AbstractObjectiveWe aimed to estimate the accuracy of visual estimation of chest compression depth and identify potential factors affecting accuracy.
MethodsThis simulation study used a basic life support mannequin, the Ambu man. We recorded chest compression with 7 different depths from 1 to 7 cm. Each video clip was recorded for a cycle of compression. Three different viewpoints were used to record the video. After filming, 25 clips were randomly selected. Health care providers in an emergency department were asked to estimate the depth of compressions while watching the selected video clips. Examiner determinants such as experience and cardiopulmonary resuscitation training and environment determinants such as the location of the camera (examiner) were collected and analyzed. An estimated depth was considered correct if it was consistent with the one recorded. A multivariate analysis predicting the accuracy of compression depth estimation was performed.
ResultsOverall, 103 subjects were enrolled in the study; 42 (40.8%) were physicians, 56 (54.4%) nurses, and 5 (4.8%) emergency medical technicians. The mean accuracy was 0.89 (standard deviation, 0.76). Among examiner determinants, only subjects’ occupation and clinical experience showed significant association with outcome (P=0.03 and P=0.08, respectively). All environmental determinants showed significant association with the outcome (all P<0.001). Multivariate analysis showed that accuracy rate was significantly associated with occupation, camera position, and compression depth.
INTRODUCTIONHigh-quality chest compression is an essential element for achieving a good prognosis in cardiac arrest patients. These compressions are composed of an appropriate compression rate (>100/min), appropriate chest compression depth (>5 cm), and sufficient chest recoil [1,2]. Guidelines state the need for mutual feedback among resuscitation providers to maintain these quality indices [3]. Based on the 2010 European Resuscitation Council (ERC) guideline, a team leader should evaluate the quality of the cardiopulmonary resuscitation (CPR) and can change the person providing CPR if necessary [4].
There have been many studies suggesting the effectiveness of feedback devices that measure the rate, depth, and release of chest compression [5-8]. Information on feedback devices was also included in the American Heart Association (AHA; class IIa, level of evidence B) and ERC guidelines since 2010 [1,2].
However, feedback devices are not used frequently in many real-life CPR locations [9]. Frequently, providers choose not to apply the device because of reluctance and ignorance of effectiveness and time and cost to import new systems [9,10]. In many cases, feedback is based on visual estimation by the naked eye. Although objective indicators such as compression rate, respiration rate, and end-tidal carbon dioxide concentration are easy to estimate, compression depth can be difficult.
The purpose of our study was to estimate the accuracy of compression depth estimation and to identify potential factors affecting accuracy.
METHODSStudy design and settingThis study was approved by the Institutional Review Board (approval number SMC 2014-03-099-001). This study was a simulation study using mannequins (Ambu man, Ballerup, Denmark). Five basic life support providers volunteered to have video clips recorded while they did chest compressions. They were asked to perform chest compressions of different depths: ≥ 0 & < 1, ≥ 1 & <2, ≥2 & <3, ≥3 & <4, ≥4 & <5, ≥5 &<6, and ≥6 & <7 cm. Compressors did chest compression for a cycle of 30 compressions on their knees. To maintain the same level of depth throughout a cycle, a quality manager monitored compression depths using the Smartman, a PC program provided by the manufacturer (Fig. 1D).
Video clips were recorded by a smartphone camera (Galaxy S3, 1080p Recording System, Samsung, Suwon, Korea). We used 3 recording positions: cephalic (Fig. 1A), side (Fig. 1B), and caudal (Fig. 1C). Camera height was fixed at 155 cm from the floor and 125 cm from the bed on which the mannequin was placed. Of 175 total clips, we randomly sampled 25, 5 clips from each volunteer. An internet-based randomization program was used for the sampling process (http://randomization.com).
Study participantsStudy participants were enrolled from a single, tertiary, teaching hospital. All were working or have worked in the emergency department. Shortly after explaining the study, participants filled out survey forms and watched the video clips while estimating the depth of each clip.
Methods and measurementsTo measure examiner determinants, we collected information regarding gender, age, occupation, affiliated department, clinical career, resuscitation certification state, and the number of CPR experiences during the past year. To measure the environmental determinants, we included the recording position, chest compressor’s gender, and compression depth.
OutcomesAn estimated depth was considered correct if the recorded depth and the estimated depth were all >5 cm, or all <5 cm (Fig. 2A). For example, if a recorded depth was 1–2 cm and the answer was 4 cm, the answer was considered correct because both depths were consistent with insufficient compression. We used this criterion because the current AHA guideline states that a provider has to compress >5 cm.
AnalysisAll statistical analyses were performed with STATA ver. 13.0 (Stata Co., College Station, TX, USA). Baseline characteristics of this study were expressed as numbers, percentages, and means with standard deviations (SDs). The results are presented as mean with SD and 95% confidence intervals (CIs). Comparisons of continuous data were performed using the t-test and analysis of variance. To identify factors affecting accuracy of compression depth estimation, a multivariate logistic regression was used. P-values <0.05 were considered statistically significant for all statistical testing.
