Emergency. 2017; 5 (1): e30 OR I G I N A L RE S E A RC H Rapid Acute Physiology Score versus Rapid Emergency Medicine Score in Trauma Outcome Prediction; a Compar- ative Study Babak Nakhjavan-Shahraki1, Masoud Baikpour2, Mahmoud Yousefifard3, Zahra Sadat Nikseresht2, Samaneh Abiri4, Jalaledin Mirzay Razaz5, Gholamreza Faridaalaee6, Mahboob Pouraghae7, Sahar Shirzadegan7, Mostafa Hosseini8∗ 1. Sina Trauma and Surgery Research Center, Tehran University of Medical Sciences, Tehran, Iran. 2. Department of Medicine, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran. 3. Physiology Research Center and Department of Physiology, Faculty of Medicine, Iran University of Medical Sciences, Tehran, Iran. 4. Department of Emergency Medicine, Jahrom University of Medical Sciences, Jahrom, Iran. 5. Department of Community Nutrition, Faculty of Nutrition and Food Technology, Shahid Beheshti University of Medical Sciences, Tehran, Iran. 6. Department of Emergency Medicine, Maragheh University of Medical Sciences, Maragheh, Iran. 7. Emergency Medicine Research Team, Tabriz University of Medical Sciences, Tabriz, Iran. 8. Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran. Received: November 2016; Accepted: November 2016; Published online: 10 January 2017 Abstract: Introduction: Rapid acute physiology score (RAPS) and rapid emergency medicine score (REMS) are two phys- iologic models for measuring injury severity in emergency settings. The present study was designed to compare the two models in outcome prediction of trauma patients presenting to emergency department (ED). Methods: In this cross-sectional study, the two models of RAPS and REMS were compared regarding prediction of mor- tality and poor outcome (severe disability) of trauma patients presenting to the EDs of 5 educational hospitals. The discriminatory power and calibration of the models were calculated and compared using STATA 11. Results: 2148 patients with the mean age of 39.50±17.27 years were studied (75.56% males). The area under the curve of REMS and RAPS in predicting in-hospital mortality were 0.93 (95% CI: 0.92-0.95) and 0.899 (95% CI: 0.86-0.93), respectively (p=0.02). These measures were 0.92 (95% CI: 0.90-0.94) and 0.86 (95% CI: 0.83-0.90), respectively, re- garding poor outcome (p=0.001). The optimum cut-off point in predicting outcome was found to be 3 for REMS model and 2 for RAPS model. The sensitivity and specificity of REMS and RAPS in the mentioned cut offs were 95.93 vs. 85.37 and 77.63 vs. 83.51, respectively, in predicting mortality. Calibration and overall performance of the two models were acceptable. Conclusion: The present study showed that adding age and level of arterial oxygen saturation to the variables included in RAPS model can increase its predictive value. Therefore, it seems that REMS could be used for predicting mortality and poor outcome of trauma patients in emergency settings. Keywords: Multiple trauma; trauma severity indices; decision support techniques; prognosis; patient outcome assessment © Copyright (2017) Shahid Beheshti University of Medical Sciences Cite this article as: Nakhjavan-Shahraki B, Baikpour M, Yousefifard M, Nikseresht Z, Abiri S, Mirzay Razaz J, Faridaalaee Gh, Pouraghae M, Shirzadegan, S, Hosseini M. Rapid Acute Physiology Score versus Rapid Emergency Medicine Score in Trauma Outcome Prediction; a Compar- ative Study. Emergency. 2017; 5(1): e30. ∗Corresponding Author: Mostafa Hosseini, Department of Epidemiology and Biostatistics School of Public Health, Tehran University of Medical Sci- ences, Poursina Ave, Tehran, Iran; Email: mhossein110@yahoo.com; Tel: +982188989125; Fax: +982188989127 This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: www.jemerg.com B. Nakhjavan-Shahraki et al. 2 1. Introduction A ppropriate and timely management is strongly associ- ated with a decrease in morbidity and mortality rates in trauma patients (3). Emergency physicians provide one of the main levels of care in management of trauma pa- tients. However, the constant overcrowding of emergency departments (ED) might deprive the physicians and nurses of the time for appropriate management of patients. In this regard, one of the best ways to perform a quick assessment of the patients and take necessary measures accordingly is appropriate application of scoring systems (4-11). Applica- tion of screening tools to lower the