|Year : 2017 | Volume
| Issue : 3 | Page : 25-30
A new prehospital score to predict hospitalization in trauma patients
Shahrokh Yousefzadeh-Chabosk1, Zahra Haghdoost1, Zahra Mohtasham-Amiri1, Ali Davoudi-Kiakalayeh2, Alireza Razzaghi1, Ehsan Kazemnegad-Leili1, Leila Kouchakinejad1
1 Guilan Road Trauma Research Center, Guilan University of Medical Sciences, Rasht, Iran
2 Department of Neurobiology, Division of Family Medicine, Karolinska Institute, Alfred NobelsAllé 12 141 83 Huddinge, Sweden
|Date of Web Publication||29-Nov-2017|
Poursina Hospital, Namjoo St., Rasht, Guilan
Source of Support: None, Conflict of Interest: None
Background: Prehospital scores are used for determining the prognosis of trauma severity in trauma patients. Objectives: This study aimed at developing a new prehospital score for emergency medical service (EMS) staff to predict hospitalization in trauma patients transferred to the hospital. Patients and Methods: This study was a diagnostic test evaluation conducted on data of 1185 traumatic patients transferred through EMS to Poursina Hospital of Rasht between March 2012 and March 2013. Data were collected using a questionnaire with two parts. The first part included data on demography, injury, and type of interventions performed at the scene of the accident. The second part consisted of initial evaluations (Glasgow coma scale (GCS), oxygen saturation (O2S), pulse rate (PR), systolic blood pressure (SBP), the ability to walk, and outcome (hospitalization, nonhospitalization). The questionnaire was filled out by EMS staff at the scene or during transfer to the hospital with respect to clinical observations. Data were analyzed using the logistic regression model. The Hosmer–Lemeshow test was also used to examine the good fit of model. Results: A total of 1185 patients were evaluated using prehospital data. Of seven variables evaluated by the scoring system, only four variables were identified in the regression analysis as predictors of hospitalization including age, SBP, O2S, and walking ability. Sensitivity, specificity, and positive and negative likelihood ratios were 0.67, 0.68, 2.09, and 0.48, respectively. Conclusions: The GOMAAPS (GCS, O2S, mechanism of injury, age, ability to walk, PR, and SBP) score serves as a guide for the EMS staff at the scene to be understood of the necessity of transfer and predicting hospitalization.
Keywords: Hospitalization, prehospital, score, trauma
|How to cite this article:|
Yousefzadeh-Chabosk S, Haghdoost Z, Mohtasham-Amiri Z, Davoudi-Kiakalayeh A, Razzaghi A, Kazemnegad-Leili E, Kouchakinejad L. A new prehospital score to predict hospitalization in trauma patients. Arch Trauma Res 2017;6:25-30
|How to cite this URL:|
Yousefzadeh-Chabosk S, Haghdoost Z, Mohtasham-Amiri Z, Davoudi-Kiakalayeh A, Razzaghi A, Kazemnegad-Leili E, Kouchakinejad L. A new prehospital score to predict hospitalization in trauma patients. Arch Trauma Res [serial online] 2017 [cited 2018 Apr 25];6:25-30. Available from: http://www.archtrauma.com/text.asp?2017/6/3/25/219406
| Background|| |
Trauma is a major problem all over the world, particularly affecting the young. It causes remarkable production loss. In recent decades, it has been considered as a critical health issue. Trauma is reported as the leading cause of death in people between 1 and 44 years old; also the third leading cause for all age groups, following cancer and cardiovascular diseases. Trauma imposes many economic costs on society. Of more than five million annual trauma deaths, over 90% occur in low- and middle-income countries. In Iran, it is the second cause of death after cardiovascular diseases and the leading cause of disability-adjusted life years.
