Archives of Academic Emergency Medicine. 2022; 10(1): e64 OR I G I N A L RE S E A RC H Predictive Factors of Outcome in Cases of Out-of-hospital Cardiac Arrest Due to Traffic Accident Injuries in Thailand; a National Database Study Thongpitak Huabbangyang1, Chunlanee Sangketchon1∗, Sakditat Ittiphisit2, Kanittha Uoun3, Chomkamol Saumok3 1. Department of Disaster and Emergency Medical Operation, Faculty of Science and Health Technology, Navamindradhiraj University, Bangkok, Thailand. 2. Faculty of Medicine, Vajira Hospital, Navamindradhiraj University, Bangkok, Thailand. 3. Division of Emergency Medical Service and Disaster, Faculty of Medicine, Vajira Hospital, Navamindradhiraj University, Bangkok, Thailand. Received: June 2022; Accepted: July 2022; Published online: 16 August 2022 Abstract: Introduction: Traffic accident injury is one of the global leading causes of death and an important public health problem. This study aimed to evaluate the predictive factors of return of spontaneous circulation (ROSC) at the scene in out-of-hospital cardiac arrest (OHCA) due to traffic accidents. Methods: This retrospective cross- sectional study was conducted on cases of OHCA due to traffic accident, who were resuscitated at the scene by emergency medical services (EMS) in Bankok, Thiland, from January 1, 2020, to December 31, 2020 (1 year). Patients were divided into two groups of with and without ROSC and independent predictive factors of outcome were evaluated. Results: 2400 OHCA cases met the inclusion criteria, among them, 1728 (72.0%) achieved ROSC at the scene. Facial injury (adjusted OR = 2.17, 95%CI: 1.37–3.44, p = 0.001); prehospital airway management using bag valve mask (adjusted OR = 1.69, 95%CI: 1.21–2.34, p = 0.002), and endotracheal tube (adjusted OR = 3.88, 95%CI: 1.84–8.18, p <0.001); and prehospital fluid therapy using normal saline (adjusted OR = 4.24, 95%CI: 3.12–5.77, p <0.001), ringer lactate (adjusted OR = 5.13, 95%CI: 3.47–7.61, p <0.001), and other solutions (adjusted OR = 5.25, 95%CI: 2.16–12.8, p <0.001) were independent predictive factors of ROSC at the scene in OHCA due to traffic accidents. Conclusion: Based on the findings, the rate of ROSC at the scene for cases with OHCA due to traffic accidents, serviced by EMS was high, i.e., 72%, and three independent predictive factors of ROSC at the scene were facial injury, prehospital airway management, and prehospital fluid management. Keywords: Prognosis; emergency medical services; heart arrest; patient outcome assessment; mortality; accidents, traffic Cite this article as: Huabbangyang T, Sangketchon C, Ittiphisit S, Uoun K, Saumok C. Predictive Factors of Outcome in Cases of Out-of- hospital Cardiac Arrest Due to Traffic Accident Injuries in Thailand; a National Database Study. Arch Acad Emerg Med. 2022; 10(1): e64. https://doi.org/10.22037/aaem.v10i1.1700. 1. Introduction The World Health Organization reported the annual average number of deaths due to traffic accidents as 1.35 million. The majority of deaths occurred in vulnerable populations, in- cluding pedestrians and motorcyclists. About 93% of deaths occurred in low-to-middle income countries, Traffic accident ∗Corresponding Author: Chunlanee Sangketchon; Department of Disaster and Emergency Medical Operation, Faculty of Science and Health Technol- ogy, Navamindradhiraj University, Bangkok, Thailand. Postal Code: 10300 Tel: +66 22443000, E-mail: chunlanee@nmu.ac.th, ORCID: https://orcid.org/0000- 0003-4509-1527. injury was the leading cause of death in those aged 5–29 years (1). Thai road safety collaboration reported the cumulative number of injuries to be 417,935 in 2021 from January to May, and the number of deaths from traffic accidents was 6,513. Every hour, two died due to traffic accidents in Thailand (2). Two-thirds of road accident victims in Thailand were male (80%) and aged <40 years. About 80% of the injured and de- ceased were motorcyclists. Traffic accidents were an important public health problem leading to injuries, disabilities, and mortalities in Thailand, a developing country, causing vast effects on individuals, fami- lies, societies, and the nation as a whole (3). Thailand had the highest rate of road accident mortality in South East Asia, i.e., This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem