCF in manual CPR periods and better team dynamics. Time to the first dose of epinephrine was significantly shorter in teams with 4 paramedics, while time to completion of intubation was shortest in teams with 6 paramedics. Troubleshooting of M-CPR machine decreased the hands-off time during resuscitation (39 s), with teams comprising 2 paramedics having the longest hands-off time (63s).
CONCLUSION: Larger paramedic crew size (≧4 paramedics) did not significantly increase the overall CCF in OHCA resuscitation but showed higher CCF in manual CPR period before the setup of the CPR machine. A crew size of ≧4 paramedics can also shorten the time of ALS interventions, while teams with 5 paramedics will have the best teamwork performance. Paramedic teams with a smaller crew size should focus more on the quality of manual CPR, teamwork, and training how to troubleshoot a M-CPR machine.

RCT Entities:   Population Interventions Outcomes

Introduction

Background

Out-of-hospital cardiac arrest (OHCA) is one of the most critical situations that both paramedics and patients can experience in the prehospital setting. Over 176,100 cardiac patients were treated by emergency medical service (EMS) personnel in the United States annually.[1] In Taiwan, 9,815 OHCA cases per year were reported between 2000 and 2012, with an average survival rate of 9.8% at 180 days.[2] Recent studies found that chest compression fraction (CCF) during cardiopulmonary resuscitation (CPR) is strongly associated with survival rate,[3,4] and the widely adopted mechanical CPR (M-CPR) machine, which provides continuous chest compression, has the potential to increase the CCF during the entire course of resuscitation in the prehospital setting.[5] Previous studies also showed the different effects of several advanced life support (ALS) interventions, such as intubation or adrenaline (epinephrine) administration, on OHCA patients.[5-7] As the aforementioned interventions were performed by paramedics in the prehospital setting, the number of paramedics in the scene will certainly have a potential influence on the performance of these interventions.

Importance

Paramedic crew size was supposed to be positively associated with the process and outcome of resuscitation of OHCA patients, but this viewpoint is still under debate.[8-12] Theoretically, the more manpower in the field, the earlier the interventions could be achieved, including regular handover to maintain high-quality CPR, higher CCF ratio, rapid defibrillation, administration of resuscitation drug, and better teamwork performance.[11-14] However, some studies showed controversial opinions. One simulation-based study reported no difference in CPR time between teams with 2 paramedics and those with 4 paramedics.[10] However, another retrospective study showed that a larger crew size of up to 7 paramedics was associated with higher survival to discharge rate.[12] Moreover, how the crew size affects the resuscitation processes including manual CPR quality, deployment of mechanical CPR machine, time to completion of advanced life support interventions, and teamwork dynamics also remained unclear. This issue is an important knowledge gap for OHCA resuscitation in the prehospital setting, policy-makers, and training curriculum in worldwide EMS.

Goals of this investigation

To help determine the best crew size for resuscitation of an OHCA patient in the field before ambulance transportation, we designed a simulation-based randomised controlled trial with cardiac arrest scenario equipped with widely adopted mechanical CPR to test different crew sizes. We hypothesised that a larger crew would have better resuscitation performance including CCF, time to advanced life supports, and team dynamics than a smaller crew. The tasks that can possibly affect the hands-off time during CPR were also analysed.

Methods

Trial design study design and oversight

This study was a scenario-based randomised controlled trial held within the paramedic continuing education program of the New Taipei City Fire Department from September 1, 2018 to January 31, 2019. The study protocol was approved by ethics board of National Taiwan University Hospital.

Participants study population and design

Among all 399 paramedics from all over New Taipei City Fire Department that attended continuous education program, 140 participants, selected through random sampling, were included in the trial. Those who were unwilling or physically unsuitable to attend this study were excluded.

Sample size calculation

This trial was designed to detect an absolute difference of 25 percentage points in the CCF between the highest (90% estimated) and lowest (65% estimated) study groups based on the previous statistic data from department training, with the use of a two-sided alpha level of 0.05 and a power of 80%.

Implementation and randomisation

First, we labelled the paramedics who were included in the continuous education program and then used stratified sampling based on the ratio of paramedics from every district of the city to select the initial 140 paramedics as study participants. After obtaining the informed consent from all participants, we collected their baseline data. Then, the participants were labelled again and were randomly allocated to five groups with different crew sizes (i.e. crew size groups of 2, 3, 4, 5, and 6). Every group contained 7 teams to be tested in the same scenario.

