|Year : 2019 | Volume
| Issue : 2 | Page : 104-109
Prediction of mortality risk in patients with traffic injury: A case study in tabriz hospitals, Iran
Yalda Mousazadeh1, Homayoun Sadeghi-Bazargani2, Ali Janati3, Mahboub Pouraghaei4, Farzad Rahmani2
1 Department of Health Services Management, Faculty of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz, Iran
2 Road Traffic Injury Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
3 Iranian Center of Excellence in Health Management, School of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz, Iran
4 Emergency Medicine Research Team, Tabriz University of Medical Sciences, Tabriz, Iran
|Date of Submission||27-Jan-2019|
|Date of Acceptance||29-Jul-2019|
|Date of Web Publication||07-Oct-2019|
Dr. Ali Janati
Iranian Center of Excellence in Health Management, School of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz
Source of Support: None, Conflict of Interest: None
Background: Trauma caused by traffic accidents is one of the main causes of mortality in the world. The trauma is a time-dependent condition. Trauma scoring systems help service providers to determine the severity of the injury and mortality risk and provide appropriate and timely services. This study aimed to predict mortality risk in patients with traffic injuries in Tabriz hospitals. Materials and Methods: This descriptive cross-sectional study included 11,238 traffic-injured patients. Databases of the emergency medicine service and forensic medicine were used to collect information. Glasgow Coma Scale, Age, and Systolic Blood Pressure (GAP) scoring system, binary logistic regression model, odds ratio with 95% confidence interval, and sensitivity and specificity were used to predict mortality. Results: Overall, 71 cases of death were identified. Pedestrians had the most rates of deaths with 31 cases (43.66%). Head trauma with 31 cases (43.66%) was the main cause of death. Mean (standard deviation) of the GAP score was 21.8 (1.8). The death rates in high-, moderate-, and low-risk groups were 22.4%, 3.18%, and 0.42%, respectively. The likelihood of death in people with saturation of oxygen (SO2) ≤95 was 1.96 times higher than those with SO2>95. The likelihood of death increased by 0.001 times each year. Furthermore, when the GAP score was ≤18, sensitivity was 64%, and for the GAP score of ≤10, sensitivity was 15.5%. Conclusion: GAP score seems to be a reliable and easy-to-use scoring method for predicting traffic injury mortality in an Iranian setting and yields reasonable results concerning international standards.
Keywords: Hospital, injury severity, mortality, traffic accident, trauma
|How to cite this article:|
Mousazadeh Y, Sadeghi-Bazargani H, Janati A, Pouraghaei M, Rahmani F. Prediction of mortality risk in patients with traffic injury: A case study in tabriz hospitals, Iran. Arch Trauma Res 2019;8:104-9
|How to cite this URL:|
Mousazadeh Y, Sadeghi-Bazargani H, Janati A, Pouraghaei M, Rahmani F. Prediction of mortality risk in patients with traffic injury: A case study in tabriz hospitals, Iran. Arch Trauma Res [serial online] 2019 [cited 2020 Aug 9];8:104-9. Available from: http://www.archtrauma.com/text.asp?2019/8/2/104/268661
| Introduction|| |
World Health Organization (WHO) defines trauma as a severe strike of the body with physical factors such as mechanical energy, heat, electricity, chemical substances, and ionization rays. Trauma is the main cause of mortality and morbidity among the young in the first four decades of life., More than 5 million die out of injuries annually. Injuries affect 700 million people all over the world. Since multiple injuries are associated with death and disability, major trauma patients undergo serious problems. Indeed, trauma is one of the major reasons for the burden of diseases worldwide. Trauma is a more critical and thoughtful problem in developing countries. The issue might be due to the lack of an organized trauma system and the extent of occasions leading to trauma, for example, traffic accidents.
Trauma caused by traffic accidents is threatening the life of all age groups. It is claimed that annually, traffic accidents result in the death of 21 million people and the disability of 20–50 million people throughout the world. Unfortunately, 60% of whom are young aged 14–45., According to the WHO report, traffic injuries have risen from around 999,000 in 1990 to more than a million deaths in 2002; and it is anticipated to reach 2 million deaths/year in 2020. In comparison with developing countries, traffic injury is the second leading cause of mortality in Iran; 38% of abnormal deaths are due to road accidents in Iran. The statistics released by Iran's forensic medicine showed that in a period of 10 years (2000–2010), 235,587 people were killed because of road accidents, and 2,281,810 people were injured. Traffic accidents also affect costs, and injuries caused by it decrease annual Gross Domestic Product (GDP) by 1%–15% in developing countries.