T. Huabbangyang et al. 2 32.7 per 100,000 population (4). Emergency medical services (EMS) were used as a tool to address this issue and focused on prehospital management of injured patients, appending two important concepts, the golden period or golden hour and the platinum 10 minutes. Regarding the golden period, the first 60 minutes for injured patients, starting from acci- dent, is a period with significantly increased morbidity and mortality. The platinum 10 minutes within the golden hour means that paramedics have at most 10 minutes at the scene to manage severely injured patients for survival rate improve- ment (5). Bystander cardiopulmonary resuscitation (CPR) increased the survival rate of injured patients with cardiac arrest from an accident, as indicated in previous studies (6, 7). For initial cardiac rhythm, shockable rhythm resulted in survival im- provement (8-10). Pediatric patients had a higher survival rate by receiving CPR for out-of-hospital cardiopulmonary arrest (OHCA) due to accidents, as compared to adult pa- tients with a survival rate of approximately 17.8% (95% CI; 15.1–20.8%). Geographical areas where accidents happened between the city and countryside were associated with inpatient survival and hospital discharge (9, 10). Thailand is 513,120 square kilometers (198,120 square miles), divided into 77 provinces. Its mean population was 66.19 million in December 2020. In the 2021 statistical report, 854,118 traffic accidents and 13,235 traffic accident mortalities occurred, which showed that the country had the highest number of injured and de- ceased patients due to traffic accidents worldwide (11). Ev- ery traffic accident led to loss, including deaths, disabilities, and inevitable traffic congestion, an obstacle for ambulances and EMS teams to access the scene. A traffic accident was one of the three most common events managed by the Thai emergency medical units. Out-of-hospital EMS was initi- ated when Emergency Medical Act B.E.2551 was declared. The National Institute for Emergency Medicine was the back- bone supporting emergency medical missions in Thailand. In Thailand, the emergency hotline is 1669. The provincial dispatch center manages each province, except for Bangkok where the Erawan Center and Bangkok EMS center are lo- cated. The present study aimed to evaluate prognostic fac- tors associated with the return of spontaneous circulation (ROSC) at the scene and determine the rate of ROSC at the scene for those injured with OHCA due to traffic accidents, serviced by EMS. 2. Methods 2.1. Study design and setting This retrospective cross-sectional study was conducted on cases of OHCA due to traffic accident, who were resuscitated at the scene by EMS in Bankok, Thiland, from January 1, 2020, to December 31, 2020 (1 year). Patients were divided into two groups of with and without ROSC and independent predic- tive factors of outcome were evaluated. The ethical approval of the study protocol was granted by The Committee on Hu- man Rights Related to Research Involving Human Subjects, Faculty of Medicine, Vajira Hospital, Navamindradhiraj Uni- versity (COA. 168/2564). The requirement for informed con- sent was waived due to the retrospective design. The study data were kept confidential to ensure the privacy of the stud- ied participants. This study was conducted in accordance with the Declaration of Helsinki. 2.2. Participants Traffic accident patients aged >18 years, coded as 25 Red 1 symptom group based on emergency medical triage proto- col and criteria-based dispatch (CBD) of Thailand, defined as a traffic accident with cardiac arrest, who received advanced cardiovascular life support from the Advanced Life Support (ALS) team were eligible for study. Traffic accident patients serviced by EMS in Bangkok who died at the scene (death on arrival), those who were evalu- ated as deceased when EMS arrived, those who were deemed unsuitable for CPR by the ALS team leader, patients whose relatives denied treatment and transportation, those receiv- ing CPR during transfer, patients with discordant CBD coding to the actual incident, and those with incomplete data were excluded. ROSC at the scene was defined as the presence of palpable pulse after CPR at the scene of trauma. 