time needed for assess- ment of patients can considerably improve the quality of care (12), increase the efficacy of treatments and lower morbid- ity and mortality rates. Various scoring systems have been developed and undergone gradual modifications through- out decades to increase their efficacy, accuracy and valid- ity. Despite the improvements in these scoring systems, un- fortunately they still have few shortcomings (13) and using them can be associated with multiple limitations. These lim- itations include the need for complicated calculations, the great number of variables they assess, and sometimes lack of validity evaluation in different clinical settings. There- fore, research in this field is still in progress and each year some new models are developed. In recent years, health or- ganizations have suggested to develop a physiologic scoring system for early detection of high-risk patients in order to regulate management of trauma patients and consequently, lower the burden of trauma injuries (14). One of these scor- ing systems was the Rapid Acute Physiology Score (RAPS), the abbreviated version of the acute physiology and chronic health evaluation (APACHE II) score in which physiologic variables including the heart rate, blood pressure, respira- tory rate and Glasgow Coma Scale (GCS) were considered as prognostic factors in trauma patients. Although the prognos- tic value of this model has been found to be acceptable for clinical use, researchers are still trying to improve its accu- racy (19). The other recently presented model is Rapid Emer- gency Medicine Score (REMS). This model incorporates the level of arterial oxygen saturation (O2 sat) and chronologi- cal age of patients with the variables included in the RAPS model and was initially proposed for predicting mortality in non-surgical patients admitted to ED (19, 20). However, the validity of this model in trauma patients has been evaluated in only a few studies. Since a limited number of variables have been included in RAPS and REMS models, assessing and calculating the score based on them is feasible and they can be easily used in EDs. However, only a few studies have compared the two models with each other (19) and disagree- ments still exist on which one to use when assessing a trauma patient. Accordingly, the present study aimed to assess and compare the prognostic value of RAPS and REMS models for in-hospital mortality and poor outcome of trauma patients presenting to ED. 2. Methods 2.1. Study design and setting In this cross-sectional study, the two models of RAPS and REMS were compared in predicting the in-hospital mortality and poor outcome (severe disability based on Glasgow out- come scale) in trauma patients presenting to ED. The study protocol was evaluated and approved by the Ethics Commit- tee of Tehran University of Medical Sciences. The authors adhered to the guidelines proposed by the Declaration of Helsinki throughout the study. The patients or their family members signed an informed written consent for participat- ing in the study. 2.2. Participants Data were gathered prospectively from EDs of 5 educational hospitals in Iran (Tehran, Tabriz, Urmia, Jahrom and Ilam) from May to October 2016. Trauma patients aged over 18 years old referring to ED were included through a conve- nience sampling method. Pregnant women and patients who expired at the event scene were excluded. 2.3. Data gathering In each ED, an emergency medicine physician prospectively gathered data on demographic characteristics of the pa- tients (age, gender and trauma mechanism), their signs and symptoms and findings of their physical examination and recorded the information in data collection forms. These data included all the factors needed for calculating RAPS and REMS models (19). Gathered information included age, body temperature, systolic and diastolic blood pressures (from which the mean arterial pressure was calculated), heart rate, respiratory rate, level of oxygen saturation and the patient’s level of consciousness based on GCS. All these factors were measured for the patients on arrival and then they were fol- lowed during their admission to record their final outcome (expired vs. alive) and the condition in which they were dis- charged from the hospital (full recovery, moderate disability, severe disability or vegetative state). 