Trauma is a time-dependent condition, and time is a very vital factor, especially during the 1st h of trauma occurrence. Providing earlier care at trauma centers has been shown to decrease mortality., At the beginning of the treatment, trauma scores can help emergency care providers with recognizing the severity of trauma and determining the operation on trauma patients. Many trauma scoring systems (TSSs) have been used so far. The Injury Severity Score (ISS) and the Trauma and ISS (TRISS) are widely accepted TSSs whose calculation requires that all examinations and checkups be performed and that injuries in anatomic locations be noted with detail. The Revised Trauma Score (RTS) is another scoring system, which is widely used. However, it seems that determining an appropriate RTS is very complicated and hard. On the other hand, respiratory rate (RR), a calculation factor of RTS, is less reliable than other factors because it is influenced by patients' condition such as age, mechanism of injury, and mechanical ventilation.
Kondo et al. defined the Glasgow Coma Scale (GCS), Age, and Systolic Blood Pressure (SBP) (GAP) score that is a physiological TSS. GAP has fewer parameters. It is similar to TRISS in terms of mortality prediction. The GAP score is easily calculable both in prehospital and hospital admission to the emergency department (ED). The GAP score is based on 27,154 patients from the Japan Trauma Data Bank (2004–2009). Hence, it is a valid scoring system. This study aimed to predict the mortality risk in patients with traffic injury admitted to Tabriz hospitals using the GAP scoring method.
| Materials and Methods|| |
Study design, the research community, and data collection
This cross-sectional descriptive study was conducted from September 2016 to February 2018. The study population consisted of all traffic accident victims during the mentioned period, which was transferred to Tabriz hospitals by emergency medicine service (EMS) (including 90% EMS missions). Patients transmitted by other vehicles were not included in the study.
The required information was extracted from the EMS database. The recorded data in this system included name of the patient, age, gender, mission date, key times, saturation of oxygen (SO2), SBP and diastolic blood pressure (DBP), GCS, the outcome of the mission, and name of the hospital where the patient was transferred, the location and result of the mission, and the mission ID. Furthermore, all the data regarding the dead were extracted from the East Azerbaijan Forensic Medicine Organization database (EAFMOD) and integrated with EMS information. The information extracted from the forensic database included names of the dead, type of the vehicle of the injured and the vehicle involved in the accident, the dead's conditions at the time of the accident, location of the death, part of the body that has been hit, the mechanism of damage, and the final cause of death.
The main outcome of this study was death, which was estimated through the GAP scoring system. Other intervening variables of death were studied through a regression model.
Glasgow Coma Scale, Age, and Systolic Blood Pressure-based scoring
All the individuals under the age of 1–103 were investigated, whose items of GAP measurements were recorded. The GAP scoring system was defined by Kondo et al. on 27,154 patients. Based on the GAP scoring system, the predictive variables of hospital mortality were included, which are as follows:
- GCS: 3–15
- Age: if age was <60 years old, score 3 is considered. Furthermore, if age was ≥60 years old, a score of 0 is considered
- SBP: if SBP was ≥120, 60–120, and <60, then scores of 6, 4, and 0 are considered, respectively.
Classification of the patients into different risk groups was based on the score obtained for GAP. The GAP scoring is as follows:
- The GAP score between 3 and 10 is considered as a high-risk group
- The GAP score between 11 and 18 is considered as a moderate-risk group
- The GAP score between 19 and 24 is considered as a low-risk group.
In this study, GAP score was between 3 and 24.
The data were analyzed using the STATA 13 statistical software package (StataCorp, Texas, USA). Descriptive statistics including the number, frequency, minimum and maximum, mean, and standard deviation (SD) were reported for the demographic variables recorded by EMS and EAFMOD. The GAP score was calculated based on age, SBP, and GCS. The mortality rate was calculated in three risk groups, that is, high-risk, moderate-risk, and low-risk group. The binary logistic regression model and odds ratio with 95% confidence intervals for variables were used to investigate the relationship among other variables intervening prediction of patient mortality. The relationship between death and GAP classification group was presented based on the Chi-square test. The sensitivity and specificity reports were used to determine the predictive power of GAP, and SO2 scores were also used to predict death. In addition, the radar chart was designed to express the most important causes of mortalities using Microsoft Excel 2017 software. The significance level was considered to be <0.05.
