a b s t r a c t

Purpose: Feedback devices are used to improve chest compression quality related to survival rates in cardiac arrest. However, several studies have shown that Feedback devices are not sufficiently reliable to ensure adequate CC depth on soft surfaces. Here, we determined the proper target depth of feedback (TDF) using an accelerometer during cardiopulmonary resuscitation in hospital beds.

Methods: In prospective randomized crossover study, 19 emergency physicians performed CCs for 2 minutes continuously on a manikin in 2 different beds with 3 TDFs (5, 6, and 7 cm). We measured CC depth, the proportion of accurate compression depths, CC rate, the proportion of incomplete chest decompressions, the velocity of CC (CC velocity), the proportion of time spent in CC relative to compression plus decompression (Duty cycle), and the time spent in CC (CC time).

Results: Mean (SD) CC depths at TDF 5, 6, and 7 were 45.42 (5.79), 52.68 (4.18), and 58.47 (2.48) on one bed and

46.26 (4.49), 53.58 (3.15), and 58.74 (2.10) mm on the other bed (all P b .001), respectively. The proportions of accurate compression depths and CC velocity at TDF 5, 6, and 7 differed significantly according to TDF on both beds (all P b .001).The CC rate, CC time, and proportion of incomplete chest decompression did not differ on both beds (all P N .05). The duty cycle differed significantly on only B2.

Conclusions: The target depth of the Real-time feedback device should be at least 6 cm but should not exceed 7 cm for optimal CC on patients on hospital beds.

(C) 2015

1. Introduction

chest compression is a major factor of high-quality cardiopul- monary resuscitation (CPR) [1,2]. However, several studies have shown that providers often fail to perform CC according to current guidelines, despite being skilled physicians [3-5]. Use of feedback devices could improve the rate and depth of CC quality [6,7]. chest compression rate and depth are usually estimated by double integration of the acceleration or by mapping the pressure according to a built-in sensor in the feedback device during CCs [6-8]. By using both a feedback device and the guidance of a metronome, providers can perform CC at a correct and

* Corresponding author at: Department of Emergency Medicine, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul, South Korea. Tel.: +82 2 2290 9291; fax: +82

2 2290 9280.

E-mail address: [email protected] (H. Kang).

¹ Contributed equally to this study.

consistent rate following CPR guidelines [6,7]. However, they might not compress the chest to the correct depth according to the guidelines be- cause some devices overestimate or underestimate the depth, depending on the stiffness and the angle of incline of the surface on which the patient is placed [9].

The main cause of the overestimation of feedback depth is compres- sion of the mattress on which the patient is placed during CPR in hospi- tal [10-12]. The depth of mattress compression averages approximately

1.5 cm [13]. An increase in CC depth is associated with shock success, and sufficient CC depth can achieve return of spontaneous circulation [1,14]. Recently, various methods to compensate for or reduce mattress compression have been shown to be helpful in addressing this problem of CPR in the hospital [15-17].

Considering a mattress compression depth of approximately 1.5 cm [13], performing CCs with a feedback device at a depth of approximately 6 or 7 cm may effectively result in a chest depth of at least 5 cm during In-hospital CPR. To our knowledge, no study about the ideal target depth of feedback (TDF) to reach a CC depth of at least 5 cm during CPR in a hospital setting has been published. We hypothesized that a

http://dx.doi.org/10.1016/j.ajem.2015.07.010

0735-6757/(C) 2015

TDF of 6 or 7 cm could help to attain a sufficient depth of at least 5 cm during CPR with use of a feedback device in a hospital setting. In this study, we tried to confirm the proper TDF of CC during CPR with a feed- back device using an accelerometer in a hospital setting, with a manikin on the mattress.

1. Methods
   1. Study design

We conducted a randomized crossover manikin study to determine the proper TDF for CCs performed during simulated in-hospital cardiac arrest at Hanyang University’s Simulation Center (Seoul, Korea) in March 2014. The institutional review board at Hanyang University Hospital approved this study in January 2014 (2013-12-010). We registered the study protocol in Clinical Trials before study initiation (ClinicalTrials.gov: NCT02073539).