2.4. Outcome measurement The outcome of the patients on discharge from the hospital was evaluated using Glasgow outcome scale (21). The pri- mary outcome was in-hospital mortality and the secondary outcome was poor outcome defined as developing severe disabilities. This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: www.jemerg.com 3 Emergency. 2017; 5 (1): e30 Figure 1: Area under the curve (AUC) of rapid emergency medicine score (REMS) and rapid acute physiology score (RAPS) in prediction of in-hospital mortality and poor outcome. 2.5. Statistical Analysis The minimum sample size was calculated to be 1894 patients considering an in-hospital mortality rate of 5.2% among trauma patients (22), a confidence interval of 95% (α=0.05), a power of 90% (β=0.1) and a maximum error of 1.5% in es- timating the mortality rate (d=0.01). Data were entered into SPSS software version 21.0 and were analyzed by STATA 11.0 software. All the patients had two different scores based on REMS and RAPS models. Area under the receiving operating characteristics curve (AUC), sensitivity, specificity, and pos- itive and negative likelihood ratios with 95% confidence in- tervals (95% CI) were calculated for each model and subse- quently the discriminatory power was evaluated. AUC of the two models were compared based on the method proposed by Cleves and Rock (23). General calibration was assessed by drawing calibration plots, in which the number of observed versus predicted mortality or poor outcome per decile of the linear predictor of RAPS or REMS models were compared. In this plot, the reference line, with an intercept of zero and a slope of one, shows perfect calibration. Overall performance was also evaluated by assessing the predictive reliability and predictive accuracy through calculating Brier score. Finally, the Spearman’s rank coefficient was calculated to assess the concordance between REMS-predicted and RAPS-predicted percentage of mortality and poor outcome. A p<0.05 was considered as the level of significance in all the analyses. Table 1: Baseline characteristics of studied patients Variable Value Age (year) 39.50 ± 17.27 Gender(n, %) Male 1623 (75.56) Female 525 (24.44) Mechanism of trauma Motorcycle accident 591 (27.51) Car rider accident 518 (24.12) Pedestrian 378 (17.60) Falls more than 3 meters 152 (7.08) Falls less than 3 meters 201 (9.36) Other 308 (14.34) GCS 14.4 ± 2.19 HR (beat/minute) 87.60 ± 15.63 SBP (mmHg) 115.38 ± 15.36 DBP (mmHg) 73.49 ± 10.07 O2 sat (%) 94.78 ± 5.80 Temperature (Celsius) 36.81 ± 0.90 RR (number/minute) 16.46 ± 6.15 Outcome Good recovery 1630 (75.88) Moderate disability 342 (15.92) Severe disability 53 (2.47) Death 123 (5.73) Data were presented as mean ± standard deviation or fre- quency and percentage; GCS: Glasgow coma scale; HR: heart rate; SBP: systolic blood pressure; DBP: diastolic blood pres- sure; O2 Sat: arterial oxygen saturation; RR: respiratory rate. This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: www.jemerg.com B. Nakhjavan-Shahraki et al. 4 Figure 2: Calibration plots of rapid emergency medicine score (REMS) and rapid acute physiology score (RAPS) in prediction of in-hospital mortality and poor outcome. 3. Results 3.1. Baseline characteristics Data from a total of 2148 patients were gathered. The mean age of included patients was 39.50±17.27 years and 75.56% of them were male. Motorcycle accident (27.51%), Car rider ac- cident (24.12%) and the pedestrian (17.60%) were the most common mechanisms of injury. The mean values of vital signs, level of consciousness and arterial oxygen saturation in the studied trauma patients are presented in Table 1. Pa- tients were discharged from the hospital with a good recov- ery and mild disability in 75.88% of cases, moderate disability in 15.92% and severe disability in 2.47% of them. Eventually, 5.73% of the included patients expired. 