The important steps for managing traumatic patients are to determine the severity and prognosis of trauma. Hence, trauma scoring systems have been used for nearly four decades to characterize the type and severity of trauma, predict outcome, improve resource allocation, and assist in clinical decision-making of trauma patients in both pre- and in-hospital phases.,,,, Many prehospital trauma scores have been developed so far, which enable the emergency medical service (EMS) staff in the precise evaluation of trauma severity to minimize the multitude damage by early diagnosis and timely actions.,, This can avoid many unnecessary transfers of mild traumatic patients to hospital. Trauma scoring tools include systems based on the anatomical, physiological, or combined criteria. Calculating anatomical scores, such as injury severity score (ISS, an index of severity and location of anatomy injury) and combined scores such as trauma ISS (based on the ISS and the revised trauma score (RTS), age, and injury mechanism),,, at the scene is difficult. Therefore, physiological scores such as RTS are used more than those scores in the prehospital phase. The RTS is the most widely used physiological score., The variables that are taken into consideration in RTS are respiratory rate (RR), systolic blood pressure (SBP), and Glasgow coma scale (GCS). The RTS is the sum of the coded value multiplied of these variables. It can be evaluated on-site; however, too complicated to calculate under stressful situation. Therefore, the triage RTS (T-RTS) was designed with the same variables, but simpler to calculate.
There are some debates about RR, one of the main components in calculating RTS and T-RTS. Respiratory rate is usually measured clinically, and it may not have high reliability. Moreover, because of pain or psychological stress in some patients, the correlation of RR with ventilation and/or oxygenation may be disrupted. In addition, this vital sign is rarely completely recorded  and cannot be calculated in patients with intubation.
To improve usefulness, there is a need for scoring systems without respiratory rate., Thus, MGAP and GAP scores and model of Toyoda et al. in Japan were developed.,, The present study was similar to the research of Toyoda et al., in which they had attempted to provide a tool for prediction of hospitalization according to prehospital physiological factors such as age, SBP, oxygen saturation (O2S), pulse rate (PR), consciousness level, and ability to walk. In addition to abovementioned factors, we applied mechanism of injury for developing a new prehospital score, as in other similar studies, because it is one of the major factors affecting the outcome of traumatic patients., Thus, GOMAAPS, which is a compound score of variables (GCS, O2S, mechanism of injury, age, ability to walk, PR, and SBP), was developed as a new score.
This study aimed to develop a new prehospital score for EMS staff at the scene to understand the necessity of transfer and predict hospitalization for trauma patients transferred to a referral hospital in Rasht City, Iran, during 2012–2013.
| Patients and Methods|| |
In this study, after approval by the ethics committee of Guilan University of Medical Sciences (Code: 1910396409), a diagnostic test evaluation was conducted in a public university hospital (Poursina) in Rasht, Guilan, between March 2012 and March 2013. Poursina is a referral hospital for traumatic patients with 263 active beds.
A total of 1185 trauma patients between 2 and 95 years of age who had been transferred directly to Poursina Hospital by the EMS staff were included in the study. Patients who were dead (on site or arrival) or had incomplete prehospital data were excluded from the study.
Our outcome measure was necessity of transfer and admission to hospital in trauma patients according to the severity of trauma. According to a search on literature, this study used seven variables extracted from a researcher-made questionnaire as follows: age, SBP, GCS, PR, ability to walk, O2S, and mechanism of injury.
The questionnaire had two parts. The first one included demographic variables (age, sex [male and female], data on the injury [time, mechanism of injury [as specified in [Table 1], type of injury [blunt or penetrate], area of injury [head and neck, face, abdomen, pelvis, spine, extremities, and chest]); type of interventions by the EMS staff at the scene of accident (intubation, intravenous line, infusion of fluids, medication prescription, splinting, wearing cervical collar, cardiopulmonary resuscitation, using backboard for lumbar trauma, putting airway tube, oxygen masks, external hemorrhage control, and putting nasogastric tube); and underlying diseases (cardiovascular, respiratory, neurologic, hepatic, renal, coagulative, gastrointestinal, infectious, diabetes, malignant, disabilities, psychological, and history of trauma or surgery, drug abuse and special diseases). The second part consisted of an initial assessment (GCS, O2S, PR, SBP, the ability to walk, and outcome [hospitalization, nonhospitalization]).
In our study, the questionnaire was filled out by the EMS staff at the scene or during transfer to the hospital with respect to clinical observations. Thus, age, SBP, GCS, PR, ability to walk, O2S, and mechanism of injury were analyzed to calculate the score. All the predictors were entered into logistic regression in categorical form. The coefficients of categories in each predictor were obtained considering the reference group. In SBP, GCS, O2S, and PR, a normal clinical range was considered as the reference group [Table 1].