• 1. Equipment and materials

In this study, we used a new concept of real-time CC depth estima- tion algorithm (called ubiquitous CPR, or U-CPR) using an Android- based smartphone with a built-in, 3-axis accelerometer. The U-CPR is a Smartphone application which was developed by Song et al [8]. A VEGA Racer (model IM-A760S; Pantech Co, Ltd, Seoul, Korea) smartphone with Android OS (Jellybean) was used to implement the application. A custom-made pocket was used to fix the smartphone on the back of the provider’s hand (Fig. 1). A Resusci Anne Modular System Skill Reporter manikin (9.89 kg; Laerdal Medical, Orpington, UK) was used in all evaluations when performing CPR. We determined the refer- ence CC depth (in millimeters) from the manikin’s recording system with a laptop. We also collected the result of real-time feedback from the smartphone and downloaded the data to a laptop. Two different types of hospital beds were used in this study: B1, Stryker ST104-747 (Transport stretcher, 760 x 2110 mm, 228 kg; Stryker Co, Kalamazoo, MI) with a mattress (660 mm x 1920 mm x 80 mm, soft foam with vinyl coverage; Stryker Co), and B2, Shinchang SB-34p, 850 x 2080 mm (Shinchang Co, Busan, Republic of Korea) with a mattress (830 mm x 1930 mm x 80 mm, soft foam with vinyl coverage, Shinchang Co). We added weight to the manikin to approximately 40 kg, thus simulating the upper body weight of an adult human when the manikin was placed in a bed. A backboard (450 x 600 x 10 mm, 3-kg Lifeline Plastic; Sung Shim Medical Co, Bucheon, Korea) was placed between the manikin and a mattress.

Fig. 1. Chest compression was performed under U-CPR guidance. U-CPR is an application of a real-time CC depth estimation algorithm using an android-based smartphone with a built-in 3-axis accelerometer.

• 1. Participants

The estimated sample size was 19 subjects accounting for a 20% dropout rate. An analysis with G-power 3.1.2 (Heine Heinrich University, Dusseldorf, German) with an ? error of .05 and power of

0.8 was performed to detect a difference of 5-mm CC depth between 2 groups in a previously performed pilot study of eight participants who have not been included in this study. The mean (SD) CC depths of the 2 groups (target CC depths of 2 groups: 5-6 and 6-7 cm) were 46.1 (6.1) and 55.5 (7.6) mm on the beds. We recruited physicians working at one Tertiary medical center in March 2014. We included healthy volunteers who were between 16 and 60 years old and certified as Basic Life Support providers by the American Heart Association (AHA) [18]. We excluded people who had wrist and low back disease. All par- ticipants signed a written consent form and completed a brief question- naire consisting of demographic information (age, sex, body weight, and height) before being included.

• 1. Interventions

Ten minutes prior to starting the trials, participants were allowed to practice CC with real-time feedback to become familiar with U-CPR. Nineteen participants were enrolled, and they were randomly allocated to 3 groups using a random number generator (www.random.org). Par- ticipants in group A (n = 7) performed the first CC with TDF 5 cm on B1, whereas participants in group B (n = 7) performed the first CC with TDF 6 cm on B1. Group C (n = 5) performed the first CC with TDF 7 cm on B1. After being allocated to the 3 groups, the participants were placed in a random order by a computer-generated list of random numbers (www.random.org) to minimize learning effects, and then they per- formed CC with real-time feedback by U-CPR for 2 minutes continuous- ly. The height of the bed was approximately the height of the participant’s upper patella level. A stool was used if needed. The location of CC was marked on the chest of the manikin by crossed plasters. Participants had a 10-minute break after each session of CCs in one bed and a 30-minute break before changing to another bed (Fig. 2).