2.3. Data gathering Data were collected from the National Institute for Emer- gency Medicine database, Thailand, which gathered data on traffic accidents with symptom group 25 based on the Thai- land emergency medical triage protocol and criteria-based dispatch (CBD), from the database of Information Technol- ogy for Emergency Medical System (ITEMS). Only symptom group 25 Red 1, i.e., traffic accident patients with cardiac ar- rest, was selected for this study. The ITEMS database of the National Institute for Emergency Medicine is the national database recording Thai EMS operation data including pa- tient management at the scene and during transportation. The EMS team recorded data in the patient record form and then transferred them into the ITEMS database within that day or month. Only authorized people could record and go over the data, including paramedics, emergency nurse practitioners (ENPs), or advanced emergency medical tech- nicians (AEMTs). The study included the injured and emergency operation unit’s data, such as sex, age, operation time, type of wounds, type of orthopedic injuries, type of hemorrhage, type of body part injured, the response time (minute), duration of trans- fer to the hospital (minute), duration from hospital to the This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem 3 Archives of Academic Emergency Medicine. 2022; 10(1): e64 base station (minute), distance from the base station to scene (kilometer), distance from the scene to the hospital (kilo- meter), distance from hospital to the base station (kilome- ter), prehospital airway management, prehospital hemor- rhage control, prehospital airway management, prehospital fluid management, prehospital immobilization, and prehos- pital automated external defibrillator (AED) /defibrillation, medication during CPR, and ROSC on the scene. The concept of emergency medicine in Thailand appends the Anglo-American and Franco-German models. In Thai- land, the out-of-hospital emergency staff includes emer- gency physicians (EPs), paramedics, ENPs, AEMTs, emer- gency medical technicians (EMTs), and emergency medical responders, which operate under an emergency medical di- rector, which can be divided into off-line and online medical directions. In general, the staff operates under a predeter- mined off-line medical direction, as a protocol created by the emergency medical director, which is different in each oper- ation unit or hospital, depending on administrative potential and existent resources. There are two tiers of ambulances in Thailand, i.e., advanced life support (ALS) and basic life support. Regarding emer- gency medical operations for traffic accident patients with cardiac arrest, most EMS teams in the study area consisted of at least three staff members during each operation, including EPs, paramedics, or ENPs as operation leaders and AEMTs and EMTs as members. 2.4. Outcome measures The primary objective was to evaluate predictive factors of ROSC at the scene in cases with OHCA due to traffic acci- dents, who were serviced by EMS. The secondary objective was to determine the ROSC rate of the injured with OHCA due to traffic accidents at the scene. 2.5. Statistical analysis Sample size was calculated using two independent propor- tions (12). The statistical significance level of 0.05 and test power of 80% were considered. Statistical values for sample size were calculated based on Jun GS et al. (9) study that re- ported survival rates of male and female patients with both OHCA and ROSC on the scene to be 0.3332% (1988/5966; p1= 0.3332) and 27.08% (615/2271; p2= 0.2708), respectively. The sample size ratio of females to males was 0.38 (2271:5966). Therefore, calculated female and male sample sizes were at least 1,524 and 580, consecutively. Hence, a sample size of 2,104 was determined. However, due to the retrospective design of the study, collecting data from medical records, the sample size was increased to compensate for incomplete data of 10% (13). The final sample size was 2,400. A descriptive analysis was performed to examine variable distribution. Continuous variables are presented as mean ± standard deviation (SD) or median and interquartile range (IQR), and categorical variables are presented as frequen- cies and proportions. When comparing the two