This study was part of Ph.D. thesis. The main protocol of this study was reviewed and approved by the Ethics Committee of Tabriz University of Medical Sciences. Its approval code was IR.TBZNED.REC.1396.560.
| Results|| |
From a total of 11,238 traffic accident victims in this study, 7816 (69.55%) were male. Regarding age distribution, the mean (SD) of the injured was 34.3 (16.2) years old. Of these, 71 people were identified as dead. The mean (SD) of SBP was 113.1 (24.8) mmHg, and mean (SD) DBP was 72.2 (17) mmHg. Mean (SD) SO2 was 83.93 mmHg (12.5). Mean (SD) of GCS was calculated to be 14.8 (0.9). In [Table 1], the demographic characteristics of the dead and the survived are presented regarding their gender.
|Table 1: Demographic characteristics of the dead and alive patients based on their gender|
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Of the 71 died individuals, 29 cases were drivers, 31 were pedestrians, and 11 were pillion passengers. The involved vehicles in accidents and the crash mechanisms are presented in [Table 2].
|Table 2: Used vehicles involved in traffic accidents and crash mechanism|
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Injured organs of the body are classified into seven groups. The most affected organs among victims were head and face (74.6%) and the least were arms and hands (4.2%). The frequency rates and percentages of other injured organs are presented in [Table 3]. Furthermore, the most cause of mortalities is presented in [Figure 1].
|Table 3: The injured parts of the body among the traffic accident victims|
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Regarding the Chi-square test, the relationship between death and GAP classification group was statistically significant (P< 0.000). A total number of 11,167 individuals (99.37%) survived the accidents while 71 (0.63%) died. The number of individuals in the three categories of GAP classification and their GAP score along with the percentage of death are presented in [Table 4].
|Table 4: The number of individuals in different categories of Glasgow Coma Scale, Age, and Systolic Blood Pressure scores and percentage of death|
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According to the logistic regression model, the likelihood of death in the high-risk and moderate-risk groups in comparison with the low-risk group was 39.39 and 6.5 times more, respectively. The likelihood of death in the patients with SO2 below 95 was about 1.96 times higher than those with SO2 above 95. Moreover, the likelihood of death increases by 0.001 times with 1 year increase in age [Table 5].
|Table 5: The affective variables on the probability of death based on logistic regression model|
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Based on the sensitivity and specificity test, when the scores were GAP ≤18 and GAP ≤10, the death likelihoods were 64% and 15.5%, respectively. Furthermore, after adding SO2<95 to GAP, the likelihoods of death were predicted 28.2% and 14.8%, respectively. Prediction power of GAP and SO2 and sensitivity, specificity, and positive and negative predictive values are presented in [Table 6].
|Table 6: Sensitivity, specificity, positive and negative predictive values of Glasgow Coma Scale, Age, and Systolic Blood Pressure, and saturation of oxygen in mortality prediction|
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| Discussion|| |
The results of this study showed that most of the patients with traffic injuries were male. Regarding age distribution, the most patients were young. According to crash-related studies, the accident rate is 3–5 times more among men than women. Furthermore, its highest rates are between the ages of 15 and 44 years. This issue imposes a large economic burden on the community.
According to the findings of the study, the number of the dead and the survived men was more than the women. Furthermore, the mean of SBP, DBP, SO2, and GCS score were worse among the dead. Based on the study of Ahun et al. on the prediction of mortality in the ED, the highest number of injured individuals were male, both the dead and survived. Furthermore, SBP was 120 mmHg versus 115 mmHg, DBP was 80 mmHg versus 70 mmHg, SO2 was 97.5 mmHg versus 92 mmHg, and GCS was 15 versus 13 between the survived and dead. Furthermore, according to Esmaeili et al. study on the survival and quality of service provided for patients with traffic injures, GCS and SBP were statistically significant in both groups of the dead and survived (P< 0.001). These results are consistent with the findings of the present study.