• 1. Primary outcomes

We measured (i) CC depth, (ii) the proportion of CCs with accurate compression depth (ACD; >= 5 cm according to 2010 AHA guidelines) [19], (iii) the number of compressions per minute (CC rate), (iv) the proportion of CCs with incomplete chest decompression (ICD; >= 1 cm of residual leaning), (v) the velocity of CC (CC velocity) (mm/s),

(vi) the proportion of time spent time in CC relative to the start of 1 cycle of compression and the start of the next (duty cycle; %), and

(vii) the time spent in CC (CC time) (seconds).

• 1. Statistical analysis

The data were compiled using a standard spreadsheet application (Excel; Microsoft, Redmond, WA) and were analyzed using the Statisti- cal Package for the Social Sciences (SPSS) 18.0 KO for Windows (SPSS Inc, Chicago, IL). We generated the descriptive statistics and presented them as frequencies and percentages for the categorical data. Mean and SD values were used to report the data with a normal distribution, and a median with interquartile range (IQR) was used for the nonpara- metric variables for the continuous data. The repeated-measure analysis of variance (repeated-measures analysis of variance) test was used for continuous variables with normal distribution. Mauchly test of sphericity was performed. A post hoc analysis was done with a paired t test. The Friedman test was used for nonparametric variables for the continuous data. A post hoc analysis was conducted with the Wilcoxon signed-rank test using a Bonferroni correction. A P value less than .05 was considered significant.

Fig. 2. A flowchart explaining patient enrolment. Device: B1, Stryker ST104-747, and B2, Shin-chang SB-34p.

1. Results
   1. General characteristics

Nineteen participants were enrolled. Nineteen cycles of CCs were performed for each TDF for each bed. There was no exclusion in our study. All participants were male. The general characteristics of the participants are shown in Table 1.

• 1. Comparison of CC parameters on B1

The mean (SD) CC depths at TDF 5, 6, and 7 cm were 45.42 (5.79),

52.69 (4.18), and 58.47 (2.48) mm (P b .001), respectively. The median

Table 1

Demographic characteristics

Data

Sex (%)	Male (100)
Age (y)	31 (28-35)
Height (cm)	172 (171-175)
Weight (kg)	77 (72-80)
BMI (kg/m2)	25.9 (23.1-27.0)

Categorical variables are given as numbers (percentage). Continuous variables are given as median (IQR).

proportions of ACD (IQR) at TDF 5, 6, and 7 cm were 12.73% (4.67%-

77.38%), 86.45% (62.05%-99.06%), and 100.00% (99.51%-100.00%; P b

.001), respectively. The mean (SD) CC velocities at TDF 5, 6, and 7 cm were 45.42 (5.79), 52.69 (4.18), and 58.47 (2.48) mm/s (P b .001).

There were no significant differences in CC rate, CC time, duty cycle, and the proportion of ICD according to TDF (all, P N .05; Table 2).

• 1. Comparison of CC parameters on B2

The mean (SD) CC depths at TDF 5, 6, and 7 cm were 46.26 (4.49),

53.58 (3.15), and 58.74 (2.10) mm (P b .001), respectively. The median proportions of ACD (IQR) at TDF 5, 6, and 7 cm were 7.01% (2.83%-

37.56%), 96.68% (81.98%-99.53%), and 100.00% (99.01%-100.00%; P b

.001), respectively. The mean (SD) CC velocities at TDF 5, 6, and 7 cm were 165.59 (20.96), 198.41 (16.55), and 226.42 (19.41) mm/s (P b

.001). The mean (SD) duty cycles at TDF 5, 6, and 7 cm were 47% (2%),

46% (2%), and 44% (3%; P = .04). There were no significant differences in CC rate, CC time, and proportion of ICD according to TDF (all, P N

.05) (Table 3).