groups, dif- ferences were evaluated using independent t-test or Mann- Whitney U test for numeric variables and chi-square test or Fisher’s exact test for categorical variables. The survival rate of the injured patients with OHCA at the scene who were serviced by EMS was reported as frequency distribution and percentage (incidence) with a 95% confi- dence interval (CI). Predictive factors associated with ROSC at the scene for the injured with OHCA due to traffic ac- cidents serviced by EMS were reported as frequency distri- bution and percentage, categorized based on survival, as- sessed with crude analysis using either the Chi-squared test or Fisher’s exact test based on the type of variable. Multivari- able analysis using multiple logistic regression analysis was reported as odds ratio (OR) and 95% CI. IBM SPSS Statistics for Windows, Version 26.0. Armonk, NY, USA: IBM Corp. was used. All statistical tests were considered statistically signifi- cant at a p-value of ≤0.05. 3. Results 3.1. Baseline characteristics of studied cases 2400 cases of OHCA with ROSC at the scene with the mean age of 40.07 ± 18.47 years, who were resuscitated by EMS during the study period were studied. The ROSC rate at the scene was 72.0% (1728 patients). There was no difference between survived and deceased cases regarding sex distribu- tion (78.9% vs. 78% male; p = 0.629) and mean age (40.07 ± 18.47 vs. 40.05 ± 18.85 years, p = 0.985). Table 1 compares the baseline characteristics between patients with and without ROSC. 3.2. Associated factors of ROSC at the scene Based on univariate analysis, associated factors of ROSC at the scene for cases of OHCA due to traffic accidents were fa- cial injury (OR = 2.08, 95%CI: 1.32–3.26, p = 0.001); prehos- pital airway management using bag valve mask (BVM) (OR = 2.4–4, 95%CI: 1.80–3.30, p <0.001), endotracheal tube (ETT) (OR = 5.79, 95%CI: 2.80–11.94, p <0.001), or supra-glottic air- way devices (OR = 2.17, 95%CI: 1.23–3.81, p = 0.007); pre- hospital fluid management with normal saline (NSS) (OR = 4.72, 95%CI: 3.51–6.34, p <0.001), lactated Ringer’s solution (RLS) (OR = 5.71, 95%CI: 3.89–8.37, p <0.001), and other so- lutions (OR = 5.71, 95%CI: 2.36–13.79, p <0.001); prehospital immobilization including splint (OR = 2.78, 95%CI: 1.59–4.88, p <0.001) and collar with long spinal board (OR = 2.59, 95%CI: 1.92–3.51, p <0.001); and medication during the CPR process (OR = 1.47, 95%CI: 1.14–1.90, p = 0.003) (Table 2). Multivariable analysis using multiple logistic regression anal- ysis and backward stepwise selection method, showed that This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem T. Huabbangyang et al. 4 Table 1: Comparing the baseline characteristics between patients with and without return of spontaneous circulation (ROSC) Variables ROSC on scene (n = 2400) P value Yes (n=1728) No (n = 672) Sex Male 1363 (78.9) 524 (78.0) 0.629 Female 365 (21.1) 148 (22.0) Age (year) 0–19 260 (15.0) 101 (15.0) 20–39 624 (36.1) 243 (36.2) 0.999 40–59 536 (31.0) 207 (30.8) >60 308 (17.8) 121 (18.0) Shift Morning (8.00–15.59) 501 (29.0) 188 (28.0) Evening (16.00–23.59) 910 (52.7) 353 (52.5) 0.772 Night (0.00–7.59) 317 (18.3) 131 (19.5) Type of wounds None 73 (4.2) 29 (4.3) Cut/laceration 1213 (70.2) 473 (70.4) Abrasion 271 (15.7) 93 (13.8) 0.690 Other 70 (4.1) 32 (4.8) Unknown 101 (5.8) 45 (6.7) Type of orthopedic injuries None 501 (29.0) 174 (25.9) Closed fracture 542 (31.4) 217 (32.3) Open fracture 446 (25.8) 184 (27.4) 0.668 Dislocation 40 (2.3) 16 (2.4) Unknown 199 (11.5) 81 (12.1) Hemorrhage status None 200 (11.6) 75 (11.2) Stopped external bleeding 608 (35.2) 182 (27.1) Active external bleeding 489 (28.3) 233 (34.7) 0.002 Internal hemorrhage 230 (13.3) 101 (15.0) Unknown 201 (11.6) 81 (12.1) Location of trauma Head/neck 1199 (69.4) 490 (72.9) Face 122 (7.1) 24 (3.6) Extremity 56 (3.2) 19 (2.8) 0.035 Other 144 (8.3) 56 (8.3) Multiple injuries 38 (2.2) 11 (1.6) Unknown 169 (9.8) 72 (10.7) Response time (minute) Median (IQR) 10 (7–14) 10 (7–15) 0.430 ≤8 697 (40.3) 257 (38.2) 0.347 >8 1031 (59.7) 415 (61.8) Scene to hospital time (minute) Median (IQR) 7 (4–10) 7 (5–12) 0.475 ≤8 1041 (60.2) 388 (57.7) 0.262 >8 687 (39.8) 284 (42.3) Time from hospital to EMS station (minute) Median (IQR) 0 (0.01–0.01) 0.01 (0.01–0.01) 0.004 ≤1 1589 (92.0) 583 (86.8) <0.001 >1 139 (8.0) 89 (13.2) Distance from EMS station to scene (km) Median (IQR) 7 (4–11) 7 (4–11) 0.249 ≤10 1254 (72.6) 481 (71.6) >10 448 (25.9) 182 (27.1) 0.817 Unknown 26 (1.5) 9 (1.3) Distance from scene to hospital (km) Median (IQR) 7 (4–11) 7 (4–11) 0.194 ≤10 1257 (72.7) 449 (66.8) >10 445 (25.8) 179 (26.6) <0.001 Unknown 26 (1.5) 44 (6.5) This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem 5 Archives of Academic Emergency Medicine. 