Sequence generation and allocation concealment

All randomisation sequences were computer generated from a randomise sequence generating website.[15]

Interventions and setting

After allocating all participants, all teams were given the same simulation scenario: “A 60-year-old man called 119 due to severe chest pain but eventually fainted. You and your crew were dispatched to the scene and saw that the patient is lying on the floor in his house, and no other persons appear to be nearby.” The scenario was declared by a supervisor, and the supervisor would not have further communication with the crew until the whole test course is finished.

Paramedics here in Taiwan resuscitate the OHCA patients in the prehospital setting following the recommendations of ILCOR (International Liaison Committee on Resuscitation), but the timing of applying a mechanical CPR device during resuscitation has not been uniformly regulated in the EMS protocol of New Taipei City. Hence, the time for deployment of M-CPR device was decided by every team themselves.

In order to get a non-shaded view during our video recording, all crews were under the surveillance of three cameras simultaneously recorded from three different angles. The scenario environment and camera setup location are shown in Appendix A in S1 Appendix.

The manikin used was Laerdal Megacode Kelly, the defibrillator used was Zoll X Series, and the M-CPR device used was Physio-Control LUCAS.

Blinding

Not applicable.

Primary and secondary outcomes

The primary outcome of this study was overall CCF. Secondary outcomes were CCF during manual CPR period, time to advanced life support interventions (e.g. administration of first-dose epinephrine and accomplishment of endotracheal intubation), and teamwork performance.

Teamwork performance was measured using a pre-designed, structuralised evaluation form and rated on four aspects: leadership dedication, awareness and communication during changes of patient heart rhythm, closed-loop communication during medication administration, and awareness of unnecessary hands-off during CPR. Based on the final scores of every team, we graded their teamwork performance in three levels: good, average, and poor (Appendix B in S1 Appendix).

Both primary and secondary outcomes were analysed by reviewing the video recordings of all scenarios captured by two independent reviewers. Interobserver correlation coefficient was calculated and reported.

Statistical analysis

We used Microsoft Excel 2007 to record the data, and IBM SPSS 22.0 was used to perform all statistical analyses. The descriptive statistics of the participants’ demographic data are presented as count, mean (standard deviation), and percentage. The Kruskal-Wallis method was used to examine significance for sample size limitation (i.e. only 7 outcome data in each crew-size group), not fulfilling the assumption of normality. We also used the Kaplan-Meier method to compare the time-to-accomplishment of ALS intervention (i.e. first-dose of epinephrine and success of endotracheal intubation). For interobserver correlation coefficient, we used two-way random model of intraclass correlation coefficient reliability. A two-tailed P value of <0.05 were considered significant. Post hoc tests were performed to do pairwise comparisons for the primary endpoint (CCF) and Kaplan-Meier curves, with the adjusted level of the p-value for multiple comparisons.

Results

Participant flow and recruitment

During the study period, 140 of 399 paramedics from New Taipei City were enrolled through a stratified sampling process. The enrollees were further randomly allocated to five groups (i.e. comprising 2, 3, 4, 5, and 6 paramedics). The participant flow of the study is shown in Fig 1.

Fig 1

Flow chart of the study process (following the CONSORT statement of randomized control trial).

Baseline data

The baseline demographic data of the participants are presented in Table 1, which showed that there was no difference between groups after the randomisation.

Table 1

Demographic characteristics of the participants.