1. Discussion

This study demonstrates that mean CC depths at TDF of 6 and 7 cm resulted in CCs greater than 5 cm, and the proportions of ACD at TDF

Table 2

Chest compression with real-time feedback on B1 (n = 19)

	B1
	TDF 5 (n = 19)	TDF 6 (n = 19)	TDF 7 (n = 19)	P	TDF 5 vs TDF 6	TDF 5 vs TDF 7	TDF 6 vs TDF 7
CC depth (mm)	45.42 (5.79)	52.68 (4.18)	58.47 (2.48)	b.001	b.001	b.001	b.001
CC rate (rate/min)	101.74 (4.12)	100.92 (3.20)	100.32 (3.99)	.36
CC velocity (mm/s)	172.01 (30.59)	197.67 (23.89)	214.62 (14.40)	b.001	b.001	.016	b.001
Duty cycle (%)	45 (3)	45 (3)	45 (2)	.18
CC time (s)	0.27 (0.25-0.28)	0.27 (0.25-0.29)	0.27 (0.27-0.28)	.094
% of ACD (%)	12.73. (4.67-77.38)	86.45 (62.05-99.06)	100.00 (99.51-100.00)	b.001	.001	.002	b.001
% of ICD (%)	0.00 (0.00-12.61)	0.00 (0.00-0.46)	0.00 (0.00-0.00)	.07

Data with a normal distribution are given as mean (SD). The nonparametric variables are given as median (IQR). P b .05 is significant.

of 6 and 7 cm were acceptable when CC was performed using a feedback device with an accelerometer on the beds. High-quality CC with accu- rate depth is strongly associated with Short-term outcomes and better survival to hospital discharge [19]. In CPR on beds in the hospital, CC depth has been reported to be insufficient, although CC is performed by trained physicians, due to the compression of the underlying mat- tress and bed structures [4,5]. Using an accelerometer to target the Depth of CC to 6 or 7 cm could increase CC depth when CC is done on the bed in hospital settings. However, there are important findings to be considered when CC is done on beds. There have been several com- plications of CC reported, such as rib fracture, sternal fracture, hemotho- rax, and pneumothorax [20,21]. Chest compression of more than 6 cm has an increased risk of complications from deeper compressions [22]. Accordingly, the European Resuscitation Council guidelines (2010) recommended that CC depth should be at least 5 cm (for an adult) but not exceeding 6 cm [23]. Therefore, it is reasonable to aim for TDF of at least 6 cm and not to exceed 7 cm when CC is done on a bed with an accelerometer feedback device.

The current guidelines recommended a CC to ventilations ratio of 30:2 in adult cardiac arrest [18,23]. In some studies, increasing the ratio of CC to ventilations may increase the chance of return of sponta- neous circulation [24]. The current guidelines recommend that a CPR provider push harder and faster [18,23]. However, this could cause rescue fatigue, and a CC rate that is too fast could influence the quality of other CC parameters, such as CC depth and chest decompression [25]. In this trial, CC was performed guided by metronome at 100 com- pressions/min according to the current guidelines. The CC rate remained about 100 compressions/min, and there was no significant difference in CC rate by TDF value (all, P N .05). The feedback devices with an accelerometer guide the CC rate by a metronome. Cardiopulmonary resuscitation provider could perform CC according to the guided CC rate without influence of the CC depth and TDF.

Complete chest wall decompression improves hemodynamics dur-

ing CC by drawing venous blood to the heart, providing cardiac preload before the next CC [26]. Thus, current guidelines emphasize complete chest wall decompression during CPR [18,23]. This feedback device that uses an accelerometer estimates the CC depth in real time through double integration of the acceleration signal [8]. However, the feedback device could not estimate the movement of CC recoil. The proportion of ACD could be influenced by other CC parameters such as CC rate [27,28].

If the rate is too fast or the depth too deep during CC, it could deteriorate the quality of CC decompression [27,28]. Although these feedback de- vices could not guide CC decompression directly, other CC parameters that are guided by these feedback devices could influence the CC de- compression indirectly.

The CPR duty cycle is the proportion of time spent in CC relative to the start of 1 cycle of compression and the start of the next [18]. Current guidelines recommend a 50:50 duty cycle, and this recommendation is based on modest evidence derived largely from experimental and ani- mal studies [18]. There were conflicting and inconsistent results in de- termining proper duty cycle in human CPR and its relationship to other CC parameters. High-impulse CC with a shorter CC time constitut- ed the characteristic CC in an EMS system with a very high survival rate [29]. According to clinical study about CPR duty cycle in out-of-hospital cardiac arrest, relatively shorter CC time was associated with greater CCD and slower CC rate [30]. In this study, relatively deeper CCD was as- sociated with faster CC velocity, but not associated with CC time. Kovacs et al [31] reported that CC decompression velocity was independently associated with improved survival and favorable Neurologic outcome at hospital discharge after adult out-of-hospital cardiac arrest. The study about the association of CC velocity and other CC parameters and clinical outcomes would be required. There were no significant dif- ferences in proportions of ICD according to TDFs in this study. Additional study or new devices that could estimate and improve the quality of CC decompression would be needed to understand the issue.