2022; 10(1): e64 Table 1: Comparing the baseline characteristics between patients with and without return of spontaneous circulation (ROSC) Variables ROSC on scene (n = 2400) P value Yes (n=1728) No (n = 672) Distance from hospital to EMS station (km) Median (IQR) 5 (2–11) 7 (3–15) 0.176 <10 103 (6.0) 54 (8.0) ≥10 36 (2.1) 30 (4.5) <0.001 Unknown 1589 (92) 588 (87.5) Prehospital airway management No 100 (5.8) 89 (13.2) BVM 1507 (87.2) 550 (81.8) <0.001 ETT 65 (3.8) 10 (1.5) Supra-glottic airway devices 56 (3.2) 23 (3.4) Prehospital hemorrhage control No 342 (19.8) 148 (22) Pressure dressing 1127 (65.2) 430 (64) 0.444 Dressing 259 (15.0) 94 (14) Prehospital fluid management No 82 (4.7) 131 (19.5) NSS 1371 (79.3) 464 (69.0) <0.001 RLS 250 (14.5) 70 (10.4) Other 25 (1.4) 7 (1.1) Prehospital immobilization No 97 (5.6) 90 (13.4) Splint 66 (3.8) 22 (3.3) <0.001 Collar with long spinal board 1565 (90.6) 560 (83.3) Prehospital AED/defibrillation No 1640 (94.9) 630 (93.8) 0.261 Yes 88 (5.1) 42 (6.3) Medication during CPR No 266 (15.4) 118 (17.6) Yes 897 (51.9) 270 (40.2) <0.001 Unknown 565 (32.7) 284 (42.3) Data are presented as number (%) or mean ± standard deviation or median (interquartile range). P-value corresponds to independent samples t-test, Mann-Whitney U test, Chi-square test, or Fisher’s exact test. ROSC, return of spontaneous circulation; EMS, emergency medical services; IQR, interquartile range; BVM, bag valve mask; ETT, endotracheal tube; NSS, normal saline; RLS, ringer lactate; AED, automated external defibrillator; CPR, cardiopulmonary resuscitation. facial injury (adjusted OR = 2.17, 95%CI: 1.37–3.44, p = 0.001); prehospital airway management using BVM (adjusted OR = 1.69, 95%CI: 1.21–2.34, p = 0.002) and ETT (adjusted OR = 3.88, 95%CI: 1.84–8.18, p <0.001); and prehospital fluid therapy using normal saline (adjusted OR = 4.24, 95%CI: 3.12–5.77, p <0.001), ringer lactate (adjusted OR = 5.13, 95%CI: 3.47–7.61, p <0.001), and other solutions (adjusted OR = 5.25, 95%CI: 2.16–12.8, p <0.001) were the independent pre- dictive factors of ROSC at the scene in OHCA due to traffic accidents (Table 2). 4. Discussion In the present study, the rate of ROSC at the scene for cases of OHCA due to traffic accidents, serviced by EMS was high, i.e., 72%, and three predictive factors of ROSC at the scene were facial injury, prehospital airway management, and prehospi- tal fluid management. The high rate of ROSC at the scene among traffic accident patients probably means that the traffic accident patients with cardiac arrest usually had a high survival rate, consis- tent with the results of a study in Thailand, reporting that the survival rate of those injured due to traffic accidents, managed by EMS, and admitted to tertiary hospitals was 97.9% at the scene (14). Moreover, the result was compa- rable to the authors’ previous study, which showed that the ROSC rate among traffic accident patients was higher than non-traumatic patients at the scene (15). The results of the present study were consistent with those of a previous study reporting short and quite good survival and recovery time for traffic accident patients (16). Most traffic accident patients with OHCA survived with a mortality rate of only 7.4% in the emergency department in Addis Ababa, Ethiopia (17). A facial injury could predict ROSC at the scene for traffic ac- cident patients with OHCA, managed by EMS in the present study, a finding consistent with that of a previous study This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem T. Huabbangyang et al. 6 Table 2: Multivariate analysis of factors associated with on-the-scene return of spontaneous circulation in patients with out-of-hospital car- diopulmonary arrest due to traffic accident Factors Multivariate analysis OR adjusted 95%CI P value Location