It should be noted that this classification is based on a Japanese research by Toyoda et al., but we had to make changes in some of the subgroups due to their poor distribution.
Logistic regression model (the backward likelihood ratio method)
The logistic regression model was fitted to the data, and hospitalization was predicted accordingly. The Backward LR model was used, and the Hosmer–Lemeshow goodness of fit was applied for assessing the fitness of the model. The results of logistic regression analysis showed the sensitivity, specificity, and accuracy of the model. Predictive variables for the logistic regression model included variables such as age, SBP, GCS, PR, O2S categorized, ability to walk, and the mechanism of injury. To control the probable confounding effects, other variables were included in the model such as underlying disease history (yes or no), type of injury (penetrating, blunt, both, unknown), and sex (male/female).
Using the logistic regression model, the effective variables on hospitalization prediction were determined. Then, the β-coefficients were specified for each independent variable. The value of the GOMAAPS score was given regarding the significant β-coefficient in each group compared with the reference group. Thus, when β-coefficients in significant predictors was <0.5, 0.5–1, or ≥1, we considered the prehospital score component equated 1, 2, or 3, respectively. In the case of nonsignificant β-levels, zero score was given as the reference group. Finally, the total score was calculated by summing the scores of components in the range of 0–10.
The diagnostic value of the score was evaluated by sensitivity, specificity, accuracy, diagnosis likelihood ratio (LR+, LR−), and cut-off point based on receiver operating characteristics. Next, the associated variables with the outcome (hospitalization, nonhospitalization) and value of this score for each individual were identified. The overall mean was calculated for both as well as individual hospitalization and nonhospitalization groups. Data were analyzed using the SPSS version 18.0 (SPSS Inc. Released 2009. PASW Statistics for Windows, Chicago: SPSS Inc.).
| Results|| |
A total of 1185 traumatic patients were included. Most patients were men (72.6%). The mean age of the patients was 35.13 ± 21.69 years. The mean age of women (37.85 ± 21.21 years) was more than that of men (34.11 ± 21.79 years). Univariate regression analysis was performed for each of the factors. The size of the standard error related to GCS was too high; so, the GCS variable was excluded. The area under the curve was 71% (95% confidence interval = 68%–73%) [Figure 1], and optimal cutoff point score ≥2.5 was determined for predicting hospitalization. Sensitivity, specificity, positive-, and negative-likelihood ratios were 0.67, 0.68, 2.09, and 0.48, respectively [Table 2].
|Figure 1: ROC Curve to Predict Hospitalization of the Traumatic Patients with a Reference Line. The area under the curve was 0.71. ROC: Receiver Operating Characteristic|
Click here to view
|Table 2: Sensitivity, Specificity, LR+ and LR- for Predicting Hospitalization in Traumatic Patients|
Click here to view
The multivariate regression model was designed using age, SBP, walking ability, O2S, PR, and mechanism of injury. Then, a regression model was developed in three steps. At first, all the independent predictive variables (the six remaining variables) and potential confounding variables (sex, type of injury, and underlying disease history) were included in the model. In the second step, PR was excluded. Finally, the mechanism of injury was also excluded and other predictive and confounding variables of sex and type of injury remained.
On variable of “type of injury,” due to a small sample size in items “both” and “unknown,” the “unknown” ones were automatically excluded from the study and “both” items were mixed in the “penetrate” group.
The final model containing four variables of age, SBP, O2S, and the ability to walk (main predictors of hospitalization of traumatic patients) was identified by the logistic regression model [Table 3]. Thereby, the new score was calculated based on only four variables (age, SBP, O2S and the ability to walk) [Table 4].
|Table 3: Values of the Coefficients and Odds Ratios by Logistic Regression Model|
Click here to view
|Table 4: GOMAAPS Score for Predictive Variables of Hospitalization in Poursina Hospital|
Click here to view
The mean score in all patients in the prehospital phase was 2.25 (standard deviation [SD] = 2.16), the lowest and highest values were 0 and 10, respectively. The mean score in hospitalization group (mean = 2.923, SD = 1.290) was higher than that in nonhospitalization group (mean = 1.653, SD = 1.290), which was statistically significant (P < 0.001) [Table 5].
|Table 5: Mean of GOMAAPS Score in Prehospital Phase in both Groups (Hospitalization and non-Hospitalization)|
Click here to view
| Discussion|| |
This study aimed at developing a new score and evaluating the relationship between prehospital data and need to hospitalization in traumatic patients. The new introduced score included seven predictive factors including GCS, O2S, mechanism of injury, age, walking ability, PR, and SBP. Using a prediction tool will help us to objectively assess prognosis, focus on resource management as well as optimize the level of care.