1. Limitation

There were several limitations to this study. First, despite having added weight to the manikin in this study, the manikin we used none- theless differs from actual human patients in weight, height, and back rigidity. Therefore, the measured CC results in this study could differ from that of actual CC and may not predict the effects for return of circu- lation or neurologic outcomes in varied cases. Second, emergency phy- sicians who were certified as Basic Life Support providers from the AHA participated in this study. We simulated in-hospital cardiac arrest, and we limited participants to trained physicians. Results of CC could be in- fluenced by degree of training and education level of participants. Third, we performed CC on 2 kinds of mattresses and bed structures. B1 is commonly used in intensive care units, and B2 is usually used in general

Table 3

Chest compression with real-time feedback on B2 (n = 19)

	B2
	TDF 5 (n = 19)	TDF 6 (n = 19)	TDF 7 (n = 19)	P-value	TDF 5 vs TDF 6	TDF 5 vs TDF 7	TDF 6 vs TDF 7
CC depth (mm)	46.26 (4.49)	53.58 (3.15)	58.74 (2.10)	b0.001	b 0.001	b 0.001	b0.001
CC rate (rate/min)	101.11 (6.05)	102.26 (3.80)	101.05 (4.75)	0.57
CC velocity (mm/s)	165.59 (20.96)	198.41 (16.55)	226.42 (19.41)	b0.001	b 0.001	b 0.001	b0.001
Duty Cycle (%)	47 (2)	46 (2)	44 (3)	0.04	0.574	b 0.001	0.034
CC time (sec)	0.28 (0.26-0.29)	0.27 (0.26-0.28)	0.26 (0.25-0.28)	0.893
% of ACD (%)	7.01 (2.83-37.56)	96.68 (81.98-99.53)	100.00 (99.01-100.00)	b0.001	b 0.001	b 0.001	0.006
% of ICD (%)	0.00 (0.00-0.00)	0.00 (0.00-0.00)	0.00 (0.00-0.00)	0.50

Data with a normal distribution are given as mean (SD). The nonparametric variables are given as median (IQR). P b .05 is significant.

wards. However, many different kinds of mattresses and bed structures are used in hospitals, and the result of CC could be influenced by the dif- fering characteristics of dissimilar beds. Fourth, there were several methods to place feedback devices with an accelerometer on patients requiring CPR: (i) place the feedback device on the patient’s sternum,

(ii) place the feedback device on the dorsum of the hand, and

(iii) place the feedback device by pockets. We used a custom-made pocket to place the smartphone on the dorsum of the provider’s hand. The estimation of the acceleration signal could be influenced by the method chosen to place feedback devices. U-CPR is a smartphone appli- cation with a built-in 3-axis accelerometer, and the error rate according to the grasping orientations of devices and direction of compression was tolerable [8].

1. Conclusions

When CC was performed using a feedback device with an acceler- ometer on the bed in hospital settings, CPR providers could perform adequate CC by targeting depth of feedback device to 6 cm.

Contributors

S.L. and J.O. were involved in all aspects of the study design, design- ing and managing the study, interpreting findings, and cowriting the manuscript. W.K., Y.S., C.A., and Y.C. contributed to critical revision of the manuscript for important intellectual content. T.L., J.C., and H.K. contributed to study concept and design, interpretation of the data, critical revision of the manuscript for important intellectual content, and final approval of the version to be published.

Conflict of interests

No conflicts of interest.

Acknowledgment

The authors would like to thank Dr Lee Yoonjae for willingness to participate in this study.

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