Demographic characteristics
	2 people (n = 14)	3 people (n = 21)	4 people (n = 28)	5 people (n = 35)	6 people (n = 42)	P value
Age, year, mean (SD)	34.1 (3.2)	35.2 (3.3)	32.0 (3.9)	33.5 (3.7)	33.0 (4.1)	0.059
EMT seniority, year, mean (SD)	9.6 (2.3)	11.3 (3.6)	10.2 (3.3)	10.7 (3.9)	9.4 (2.8)	0.192
Paramedic seniority, year, mean (SD)	5.6 (2.5)	6.1 (3.2)	5.6 (2.4)	5.7 (3.1)	5.0 (2.5)	0.594
Last ACLS certification						0.485
>1 year, %(n)	50.0 (7)	52.4 (11)	53.6 (15)	42.9 (15)	40.5 (17)	0.787
≦1 year, %(n)	28.6 (4)	38.1 (8)	42.9 (12)	45.7 (16)	54.8 (23)	0.462
Don’t remember, %(n)	21.4 (3)	9.5 (2)	3.6 (1)	11.4 (4)	4.8 (2)	0.286
OHCA experience in last 3 years						0.159
>30 times, %(n)	7.1 (1)	14.3 (3)	28.6 (8)	25.7 (9)	33.3 (14)	0.247
≦30 times, %(n)	64.3 (9)	81.0 (17)	57.1 (16)	60.0 (21)	61.9 (26)	0.480
Don’t remember, %(n)	28.6 (4)	4.8 (1)	14.3 (4)	14.3 (5)	4.8 (2)	0.119
Experience as leader at OHCA scene in last year						0.995
>10 times, %(n)	7.1 (1)	9.5 (2)	7.1 (2)	5.7 (2)	9.5 (4)	0.974
≦10 times, %(n)	85.7 (12)	85.7 (18)	89.3 (25)	91.4 (32)	88.1 (37)	0.964
Don’t remember, %(n)	7.1 (1)	4.8 (1)	3.6 (1)	2.9 (1)	2.4 (1)	0.934
Experience of Intubation at OHCA scene in the previous year						0.964
>10 times, %(n)	7.1 (1)	4.8 (1)	3.6 (1)	2.9 (1)	4.8 (2)	0.886
≦10 times, %(n)	85.7 (12)	95.2 (20)	92.9 (26)	91.4 (32)	92.9 (39)	0.971
Don’t Remember, %(n)	7.1 (1)	0.0 (0)	3.6 (1)	5.7 (2)	2.4 (1)	0.748
Experience of IV insertion at any scene in the previous year						0.162
> 10 times, %(n)	14.3 (2)	23.8 (5)	10.7 (3)	11.4 (4)	11.9 (5)	0.678
≦10 times, %(n)	71.4 (10)	71.4 (15)	85.7 (24)	88.6 (31)	88.1 (37)	0.276
Don’t remember, %(n)	14.3 (2)	4.8 (1)	3.6 (1)	0.0 (0)	0.0 (0)	0.057
Experience of LUCAS operating at OHCA scene in last year						0.111
>10 times, %(n)	14.3 (2)	19.1 (4)	0.0 (0)	5.7 (2)	14.3 (6)	0.142
≦10 time, %(n)	71.4 (10)	76.2 (16)	96.4 (27)	82.9 (29)	81.0 (34)	0.224
Don’t remember, %(n)	7.1 (1)	0.0 (0)	0.0 (0)	0.0 (0)	0.0 (0)	0.060
Other MCPR Device, %(n)	7.1 (1)	4.8 (1)	3.6 (1)	11.4 (4)	4.8 (2)	0.711

EMT, emergency medical technician; ACLS, advanced cardiovascular life support; OHCA, out-of-hospital cardiac arrest; MCPR, mechanical cardiopulmonary resuscitation; IV, Intravenous; LUCAS, Lund University Cardiopulmonary Assist System; SD, standard deviation

Outcome and estimation

Of all 35 teams with 2, 3, 4, 5, and 6 paramedics, the overall CCF were 65.1%, 64.4%, 70.7%, 72.8%, and 71.5%, respectively (P = 0.148). The CCFs in manual CPR periods were significantly high in teams with 5 paramedics (58.4%, 61.8%, 68.9%, 72.4%, and 68.7%, P<0.05) (Table 2). The scatter plot of CCF data was shown in Appendix C and D in S1 Appendix. When examining the reason for the decrease in CCF (i.e. hands-off time during CPR), we found that the hands-off time was shortened as crew size increased. The time of chest compression and the causes of chest compression interruption in different teams were presented in Fig 2. The longest period of interruption was observed in teams with a crew size of 2 paramedics due to M-CPR device troubleshooting.

Table 2

Primary and secondary outcome results.