3.2. Discrimination Figure 1 depicts the AUC of RAPS and REMS models in pre- dicting mortality and poor outcome. The AUC of REMS and RAPS models in predicting in-hospital mortality were 0.93 (95% CI: 0.92-0.95) and 0.899 (95% CI: 0.86-0.93), respec- tively, and the difference between the two was found to be statistically significant (p=0.02). Similarly, the AUC of REMS and RAPS in predicting poor outcome were calculated to be 0.92 (95% CI: 0.90-0.94) and 0.86 (95% CI: 0.83-0.90), re- spectively, with the differences being statistically significant (p=0.001). The optimum cut-off value for REMS model in predicting mortality and poor outcome was 3 while this fig- ure was found to be 2 for the RAPS model. Screening per- formance characteristics of REMS and RAPS models are pre- sented in Table 2. As can be seen, the sensitivity of REMS model was considerably higher than RAPS (95.63 vs. 85.37), while its specificity was found to be lower than that of the RAPS model in predicting mortality (77.63 vs. 83.51). Sim- ilar findings were yielded for predicting poor outcome in patients. Since both of these models were developed for screening trauma patients, the model with a higher sensi- tivity would be more suitable for this purpose. Therefore, it This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: www.jemerg.com 5 Emergency. 2017; 5 (1): e30 Figure 3: Concordance between rapid emergency medicine score (REMS) predicted and rapid acute physiology score (RAPS) predicted per- centage of mortality and poor outcome. seems that the value of REMS model in predicting mortality and poor outcome in trauma patients is higher than the RAPS model. 3.3. Calibration Figure 2 depicts the calibration plots of REMS and RAPS models, showing acceptable curves for both models in pre- dicting mortality and poor outcome. Calibration plot of REMS model in predicting in-hospital mortality had a slope and intercept of 0.98 and 0.001, respectively, while these fig- ures were found to be 0.96 and 0.003, respectively, for pre- dicting poor outcome. As for the RAPS model the calibration plot for predicting mortality had a slope and intercept of 1.01 and -0.0005 while these figures were calculated to be 1.009 and -0.0007, respectively, for predicting poor outcome. These plots indicate that both models are perfect in predicting both mortality and poor outcome in trauma patients. 3.4. Overall performance Brier score for REMS model in predicting mortality was 0.034 and the scaled reliability was found to be 0.0004. For the RAPS model, these figures were calculated to be 0.028 and 0.0001, respectively. Similar results were obtained on predic- tion of poor outcome. These findings confirm the high pre- dictive accuracy and reliability of the two models (Table 3). Finally, concordance between REMS and RAPS models was evaluated and a good correlation was observed in the pre- dicted risk of mortality (r=0.77; p <0.001) and poor outcome (r=0.77; p<0.001) between the two models (Figure 3). 4. Discussion The findings of the present study showed that both REMS and RAPS models have acceptable predictive values for mor- tality and poor outcome of adult trauma patients referring to EDs. However, in comparison it seems that the REMS model is slightly better than the RAPS model for this purpose. These findings were congruent with the results of the study con- ducted by Olsson et al. that showed the REMS model to be a strong predictor of in-hospital mortality in patients refer- ring to EDs and has a higher predictive value compared to RAPS model (24). These researchers aimed to assess the pre- dictive value of REMS model in three further studies, two of which indicated that this model is a strong tool for pre- dicting mortality in non-surgical patients (19, 20). The third study showed that even with incorporation of the Charlson comorbidity index in the analyses, the REMS model has a high predictive value for mortality of non-surgical patients (25). In another study conducted on 3680 patients, Imholff et al. showed that a higher REMS score is associated with an in- crease in the mortality rate of trauma patients. These authors suggest that this scoring system is a simple and accurate pre- dictor for in-hospital mortality of trauma patients (22). In their survey aiming to evaluate the role of REMS model in predicting mortality of patients infected with Vibrio vulnifi- cus, Kuo et al. also found that this model provides an ac- ceptable predictive value for mortality of patients (26). Ha et al. aimed to compare the prognostic performance of the two REMS model and Worthing Physiological Scoring sys- tem in predicting mortality of patients referring to EDs and found that both models have acceptable prognostic perfor- This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: www.jemerg.com B. Nakhjavan-Shahraki et al. 6 Table 2: Screening performance characteristics of rapid emergency medicine score (REMS) and rapid acute physiology score (RAPS) in pre- diction of mortality and poor outcome Characteristics Mortality Poor outcome REMS RAPS REMS RAPS True positive 118 105 162 136 True negative 1572 1691 1563 1669 False positive 453 334 409 303 False negative 5 18 14 40 Sensitivity 95.93 (90.30-98.49) 85.37 (77.59-90.86) 92.04 (86.75-95.42) 77.27 (70.23-83.09) Specificity 77.63 (75.74-79.42) 83.51 (81.80-85.08) 79.26 (77.39-81.02) 84.64 (82.95-86.18) PositiveLR 4.29 (3.92-4.69) 5.18 (4.58-5.85) 4.44 (4.03-4.89) 5.03 (4.41-5.72) Negative LR 0.05 (0.02-0.12) 0.18 (0.11-0.27) 0.10 (0.06-0.17) 0.27 (0.20-0.35) ∗ Data are presented as estimated value and 95% confidence interval. LR: Likelihood ratio. Table 3: Overall performance of rapid emergency medicine score (REMS) and rapid acute physiology score (RAPS) in prediction of in-hospital mortality and poor outcome Characteristics Mortality Poor outcome REMS RAPS REMS RAPS Brier score 0.034 0.028 0.049 0.043 Scaled reliability 0.0004 0.0001 0.0005 0.0003 mances, with the Worthing Physiological Scoring system be- ing slightly better that the REMS model (27). Bulut et al. eval- uated 2000 patients and reported that although both models have moderate predictive values, but the prognostic value of REMS model for mortality of patients referring to EDs was significantly higher than Modified Early Warning Score (28). As can be seen, slight disagreements can be observed be- tween the results of various studies considering the prog- nostic value of REMS and RAPS models for mortality of pa- tients, which can be attributed to the differences in settings of the surveys. Various scoring systems have been developed for classification of injuries, which include physiologic and anatomic systems, specialized trauma scoring systems and combined scores (29). Each of these systems has their own specific limitations and advantages, but a scoring system that is going to be used in the emergency settings should involve fewer variables and be easy to use. In this regard, the RAPS model, which includes few variables, might be a good can- didate for application in emergency settings, but to increase its predictive value, the two variables of age and arterial oxy- gen saturation level were added to the model and the REMS model was developed. Results of the present study, based on calculated AUCs, showed that predictive value of RAPS model for in-hospital mortality (AUC=0.899) and poor out- come of patients (AUC=0.86) were good, while the prognos- tic values of REMS model were found to be excellent for mor- tality (AUC=0.93) and poor outcome (p=0.92). The relatively large sample population and the multi-center setting can be considered as the strengths of the present study, which war- rants its power. Having included patients from five cities of Tehran, Tabriz, Urmia, Jahrom and Ilam reassured the repre- sentativeness of the findings to the whole Iranian population. Accordingly, it seems that REMS model has a higher value for predicting in-hospital mortality and poor outcome of trauma patients presenting to EDs. 5. Limitation Employing a convenience sampling method suggests pres- ence of selection bias in this study. Another limitation of this survey was inclusion of body temperature in the analy- ses based on an axillary reading which might not be accurate particularly in overcrowded emergency settings and can af- fect the final interpretation of results. 6. Conclusion The present study showed that adding age and the level of ar- terial oxygen saturation to the variables included in the RAPS model can increase its predictive value. Therefore, it seems that REMS could be used for predicting mortality and poor outcome of trauma patients in emergency settings. 7. Appendix 7.1. Acknowledgements The authors wish to acknowledge the cooperation of Tehran, Tabriz, Urmia, Jahrom and Ilam emergency departments in This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: www.jemerg.com 7 Emergency. 2017; 5 (1): e30 providing patient data. 7.2. Author contribution All authors passed four criteria for authorship contribution based on recommendations of the International Committee of Medical Journal Editors. 7.3. 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This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: www.jemerg.com Introduction Methods Results Discussion Limitation Conclusion Appendix References