The T-RTS and T-RTS are the most widely used scores in prehospital setting but are not easy to calculate. Therefore, MGAP was designed that could predict mortality better than RTS. This score was able to clearly identify patients with low-, intermediate-, and high-risk of mortality. Then, the GAP score was suggested, a better predictor and more generalizable than the MGAP score. This score predicted trauma severity as well as or better than the other trauma scores and was easier to calculate. All the prior scores were based on prehospital data. Overall, they are broadly used in the emergency department (ED) rather than in the prehospital phase for predicting the outcome of traumatic patients. If they are used in the ED, they should be allocated according to ED data. While similar to the results of the study of Toyoda et al., the GOMAAPS score is calculated according to prehospital data to help the EMS staff in critical decision-making about transfer and hospitalization of traumatic patients in a referral hospital.
We used all the variables of this study, in addition to the mechanism of trauma. It is an important factor influencing the outcome of traumatic patients. The mechanism of injury, a major trauma identifier, is considered as a symptom for patient transfer to the trauma center and one of the components of triage score in trauma. Furthermore, this variable reduces overtriage and undertriage when it is placed along with other components of a trauma scoring tool.
Following modeling using multiple logistic regression, age, SBP, O2S, and the ability to walk were found significant predictive factors for hospitalization. However, GCS, the mechanism of injury and PR excluded from the model. Perhaps, the main reason was the small sample size. Indeed, there are possibilities for GCS that may limit accuracy and usefulness of GCS classification at the scene. For example, in patients who suffered from poisoning or face injuries,, the score could not be calculated correctly. Another reason is the poor performance of the predictive power of initial GCS score in the prehospital phase, especially in the epidural hematoma. Despite the bad prognosis of epidural hematoma, patients are conscious in the early hours following trauma. Furthermore, Kehoe et al. (2015) in their study showed that in all cases, GCS is not regarded as a reliable factor for predicting outcomes, especially in elderly patients with severe brain trauma. It should be noted that in the present study, people older than 60 years of age constitute about 10% of the sample size.
In this study, the mechanism of injury was not significant. Other studies are consistent with our results that showed no significant relationship between the mechanism of injury and the patients' outcomes., Besides, the trauma mechanism score might not work without an anatomical score.
Sensitivity, specificity, positive-, and negative-likelihood ratios, positive and negative predictive values (PPV and NPV) with the cutoff point score of ≥2.5 were 0.67, 0.68, 2.09, 0.48, 0.75, and 0.59, respectively. Sensitivity, specificity, positive-, and negative-predictive values in Toyoda et al. score with cutoff ≥2 were 97%, 16%, 39%, and 89%, respectively. In MGAP, sensitivity, specificity, positive-, and negative-likelihood ratios were 0.95, 0.70, 3.13, and 0.07, respectively.
An increased GOMAAP score led to an increase in the number of patients who required hospitalization. So that if score ≤1 and score ≥8, approximately 39% and 100% of the patients were hospitalized, respectively. Given that in score ≤1, most transferred patients were considered in the nonhospitalization group. The EMS staff could, with correct triage and proper management, avoid transfer of such patients to Poursina Hospital. Toyoda et al. (2007) showed that if the patients with score ≤1 were not transferred with ambulance, 16% of the cases with inappropriate ambulance usage would be prevented.
Therefore, GOMAAPS is a scoring system which can enable EMS staff at the scene to prioritize the transfer of trauma patients to hospital.