In this study, the highest number of the dead belonged to pedestrians. In line with the present study and by the cause of trauma and road users in Iran, the classification of the patients injured in road traffic accidents showed that pedestrians with 39.8% were the most injured individuals. Motorcyclists with 33.1%, occupants of the car with 24.3%, bus and minibus occupants with 1.6%, and truck occupants with 1.2% were the other most injured patients. In some other studies, the findings were opposite the findings of this study. In Soltani et al.'s study, unlike the present study, the highest rate of death in traffic accidents in Yazd was for motorcyclists with 34 cases, none of whom had helmets.
Head trauma was the most frequent injured part of the body and cause of death. Based on the study by Ghafari Fam et al. on pedestrians referred to Shohada Hospital of Tabriz, the most frequent area of injury was lower limbs with 43.5%. Although the findings of this study are different from the findings of the present study, this may be due to the examination of only one hospital and its specialty, which was orthopedic. According to Taghipour et al., the anatomy of the individuals injured in driving accidents showed that head injury with a frequency of 220 and face injuries with the frequency of 169 cases were the most damaged areas. Whereas, traumatic brain injury with about 60% of occurrence was the most prevalent cause of death, which is consistent with the findings of the present study.
The findings of GAP scoring in this study were consistent with the findings of Kondo et al. study, in which the mortality rate in the low-risk individuals was <5%, and in the moderate- and high-risk groups, it was ≥50%. In Kondo et al.'s study, it is indicated that GAP is a better predictor, and it is more commonly used and predicts injury severity better than other injury grading systems. On the other hand, it is easier to use. In fact, in the early predictions of the severity of the injury, there is a need for indicators that can be easily calculated. These include indicators such as GCS, blood pressure, heart rate, and RR. Perel et al. in their study on patients with hemorrhagic trauma found that variables such as age, blood pressure, and GCS were associated with the early prediction of mortality in patients.
In the present study, based on the binary logistic regression model, the likelihood of death in high-risk and moderate-risk group was much higher than that of the low-risk group. Two variables of SO2 and age were related to likelihood of death. In the study of Soltani et al., a binary logistic regression model was used to investigate the various factors involved in traffic accident injuries. The results showed that gender, the type of vehicle collision, and the time of the accident were effective factors that should be considered. Weather conditions, laws, and culture were among these variables.
| Conclusion|| |
It seems that the use of the GAP grading system in predicting mortality risk can be useful due to the items used in GAP and simplicity of calculation and yields reasonable results concerning international standards. In general, in developing countries such as Iran, which design registries of injuries at an early stage, the use of scoring systems such as GAP is useful and constructive for examining the current status.
We would like to thank the Research deputy of Tabriz University of Medical Sciences for their financial support and the organization of emergency and forensic medicine in East Azarbaijan, the Road Traffic Injury Prevention Research Center of Tabriz University of Medical Sciences, and Dr. Meshayneh Haddadi and Mr. Milad Jamali for their general and technical contribution.
Financial support and sponsorship
This study was financially supported by Tabriz University of Medical Sciences, Tabriz, Iran.
Conflicts of interest
There are no conflicts of interest.
| References|| |
Baker SP, O'Neill B, O'Neill M, Li G. The Injury Fact Book. 2nd
ed. New York: Oxford University Press; 1992.
Moini M, Rezaishiraz H, Zarineh A, Rasouli MR. Evaluation of quality of trauma care in a local hospital using a customization of ASCOT. Eur J Trauma Emerg Surg 2009;35:56-60.
Zafarghandi MR, Modaghegh MH, Roudsari BS. Preventable trauma death in Tehran: An estimate of trauma care quality in teaching hospitals. J Trauma 2003;55:459-65.
Latifi R, Ziemba M, Leppäniemi A, Dasho E, Dogjani A, Shatri Z, et al.
Trauma system evaluation in developing countries: Applicability of American College of Surgeons/Committee on Trauma (ACS/COT) basic criteria. World J Surg 2014;38:1898-904.
Stelfox HT, Bobranska-Artiuch B, Nathens A, Straus SE. Quality indicators for evaluating trauma care: A scoping review. Arch Surg 2010;145:286-95.
Glen J, Constanti M, Brohi K; Guideline Development Group. Assessment and initial management of major trauma: Summary of NICE guidance. BMJ 2016;353:i3051.
Moore L, Lavoie A, Sirois MJ, Amini R, Belcaïd A, Sampalis JS. Evaluating trauma center process performance in an integrated trauma system with registry data. J Emerg Trauma Shock 2013;6:95-105.