		2 people	3 people	4 people	5 people	6 people	P value	post hoc tests
		Primary outcome
CCF, %(SD)		65.1 (8.5)	64.5 (5.1)	70.7 (6.7)	72.8 (8.2)	71.5 (9.5)	0.148	2 = 3 = 4 = 5 = 6
Hand compression Duration CCF, %(SD)		58.4 (7.7)	61.8 (10.4)	68.91 (5.8)	72.35 (6.7)	68.73 (14.9)	0.043*	5>2 = 3 = 4 = 6
MCPR duration CCF, %(SD)		67.0 (19.6)	75.5 (19.7)	68.5 (14.5)	70.1 (13.6)	72.3 (8.2)	0.883	2 = 3 = 4 = 5 = 6
	Secondary outcome
Contact to recognition, sec (SD)	12.9 (3.1)		12.9 (4.9)	12.6 (4.00)	14.3 (3.2)	17.0 (5.7)	0.339	2 = 3 = 4 = 5 = 6
Time without CPR, sec (SD)	199.0 (59.3)		257.7 (43.6)	120.1 (35.1)	118.3 (29.9)	101.0 (34.1)	0.011*	4 = 5 = 6>2 = 3
Time with hand CPR, sec (SD)	205.0 (134.5)		184.9 (94.5)	180.0 (58.3)	190.0 (101.0)	187.1 (127.8)	0.987	2 = 3 = 4 = 5 = 6
Time with mechanical CPR, sec (SD)	165.9 (132.0)		102.4 (82.5)	109.4 (59.7)	153.9 (133.3)	88.0 (79.0)	0.641	2 = 3 = 4 = 5 = 6
Time to finish, sec (SD)	569.9 (101.2)		445.0 (100.1)	409.6 (71.0)	461.6 (123.5)	376.1 (87.2)	0.031*	3 = 4 = 5 = 6>2
Mean hand CPR depth, cm (SD)	3.8 (0.6)		3.9 (0.6)	4.0 (0.3)	4.2 (0.6)	3.9 (0.6)	0.688	2 = 3 = 4 = 5 = 6
Mean hand CPR compression rate, cpm (SD)	108.6 (9.1)		110.4 (10.6)	104.1 (5.5)	105.4 (4.1)	111.4 (7.2)	0.437	2 = 3 = 4 = 5 = 6
Initialize M-CPR assembly, sec (SD)	176.0 (156.8)		235.3 (146.4)	176.3 (81.4)	204.7 (122.4)	199.0 (144.1)	0.938	2 = 3 = 4 = 5 = 6
CPR interruption due to defibrillation, sec (SD)	32.0 (23.5)		30.4 (22.6)	16.4 (8.6)	11.9 (7.9)	17.6 (10.1)	0.200	2 = 3 = 4 = 5 = 6
CPR interruption due to ventilation, sec (SD)	43.5 (34.9)		41.3 (30.5)	21.7 (8.5)	22.7 (6.8)	18.1 (8.9)	0.433	2 = 3 = 4 = 5 = 6
CPR interruption due to placement of the M-CPR board, sec (SD)	7.3 (3.9)		5.0 (1.3)	3.9 (1.0)	5.8 (3.0)	4.4 (1.9)	0.294	2 = 3 = 4 = 5 = 6
CPR interruption due toMCPR assembly, sec (SD)	20.1 (8.4)		17.4 (12.9)	14.4 (8.8)	12.4 (6.2)	12.0 (9.3)	0.424	2 = 3 = 4 = 5 = 6
CPR interruption due to MCPR troubleshooting, sec (SD)	62.7 (64.2)		35.6 (40.2)	32.9 (33.1)	33.1 (24.1)	29.0 (40.2)	0.823	2 = 3 = 4 = 5 = 6
CPR interruption for other reasons, sec(SD)	35.5 (21.0)		27.9 (14.1)	31.4 (15.4)	32.4 (17.6)	27.0 (12.7)	0.922	2 = 3 = 4 = 5 = 6
Initiation of intubation, sec (SD)	208.7 (62.5)		158.1 (86.2)	157.7 (74.3)	162.6 (49.5)	89.0 (71.3)	0.103	6>2 = 3 = 4 = 5
Completion of intubation, sec (SD)	277.6 (90.2)		212.0 (84.6)	198.0 (77.3)	196.9 (55.5)	130.1 (68.0)	0.036*	3 = 4 = 5 = 6>2
Administration of the first dose of epinephrine, sec (SD)	316.3 (94.9)		252.4 (92.5)	179.9 (59.2)	176.7 (50.5)	147.3 (40.2)	0.004*	4 = 5 = 6>2 = 3
Teamwork performance							0.010*	5 = 6>2 = 3 = 4
Good, n(%)	0 (0)		0 (0)	1 (10.0)	5 (50.0)	4 (40.0)	0.004*
Medium, n(%)	5 (26.3)		4 (21.1)	6 (31.6)	2 (10.5)	2 (10.5)	0.118
Poor, n(%)	2 (33.3)		3 (50.0)	0 (0)	0 (0)	1 (16.7)	0.145

MCPR, mechanical cardiopulmonary resuscitation; CCF, chest compression fraction; CPR, cardiopulmonary resuscitation; SD, standard deviation

Fig 2

Composition of no compression time.

* MCPR, mechanical cardiopulmonary resuscitation.