The present study has several limitations. First, some patients may take score ≤1 with this scoring system, while requiring quick transfer to hospital and receiving emergency treatment interventions, such as head trauma patients with epidural hematoma. These patients appear to have no clinical symptoms in the initial evaluation. Hence, exact evaluation on patient's basic problems should be performed along with the score calculation. Second, in addition to the seven variables studied in this scoring system, other variables such as body temperature may affect the prediction of outcomes that were not considered in this study because the body temperature is not evaluated for all patients according to the report form on emergency care in the prehospital phase. Therefore, it is recommended that the impact of this variable on patients' outcome is assessed in the future studies. Third, criteria for hospitalization partly depend on individual skills of emergency physicians. Some might be misdiagnosed in the emergency room and not hospitalized promptly or vice versa. Finally, more studies are required to research the exact role of the GOMAAPS in the prehospital phase.
| Conclusion|| |
Applying a lot of strategies and tools to evaluate the patients precisely, developing prevention programs, improving quality of provided services, and ultimately improving patient health must be concerns of researchers, policy-makers and managers of health care system. GO MAAPS tool can considered as a tool to determine the severity of trauma in traumatic patients to improve the quality of services provided to these patients and save the lives of them. In this study, the predictive ability of this tool in traumatic patients admitted to the hospital as well as the score ability to prevent ambulance transportation in mild traumatic patients were studied. Using obtained results of the study and similar studies in policy-making and management of health system as well as utilizing this tool in surveillance system can have key role in patient's situation evaluation in pre-hospital and hospital phase, appropriate planning for right and timely providing hospitalization services for patients (secondary level of prevention), and reduced hospital costs.
We thank Ms. Fatemeh Javadi for translating the manuscript.
Financial support and sponsorship
There is no financial support.
Conflicts of interest
There are no conflicts of interest.
| References|| |
Hunt RC, Brasel KJ. Advanced Trauma Life Support Program for Doctors. ACS: American College of Surgeons Committee on Trauma; 2004.
Sauaia A, Moore FA, Moore EE, Moser KS, Brennan R, Read RA, et al.
Epidemiology of trauma deaths: A reassessment. J Trauma 1995;38:185-93.
Chandran A, Hyder AA, Peek-Asa C. The global burden of unintentional injuries and an agenda for progress. Epidemiol Rev 2010;32:110-20.
Montazeri A. Road-traffic-related mortality in Iran: A descriptive study. Public Health 2004;118:110-3.
Civil ID, Schwab CW. The abbreviated injury scale, 1985 revision: A condensed chart for clinical use. J Trauma 1988;28:87-90.
Wisner DH. History and current status of trauma scoring systems. Arch Surg 1992;127:111-7.
Kirkpatrick JR, Youmans RL. Trauma index. An aide in the evaluation of injury victims. J Trauma 1971;11:711-4.
Kondo Y, Abe T, Kohshi K, Tokuda Y, Cook EF, Kukita I, et al.
Revised trauma scoring system to predict in-hospital mortality in the emergency department: Glasgow Coma Scale, Age, and Systolic Blood Pressure Score. Crit Care 2011;15:R191.
Darbandsar Mazandarani P, Heydari K, Hatamabadi H, Kashani P, Jamali Danesh Y. Acute Physiology and Chronic Health Evaluation (APACHE) III score compared to Trauma-Injury Severity Score (TRISS) in predicting mortality of trauma patients. Emerg (Tehran) 2016;4:88-91.
MacKenzie EJ, Rivara FP, Jurkovich GJ, Nathens AB, Frey KP, Egleston BL, et al.
A national evaluation of the effect of trauma-center care on mortality. N Engl J Med 2006;354:366-78.
Rehn M, Perel P, Blackhall K, Lossius HM. Prognostic models for the early care of trauma patients: A systematic review. Scand J Trauma Resusc Emerg Med 2011;19:17.
Varghese M, Sasser S, Kellermann A, Lormand JD. Prehospital Trauma Care Systems. Geneva: World Health Organization; 2005.
Toyoda Y, Matsuo Y, Tanaka H, Fujiwara H, Takatorige T, Iso H, et al.
Prehospital score for acute disease: A community-based observational study in Japan. BMC Emerg Med 2007;7:17.
Norouzi V, Feizi I, Vatankhah S, Pourshaikhian M. Calculation of the probability of survival for trauma patients based on trauma score and the injury severity score model in fatemi hospital in ardabil. Arch Trauma Res 2013;2:30-5.