of trauma Head/Neck 1.00 Reference Face 2.17 (1.37–3.44) 0.001 Extremity 1.17 (0.68–2.01) 0.570 Other 1.01 (0.72–1.42) 0.948 Multiple injuries 1.55 (0.77–3.12) 0.220 Unknown 1.03 (0.76–1.41) 0.833 Prehospital airway management No 1.00 Reference Bag valve mask 1.69 (1.21–2.34) 0.002 Endotracheal tube 3.88 (1.84–8.18) <0.001 Supraglottic airway devices 1.41 (0.78–2.54) 0.253 Prehospital fluid management No 1.00 Reference Normal saline 4.24 (3.12–5.77) <0.001 Ringer lactate 5.13 (3.47–7.61) <0.001 Other 5.25 (2.16–12.8) <0.001 Hosmer and Lemeshow Test: Chi-square = 7.060, df = 4, p-value = 0.133, Constant = -0.922. OR: odds ratio; CI: confidence interval. demonstrating that the injured body part affected the chance of survival, especially in those with a single injury, such as the face and extremity. They also reported that injury to criti- cal body parts, such as the chest, abdomen, pelvis, head, and neck, and multiple injuries negatively affected survival (18). Prehospital airway management, such as BVM and ETT in traffic accident patients with OHCA, can also be used to pre- dict ROSC at the scene, a finding consistent with the authors’ previous national database study, comparing ROSC rates be- tween patient groups receiving BVM and ETT. No statistically significant difference in prehospital ROSC rate was detected (19), which conflicted with that of a previous study indicat- ing that traffic accident patients intubated with ETT by EMS had a better outcome than those without ETT intubation and prehospital ETT intubation in indicated patients. Patients with OHCA might have a lower mortality rate and also im- proved early neurological outcomes (20), a finding compara- ble to the study indicating that those with ETT intubation in out-of-hospital patients had better outcome than the group without ETT intubation, including decreased mortality rate, and prehospital ETT intubation was not associated with in- creased morbidities and mortalities (21). However, ETT intu- bation in our study area depended on the protocols of each area, and only EPs and paramedics were permitted to intu- bate, which limited its performance. For ALS teams with nei- ther EPs nor paramedics, BVM was used instead of ETT. Prehospital fluid management was a factor that could predict ROSC at the scene for traffic accident patients with OHCA, managed by EMS. RLS, NSS, and other fluid replacements were well known for prehospital fluid resuscitation in trau- matic arrest, as an isotonic crystalloid solution is suggested to be the first choice in the injured with shock due to blood loss and OHCA (22). A previous clinical trial study indicated that RLS was superior to NSS in clinical outcomes, admis- sion duration, and survival (23, 24). However, a decision in fluid replacement selection for traffic accident patients with OHCA in the present study relied on the team leader, and the protocol determined in each area was different. 5. Limitations The most important limitation of the present study was for ITEMS database, as some of the important factors reported by previous studies to affect survival of the injured patients by were not recorded for OHCA patients, such as bystander CPR and initial cardiac rhythm. The second limitation was the inevitable bias associated with a retrospective study, as some data were lost, leading to exclusion. Third, since data were collected from the database in the ITEMS, treatment re- sults were limited, accessible only to treatment at the scene; thus, no in-hospital treatment and patient outcomes, treat- ment in the emergency department, surgery, or other treat- ments were included, resulting in evaluating only the out- comes at the scene by emergency unit or EMS team. Fourth, the severity and mechanism of injury were not considered in the study. Lastly, EMS operation units in the study area were markedly different in operation staff, resources, and proto- cols for each area, which probably leads to the inability to generalize the present study’s results. This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem 7 Archives of Academic Emergency Medicine. 2022; 10(1): e64 6. Conclusion Based on the findings, the ROSC rate of OHCA cases due to traffic accidents, serviced by EMS was high at the scene, i.e., 72%, and three independent predictive factors of ROSC at the scene were facial injury, prehospital airway management, and prehospital fluid management. 