There was a significant difference in the time to first dose of epinephrine (316.29 s, 252.43 s, 179.91 s, 176.71 s, and 147.29 s; P = 0.004) and time to finish intubation (277.6 s, 212.0 s, 198.0 s, 196.9 s, and 130.1 s; P = 0.036) among the different crew sizes. The Kaplan-Meier test showed that the time to the first dose of epinephrine was significantly shortened in teams with a crew size over 4 paramedics (Fig 3A), while time to completion of intubation was shortest in teams with a crew size of 6 paramedics (Fig 3B). Teamwork performance rating was positively associated with increasing CCF (P = 0.01 for trend), and larger crew size was found to have better team dynamics (Fig 4). A crew size of 5 paramedics had the best teamwork performance as shown in Fig 4 (visual difference). The interobserver correlation coefficient reliability was 0.99 for the two video reviewers.

Fig 3

A. Accumulative successful rate of ALS interventions (intravenous epinephrine). B. Accumulative successful rate of ALS interventions (endotracheal intubation).

Fig 4

Association between teamwork performance and chest compression fraction.

Larger crew size tends to have higher CCF and better teamwork performance rating. CCF, chest compression fraction.

Limitations

There are some limitations in this study. First, because the examined paramedics were randomly distributed to different teams, each resuscitation team was composed of unfamiliar partners and EMTs from different levels, which was somewhat different from their daily clinical practice. Some measurement variables like CCF or teamwork performance may be suitable in that situation. Second, due to the lack of resources, CCF was measured by reviewing the video footage of two reviewers instead of using a computerised program. However, we checked the interobserver consistency to guarantee the validity of the evaluation. By reviewing the video footage, the researchers were able to determine the CCF from the time the paramedic approached the patient; by contrast, if a computerised program was used, it can only be operated once the machine has been completely set up. Third, the resuscitation in this simulation study may not perfectly match the practice in the true prehospital setting, which might have some uncertainties that may interfere the crew such as the confined space or opinions from the families. Hence, further clinical research is needed to verify the findings in this study. Finally, this study examined the optimal crew size based on the CCF and prehospital ALS (i.e. endotracheal intubation and intravenous epinephrine). The results may not be applicable to those EMS systems without the recommendation of prehospital ALS in the field.

Discussion

In this randomised controlled trial, we have three major findings. First, a larger paramedic crew size (≧4 paramedics) does not significantly increase the overall CCF in OHCA resuscitation, but has higher CCF in manual CPR period before the setup of the CPR machine. Second, a crew size of over 4 paramedics can also shorten the time of ALS interventions, but complex interventions (i.e. endotracheal intubation, compared with intravenous drug administration) require more paramedics in order to accomplish the interventions on time. Third, a crew size of 5 will have the best teamwork performance, and the teams with a smaller crew size should focus more on the quality of manual CPR, on improving teamwork, and on training how to troubleshoot a mechanical CPR device.

Our study had some strengths compared with previous studies evaluating the number of paramedics during resuscitations. Martin-Gill et al. recruited 40 paramedics and divided them into teams with crew sizes of 2, 3, and 4 paramedics. They concluded that crew sizes had no significant impact on the “no flow fraction” during CPR.[10] However, the results of the current study were different from the findings of the previous study and informative in two aspects. First, we used the widely adopted M-CPR devices, which had not been tested in a previous study, in our scenario. Second, we divided the participants into teams with paramedic crew sizes of 2, 3, 4, 5, and 6, in which the larger crew size effect could be evaluated. Furthermore, our study provided complementary knowledge regarding the crew size issue. Sun et al. reported that a total crew size of 4 paramedics and a paramedic ratio of >50% had the best odds of survival among OHCA patients.[11] The reasons for the survival benefit in their retrospective study was supposed to be related to the provision of early ALS interventions, since literatures from Taiwan EMS suggested that epinephrine can improve survival to discharge rate for OHCA patients and prehospital endotracheal intubation can increase the odds of survival to discharge.[6, 7] Our study demonstrated that larger crew sizes contributed to the early accomplishment of ALS interventions. A crew size of over 4 paramedics can significantly shorten the time of intravenous epinephrine interventions, while a complex intervention such as endotracheal intubation might require more paramedics (i.e. a 6-paramedic team) to accomplish the task within a short time.