Brockamp T, Maegele M, Gaarder C, Goslings JC, Cohen MJ, Lefering R, et al.
Comparison of the predictive performance of the BIG, TRISS, and PS09 score in an adult trauma population derived from multiple international trauma registries. Crit Care 2013;17:R134.
Champion HR, Sacco WJ, Copes WS, Gann DS, Gennarelli TA, Flanagan ME. A revision of the trauma score. J Trauma Acute Care Surg 1989;29:623-9.
Sartorius D, Le Manach Y, David JS, Rancurel E, Smail N, Thicoïpé M, et al.
Mechanism, Glasgow Coma Scale, Age, and Arterial Pressure (MGAP): A new simple prehospital triage score to predict mortality in trauma patients. Crit Care Med 2010;38:831-7.
Moore L, Lavoie A, Abdous B, Le Sage N, Liberman M, Bergeron E, et al.
Unification of the revised trauma score. J Trauma 2006;61:718-22.
Lovett PB, Buchwald JM, Stürmann K, Bijur P. The vexatious vital: Neither clinical measurements by nurses nor an electronic monitor provides accurate measurements of respiratory rate in triage. Ann Emerg Med 2005;45:68-76.
Raux M, Thicoïpé M, Wiel E, Rancurel E, Savary D, David JS, et al.
Comparison of respiratory rate and peripheral oxygen saturation to assess severity in trauma patients. Intensive Care Med 2006;32:405-12.
Trickey AW, Fox EE, del Junco DJ, Ning J, Holcomb JB, Brasel KJ, et al.
The impact of missing trauma data on predicting massive transfusion. J Trauma Acute Care Surg 2013;75:S68-74.
Kimura A, Chadbunchachai W, Nakahara S. Modification of the Trauma and Injury Severity Score (TRISS) method provides better survival prediction in Asian blunt trauma victims. World J Surg 2012;36:813-8.
Khajanchi MU, Kumar V, Gerdin M, Roy N. Indians fit the Asian trauma model. World J Surg 2013;37:705-6.
Knudson P, Frecceri CA, DeLateur SA. Improving the field triage of major trauma victims. J Trauma 1988;28:602-6.
Kennedy RL, Grant PT, Blackwell D. Low-impact falls: Demands on a system of trauma management, prediction of outcome, and influence of comorbidities. J Trauma 2001;51:717-24.
Knopp R, Yanagi A, Kallsen G, Geide A, Doehring L. Mechanism of injury and anatomic injury as criteria for prehospital trauma triage. Ann Emerg Med 1988;17:895-902.
Andrews PJ, Sleeman DH, Statham PF, McQuatt A, Corruble V, Jones PA, et al.
Predicting recovery in patients suffering from traumatic brain injury by using admission variables and physiological data: A comparison between decision tree analysis and logistic regression. J Neurosurg 2002;97:326-36.
Lehr D, Baethmann A, Reulen HJ, Steiger HJ, Lackner C, Stummer W, et al.
Management of patients with severe head injury in the preclinical phase: A prospective analysis. J Trauma 1997;42:S71-5.
Kehoe A, Rennie S, Smith JE. Glasgow coma scale is unreliable for the prediction of severe head injury in elderly trauma patients. Emerg Med J 2015;32:613-5.
Gorji MA, Hoseini SH, Gholipur A, Mohammadpur RA. A comparison of the diagnostic power of the full outline of unresponsiveness scale and the Glasgow Coma Scale in the discharge outcome prediction of patients with traumatic brain injury admitted to the Intensive Care Unit. Saudi J Anaesth 2014;8:193-7.
] [Full text]
Fakharian E, Alavi NM. Outcome of factors related to traumatic brain injuries among the patients hospitalized in Intensive Care Unit. Feyz J Kashan Univ Med Sci 2010;14:112-9.
Gerdin M, Roy N, Khajanchi M, Kumar V, Dharap S, Felländer-Tsai L, et al.
Correction: Predicting early mortality in adult trauma patients admitted to three public university hospitals in urban India: A prospective multicentre cohort study. PLoS One 2015;10:e0144886.
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5]