] [Full text]
Esmaeili ED, Sadeghi-Bazargani H, Vahdati SS, Shokouhi G, Safaiyan A, Lak SS. Assessment of survival and hospital care quality in patients with traffic injury in East Azerbaijan. Trauma Mon 2017;2;e35303.
Ziyab AH, Akhtar S. Incidence and trend of road traffic injuries and related deaths in Kuwait: 2000-2009. Injury 2012;43:2018-22.
Sánchez González MP, Escribano Sotos F, Tejada Ponce Á. Impact of provincial characteristics on the number of traffic accident victims on interurban roads in Spain. Accid Anal Prev 2018;118:178-89.
Nantulya VM, Reich MR. The neglected epidemic: Road traffic injuries in developing countries. BMJ 2002;324:1139-41.
Rasouli MR, Nouri M, Zarei MR, Saadat S, Rahimi-Movaghar V. Comparison of road traffic fatalities and injuries in Iran with other countries. Chin J Traumatol 2008;11:131-4.
Moafian G, Aghabeigi MR, Heydari ST, Hoseinzadeh A, Lankarani KB, Sarikhani Y, et al.
An epidemiologic survey of road traffic accidents in Iran: Analysis of driver-related factors. Chin J Traumatol 2013;16:140-4.
Peden M, Scurfield R, Sleet D, Mohan D, Hyder AA, Jarawan E, et al
. World Report on Road Traffic Injury Prevention. Geneva: World Health Organization; 2004.
Nirula R, Maier R, Moore E, Sperry J, Gentilello L. Scoop and run to the trauma center or stay and play at the local hospital: Hospital transfer's effect on mortality. J Trauma 2010;69:595-9.
MacKenzie EJ, Rivara FP, Jurkovich GJ, Nathens AB, Frey KP, Egleston BL, et al.
Anational evaluation of the effect of trauma-center care on mortality. N
Engl J Med 2006;354:366-78.
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.
Chawda MN, Hildebrand F, Pape HC, Giannoudis PV. Predicting outcome after multiple trauma: Which scoring system? Injury 2004;35:347-58.
Champion HR, Sacco WJ, Copes WS, Gann DS, Gennarelli TA, Flanagan ME. A revision of the trauma score. J Trauma 1989;29:623-9.
Kondo Y, Abe T, Kohshi K, Tokuda Y, Cook EF, Kukita I. 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.
Ahun E, Köksal Ö, Sıǧırlı D, Torun G, Dönmez SS, Armaǧan E. Value of the glasgow coma scale, age, and arterial blood pressure score for predicting the mortality of major trauma patients presenting to the emergency department. Ulus Travma Acil Cerrahi Derg 2014;20:241-7.
Yazdani Charati J, Abbasi A, Fenderski A, Ali Pour N. Epidemiology of fatal traffic accidents in Sari, Iran in 2014. J Health Res Commumity 2016;2:61-8.
Ghafari Fam S, Sadeghi Bazargani H, Salarilak S. A survey on epidemiological and demographic aspects of pedestrian road accident victims in Shohada referral university hospital, East Azerbaijan province. J Urmia Uni Med Sci 2014;25:549-60.
Soltani G, Ahmadi B, Pourreza A, Rahimi A. Investigating prevalence of deaths from traffic accidents and factors associated with it in Yazd in 2009. J Shahid Sadoughi Univ Med Sci 2014;21:831-9.
Taghipour H, Panahi F, Khoshmohabat H, Firoozabadi NH, Moharamzad Y, Abbasi A. Causes and severity of fatal injuries in autopsies of victims of fatal traffic accidents. J Shahid Sadoughi Univ Med Sci 2010;17:358-64.
Rahmani F, Bakhtavar HE, Vahdati SS, Hosseini M, Esfanjani RM. Evaluation of MGAP and GAP trauma scores to predict prognosis of multiple-trauma patients. Trauma Mon 2017;22:e33249.
Perel P, Prieto-Merino D, Shakur H, Clayton T, Lecky F, Bouamra O, et al.
Predicting early death in patients with traumatic bleeding: Development and validation of prognostic model. BMJ 2012;345:e5166.
Sadeghian F, Khosravi A, Emamian MH, Yonesian R. The pattern of road traffic injuries and related factors in Shahrood, Iran. Payesh 2008;7:225-33.
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6]