The association between crew sizes and the CPR performance was deeply explored in this study. With regard to the reducible hands-off time during CPR, we found that hands-off time during CPR had dropped sharply as the crew size increased (trend P = 0.011). The most time-consuming event was the troubleshootingof the M-CPR device. Although without significance, we found that the interruption time of two people was almost twice as long as that of three people. By video review, we found that all crew members immediately noticed that the alarm on the M-CPR device malfunctioned, which increased their level of awareness regarding the situation; after the M-CPR was implanted, each crew member was simultaneously executing further interventions including endotracheal tube intubation and medication administration. Most of the time, the M-CPR device malfunctioned while all members were preoccupied by other procedures. Hence, although all members were aware that the device malfunctioned, without good teamwork and coordination, they will not be able to handle the incident immediately. Therefore, we suggest that teams with a smaller crew size should focus more on the quality of manual CPR, on improving teamwork, and on training how to troubleshoot a mechanical CPR device.

In this study, we found that a crew size of 5 paramedics had the best performance in resuscitation as all of them have a clearly defined task in the OHCA setting: one leader, one assigned in managing the airway, two as interchangeable chest compressor and defibrillator operator, and one assigned in establishing an intravenous access and administering medications. The team performance rating of a crew size of 6 paramedics was not as good as that of a crew size of 5 paramedics, and this might be due to the overcrowding in the confined space of the OHCA setting as well as due to ineffective communication caused by unclear task allocation as observed in the video review. Indeed, it has been mentioned in some teamwork trainer books that an ideal crew size should not be more than 6.[16, 17] However, we also observed that complex interventions like endotracheal intubation may require more paramedics in order to accomplish the task on time; however, given the said circumstance, only two paramedics are usually needed for airway management. Therefore, the number of paramedics who should be dispatched to the scene of a cardiac arrest should depend on who have been authorised by the local EMS to perform the interventions.

In summary, larger paramedic crew size (≧4 paramedics) does not significantly increase the overall CCF in OHCA resuscitation but have higher CCF in manual CPR period before the setup of the CPR machine. A paramedic crew size of over 4 can also shorten the time of ALS interventions, while a paramedic crew size of 5 will have the best teamwork performance. Paramedic teams with a smaller crew size should focus more on the quality of manual CPR, on improving teamwork, and training how to troubleshoot a mechanical CPR device.

(DOCX)

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20 May 2020

PONE-D-20-07990

Optimal Paramedic Numbers in Resuscitation of Patients with Out-of-hospital Cardiac Arrest: A Randomized Controlled Study in a Simulation Setting

PLOS ONE

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Reviewers' comments:

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Comments to the Author

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: No

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #2: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Thank you for giving me the opportunity to review your article. I could read this article with interest, however I felt some concern about your study.

The author mention the more crow reduce the time of intubation, and injection. However, there are many reports which do not recommend intubation and recommend compression only CPR. Therefore, this report has no worth for the investigators who believe such reports.

What kind of guideline did this study follow? RCA?

In this study, the author recommend the best cure size is 5. However, transportation is also the important work for the emergency medical service. In practice, can 5 crows work in an ambulance?

Reviewer #2: This study investigated the effect of paramedic crew size in the resuscitation of patients with out-of-hospital cardiac arrest (OHCA) remains inconclusive. The hypothesis was teams with a larger crew size have better resuscitation performance including chest compression fraction (CCF), advanced life support (ALS), and teamwork performance than those with a smaller crew size. The study was well designed with strong point as a randomized one.

Below are some comments:

1. The primary endpoint, chest compression fraction (CCF) is essentially continuous data with interval scale at the very least. And this has been used to power the study. However, the test statistics for the primary endpoint is based on non-parametric method, Kruskal-Wallis instead of 1-way ANOVA. Is there any particular reasons for using this non parametric approach? As it is commonly known, the use of non-parametric method may reduce the power to detect the intended meaningful difference stated in the hypothesis

2. There is not any plot showing the distribution of the primary endpoint chest compression fraction (CCF) among the five groups. This is an important plot that will enable us to check a few things for example the data distribution and hence whether assumption on normality is fulfilled.

3. In the statistical analysis section, it is mentioned about ‘For all non-parametric continuous data, … ‘. It is not defined what it means by non—parametric continuous data. What is the criteria for non-parametric?

4. For the Kaplan-Meier method used to compare the time-to-accomplishment of ALS intervention (i.e. first-dose of epinephrine and success of endotracheal intubation), there are 5 groups to compare yet only a single p-value is reported there. What exactly this single p-value represent? Have you done any further analyses to do pairwise comparisons of the KM curves? Have any adjustment of the p-values made?