RESULTSCharacteristics of study subjectsA total of 103 health care providers participated in this study. Table 1 shows the demographic characteristics of the participants. The number of males was 28 (27.2%). The mean age was 29.5 years (SD, 5.2). Forty-two (40.8%) were physicians, 56 (54.4%) were nurses, and 5 (4.8%) were emergency medical technicians (EMTs); 81 (78.6%) were currently affiliated with the emergency department; 13 (12.6%) had valid CPR instructor certification issued by the AHA; and 23 (22.3%) had advanced cardiac life support certification. In terms of CPR experience, 45 participants (43.7%) had more than 21 CPR experiences during the past year (Table 1).
Main resultsThe mean estimated accuracy was 0.89 (SD 0.76). Table 2 shows the examiner determinants. There was no significant association between accuracy and gender, age, affiliated department, clinical career, instructor certification, or advanced cardiac life support certification. The accuracy rate and number of CPR experiences showed a trend of negative association (P=0.08). The estimated accuracy rate was significantly higher among nurses (P=0.02).
Table 3 shows environmental determinants associated with the estimated accuracy. The percentage of correct answers was significantly higher in video clips that were recorded at the bed side and in the caudal position than in the cephalic position (P<0.001). The estimated accuracy was significantly higher with female compressors (P<0.001). The estimated accuracy was significantly lower in video clips with 4–5 cm or 5–6 cm compressions (P<0.001).
The 5 factors associated with the estimated accuracy were selected and included for multivariate analysis. The results from logistic regression analysis of these factors are shown in Table 4. The adjusted odds ratio (AOR) of correct estimation was 1.50 (95% CI, 1.09 to 2.09; P=0.01) for nurses compared with physicians. The AOR was 8.19 (95% CI, 5.40 to 12.4; P<0.001) for the caudal position compared with the cephalic position. The AORs were 0.68, 0.03, and 0.06, respectively, for 3–4, 4–5, and 5–6 cm video clips compared with the 0–1 cm video clips.
Sensitivity analysisWe performed sensitivity analysis using different standards for the correct answers. For the ERC guideline–based analysis, the mean accuracy was 0.75 (SD, 0.06). Multivariate analysis revealed statistically significant factors including camera position, compressor gender, and compression depth. For the strict standard, mean accuracy was only 0.40 (SD, 0.11) and the significant factors were the same as the ERC guideline–based standard (Appendix Tables 1–6).
DISCUSSIONIn this study, we investigated the accuracy of chest compression depth estimation. The accuracy rate was 0.89 (SD, 0.76), which was similar to previous research [11]. However, when more strict criteria were applied, the estimated accuracy declined to 0.75 (SD, 0.40).
Although current guidelines have addressed the importance of mutual feedback, only a few have studied the effect. Lynch et al. [11] found that examiner’s estimation was not sufficient to determine providers’ performance. Although multiple studies reported the importance of a feedback system, there was no research performed with health care providers in the field [12,13].
Accuracy rates did not differ with clinical experience or gender, perhaps because measuring chest compressions is a very simple, low-tech procedure that does not require much experience. No difference in measuring skill was noted in previous studies either [14,15].
In this study, the multivariate analysis showed nurses’ superiority over physicians estimating compression depth, which may result from the tendency of nurses to follow protocols more strictly than physicians. Nurses tend to stand at a distance from chest compression procedures, which may have given them more room to observe chest compression than physicians. However, factors not measured in this study, such as willingness to make an accurate estimation or professional experience, could have caused interactions among variables.
Camera position was highly correlated with the accuracy rate, indicating that one should stand at the side or foot of the patient to measure compression quality more accurately. In our study, accuracy rate dropped when the depth was near the target range, which emphasizes that the estimated accuracy in actual practice could be much lower than the result presented here. CPR team leaders should consider these factors while leading a CPR team. Another option could include a real-time feedback system for compression depth.
To improve the accuracy of estimation and feedback with precise information, applying new devices and measures is necessary. These measures include physiologic data such as end-tidal carbon dioxide levels, cerebral oximetry, and mechanically produced data such as compression depth by gyroscopes.
There are some major limitations to this study. First, study subjects did not fully represent the general population because subjects were enrolled from a single center. Second, watching an actual CPR process could be different from watching a video clip. Each individual used his or her own height and preferred position, which was standardized during the study. Third, the small number of participants could have influenced the negative outcomes. For example, EMT occupation showed an odds ratio of 0.70 without statistical significance. Because the number of enrolled EMTs was only 5, more participants could have led to different results. Last, dividing compression depth into multiple scales would have complicated the determination of depth by examiners, which would have increased the inaccuracy of estimation.
In conclusions, the accuracy of chest compression depth estimation was 0.89 (SD, 0.76) in this simulation model. The factors affecting accuracy were occupation, recording position, and compression depth itself.
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Table 1.Table 2.Table 3.Table 4.AppendicesAppendix Table 1.Examiner-determinants based on European Resuscitation Council guideline criteriaAppendix Table 2.Environment-determinants based on European Resuscitation Council guideline standardAppendix Table 3.Logistic regression based on European Resuscitation Council guideline criteriaAppendix Table 4.Examiner-determinants based on strict criteriaAppendix Table 5.Environment-determinants based on strict criteriaAppendix Table 6.Logistic regression based on strict criteria |
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