5. In the same question as in (4) is also for time to finish intubation

6. For the primary endpoint CCF, how is the result if using ANOVA with the obtained/observed CCF instead of the ranks? With this approach, is it possible to do further analyses of pairwise comparisons between groups?

**********

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Reviewer #1: Yes: Shuichi Hagiwara

Reviewer #2: No

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10 Jun 2020

Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1:

Thank you for giving me the opportunity to review your article. I could read this article with interest, however I felt some concern about your study.

The author mention the more crow reduce the time of intubation, and injection. However, there are many reports which do not recommend intubation and recommend compression only CPR. Therefore, this report has no worth for the investigators who believe such reports.

Response from the authors:

Thank you for your comments.

The effect of prehospital advanced life support (ALS), including endotracheal intubation and intravenous epinephrine on the outcome of patients with out-of-hospital cardiac arrest (OHCA), remained inconclusive in literature. However, there are many studies here in Taiwan showing the positive association between ALS and OHCA patient outcomes (reference 1, 2, 3 beneath), and that’s why we have to analyze the effect of crew size on these interventions. However, we do agree with your concern and will add it in the revised manuscript.

Reference1, 2, 3

1. The Effect of Successful Intubation on Patient Outcomes after Out-of-Hospital Cardiac Arrest in Taipei. Chiang WC, Hsieh MJ, Chu HL, et al. Annals Emergency Medicine. 2018; 71(3):387-396.

2. The effect of the number and level of emergency medical technicians on patient outcomes following out of hospital cardiac arrest in Taipei. Sun JT, Chiang WC, Hsieh MJ, et al. Resuscitation. 2018;122:48-53

3. Prehospital intravenous epinephrine may boost survival of patients with traumatic cardiac arrest: a retrospective cohort study. Chiang WC, Chen SY, Ko PCI et al. Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine 2015; 23:102-108.

Changes made (Please refer to the marked version):

Page 14, Line 8:

Finally, this study examined the optimal crew size based on the CCF and prehospital ALS (i.e. endotracheal intubation and intravenous epinephrine). The results may not be applicable to those EMS systems without the recommendation of prehospital ALS in the field.

What kind of guideline did this study follow? RCA?

Response from the authors:

Thank you.

Paramedics here in Taiwan resuscitate the OHCA patients in the prehospital setting following the recommendations of ILCOR (International Liaison Committee on Resuscitation), but the timing of applying a mechanical CPR device during resuscitation has not been uniformly regulated in the EMS protocol of New Taipei City.

We have clarified this in the revised manuscript.

Changes made (Please refer to the marked version):

Page 9, Paragraph 2:

Paramedics here in Taiwan resuscitate the OHCA patients in the prehospital setting following the recommendations of ILCOR (International Liaison Committee on Resuscitation), but the timing of applying a mechanical CPR device during resuscitation has not been uniformly regulated in the EMS protocol of New Taipei City. Hence, the time for deployment of M-CPR device was decided by every team themselves.

In this study, the author recommend the best cure size is 5. However, transportation is also the important work for the emergency medical service. In practice, can 5 crows work in an ambulance?

Response from the authors:

The goal of this investigation aimed to help determine the best crew size for resuscitation of an OHCA patient in the field before ambulance transportation, thus we designed a simulation-based randomized controlled trial with cardiac arrest scenario equipped with widely adopted mechanical CPR to test different crew sizes. Based on the local EMS protocol, we will dispatch the 2nd crew to the scene in case of encountering an OHCA patient, but the transportation is still with only 2 paramedics in the ambulance patient compartment, not 5.

Thank you for your comments. We have clarified this in the revised manuscript.

Changes made (Please refer to the marked version):

Page 6, Line 17-18:

To help determine the best crew size for resuscitation of an OHCA patient in the field before ambulance transportation, we designed a simulation-based randomized controlled trial…

Reviewer #2:

This study investigated the effect of paramedic crew size in the resuscitation of patients with out-of-hospital cardiac arrest (OHCA) remains inconclusive. The hypothesis was teams with a larger crew size have better resuscitation performance including chest compression fraction (CCF), advanced life support (ALS), and teamwork performance than those with a smaller crew size. The study was well designed with strong point as a randomized one.

Below are some comments:

1. The primary endpoint, chest compression fraction (CCF) is essentially continuous data with interval scale at the very least. And this has been used to power the study. However, the test statistics for the primary endpoint is based on non-parametric method, Kruskal-Wallis instead of 1-way ANOVA. Is there any particular reasons for using this non parametric approach? As it is commonly known, the use of non-parametric method may reduce the power to detect the intended meaningful difference stated in the hypothesis

Response from the authors:

Thank you for your comments. We used the Kruskal-Wallis method for our primary end point because our sample size is relatively small, only 7 outcome data in each crew-size group.

We have clarified this in the revised manuscript.

Changes made (Please refer to the marked version):

Page 11, line 12-14:

… the Kruskal-Wallis method was used to examine significance because of sample size limitation (i.e. only 7 outcome data in each crew-size group).

2. There is not any plot showing the distribution of the primary endpoint chest compression fraction (CCF) among the five groups. This is an important plot that will enable us to check a few things for example the data distribution and hence whether assumption on normality is fulfilled.

Response from the authors:

Thank you for your comments to make our manuscript more precise.

It is important to plot the distribution of the primary endpoint.

We’ve added scatter plots for primary and secondary outcomes in the revised manuscript.

Changes made (Please refer to the marked version):

Page 12, Line 16: The scatter plot of CCF data was shown in Appendix C and D.

Appendix Figure C: The scatter plot of primary endpoint (overall chest compression fraction, CCF)

Appendix Figure D: The scatter plot of chest compression fraction (CCF) during manual period (hand compression) and mechanical period (machine compression).

3. In the statistical analysis section, it is mentioned about ‘For all non-parametric continuous data, … ‘. It is not defined what it means by non—parametric continuous data. What is the criteria for non-parametric?

Response from the authors:

Thank you for reminding us of the misused statistical term.

We have made a correction.

The criteria for non-parametric in this study was its sample size limitation. Because there was only 7 outcome data in each crew-size group, the assumption on normality is not fulfilled.

Changes made (Please refer to the marked version):

Page11, line11:

(misused term deleted)...The Kruskal-Wallis method was used to examine significance for sample size limitation (i.e. only 7 outcome data in each crew-size group), not fulfilling the assumption of normality.

4. For the Kaplan-Meier method used to compare the time-to-accomplishment of ALS intervention (i.e. first-dose of epinephrine and success of endotracheal intubation), there are 5 groups to compare yet only a single p-value is reported there. What exactly this single p-value represent? Have you done any further analyses to do pairwise comparisons of the KM curves? Have any adjustment of the p-values made?

Response from the authors:

Thank you for your comments.

The single p-value reported here is the overall p-value. Statistically significant results indicate that not all of the group means are equal.

Per your suggestions, we have consulted the staff of National Taiwan University Hospital-Statistical Consulting Unit (NTUH-SCU), and performed post hoc tests to do pairwise comparisons with the adjusted level of the p-value of the KM curves. The results were reported on the revised figure 3A&3B.

Thank you for making our manuscript more informative to the readers.

Changes made (Please refer to the marked version):

Page 12, line 1:

Post hoc tests were performed to do pairwise comparisons for the primary endpoint (CCF) and Kaplan-Meier curves, with the adjusted level of the p-value for multiple comparisons.

Figure. 3A & 3B (figure tag: results of overall p-value and post hoc test)

5. In the same question as in (4) is also for time to finish intubation

Response from the authors:

Thank you for the suggestions.

Changes made (Please refer to the marked version):

Page 12, line 1:

Post hoc tests were performed to do pairwise comparisons for the primary endpoint (CCF) and Kaplan-Meier curves, with the adjusted level of the p-value for multiple comparisons.

Figure. 3A & 3B (figure tag: results of overall p-value and post hoc test)

6. For the primary endpoint CCF, how is the result if using ANOVA with the obtained/observed CCF instead of the ranks? With this approach, is it possible to do further analyses of pairwise comparisons between groups?

Response from the authors:

Thank you for your comments. We have made changes accordingly.

Per your suggestions, we performed post hoc tests with the CCF.

The results were reported on the revised Table 2.

Thank you for making our manuscript more informative to the readers.

Changes made (Please refer to the marked version):

Revised Table. 2 (with a new column “post hoc tests”)

Submitted filename: Responses to the reviewers.docx

Click here for additional data file.

15 Jun 2020

Optimal Paramedic Numbers in Resuscitation of Patients with Out-of-hospital Cardiac Arrest: A Randomized Controlled Study in a Simulation Setting

PONE-D-20-07990R1

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18 Jun 2020

PONE-D-20-07990R1

Optimal Paramedic Numbers in Resuscitation of Patients with Out-of-hospital Cardiac Arrest: A Randomized Controlled Study in a Simulation Setting

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