7. Declarations 7.1. Acknowledgments The authors were grateful to Navamindradhiraj University Research Fund, National Institute for Emergency Medicine for supporting data in the study, Chunlanee Sangketchon MD, chief of Department of Disaster and Emergency Medical Operation, Faculty of Science and Health Technology, Nava- mindradhiraj University for always assisting and facilitating researching, Anucha Kamsom, Division of Biostatistic, Fac- ulty of Medicine Vajira Hospital, Navamindradhiraj Univer- sity for statistical consultancy and Dr. Aniwat Berpan for act- ing as an English consultant for this study. 7.2. Authors’ contributions Design of the study by Thongpitak Huabbangyang; Data acquisition by Thongpitak Huabbangyang, Chunlanee Sangketchon, Sakditat Ittiphisit and Kanittha Uoun; Data analysis and interpretation by Thongpitak Huabbangyang, Chunlanee Sangketchon and Chomkamol Saumok; drafting the manuscript by Thongpitak Huabbangyang; Revision of the manuscript by Thongpitak Huabbangyang, Chun- lanee Sangketchon, Sakditat Ittiphisit, Kanittha Uoun and Chomkamol Saumok; the final version of the manuscript is approved by all the authors. 7.3. Funding and supports The study was funded by Navamindradhiraj University (No. 102/2564) who played no role in study design, data collec- tion, data analysis, or writing the manuscript. 7.4. Conflict of interest The authors report no conflict of interest. References 1. WHO. World report on road traffic injury 2021 [2021 June 28]. Available from: https://www.who.int/news- room/fact-sheets/detail/road-traffic-injuries. 2. Limited RAVPC. Statistics and information through- out the year [2021 June 28]. Available from: https://www.thairsc.com. 3. Chadbunchachai W, Suphanchaimaj W, Settasatien A, Jinwong T. Road traffic injuries in Thailand: current sit- uation. J Med Assoc Thai. 2012;95(Suppl 7):S274-81. 4. Aramrerng P, Sutham K, Wittayachamnankul B, Kaew- paengchan W, Laosuksri W, Sairai R, et al. [Survival of Out-of-Hospital Cardiac Arrest of Traumatic Patients who Received Medical Care from Emergency Medical Service System]. J Health Syst Res. 2020;14:43-50. Thai. 5. Saeheng P, Chavanasporn K, Phanrangsee P, Waikila N, Santaweephol N, Noosila N, et al. The response time and the adequacy of emergency medical service team for elderly patients in Bangkok: A case study of Bangkok Emergency Medical Service Center,(Erawan Center). Va- jira Med J. 2019;63(Supplement):S65-72. 6. Djarv T, Axelsson C, Herlitz J, Stromsoe A, Israelsson J, Claesson A. Traumatic cardiac arrest in Sweden 1990- 2016-a population-based national cohort study. Scand J Trauma Resusc Emerg Med. 2018;26(1):1-8. 7. Zwingmann J, Lefering R, Feucht M, Südkamp NP, Strohm PC, Hammer T. Outcome and predictors for suc- cessful resuscitation in the emergency room of adult pa- tients in traumatic cardiorespiratory arrest. Crit Care. 2016;20(1):1-10. 8. Inamasu J, Miyatake S, Yagi T, Noma S. Resuscitation out- comes of cardiac arrest patients who caused witnessed non-fatal road traffic accidents while driving. Resuscita- tion. 2017;119:e15-e6. 9. Jun GS, Kim JG, Choi HY, Kang GH, Kim W, Jang YS, et al. Prognostic factors related with outcomes in traumatic out-of-hospital cardiac arrest patients without prehospi- tal return of spontaneous circulation: a nationwide ob- servational study. Clin Exp Emerg Med. 2020;7(1):14-20. 10. Beck B, Bray JE, Cameron P, Straney L, Andrew E, Bernard S, et al. Predicting outcomes in traumatic out-of-hospital cardiac arrest: the relevance of Utstein factors. Emerg Med J. 2017;34(12):786-92. 11. Department SaE. Thailand Statistical Re- port 2020 [2021 June 28]. Available from: http://www.bangkok.go.th/upload/user/00000130/BMA _STATISTICS%202563/ebook%2063.pdf. 12. Rosner B. Fundamentals of biostatistics. Fifth edition ed: Duxbury Thomson learning–united state USA; 2000. 13. P Pitisuttithum, Picheansunthorn C. Textbook of clin- ical research fourth edition. revised and expanded ed. Bangkok: Faculty of Tropical Medicine, Mahidol Univer- sity: Faculty of Tropical Medicine, Mahidol University; 2011. 14. Wongvatanakij P, Tadadej C, Meyai A, Suriyawongpaisal P. Relation of factor on survival outcomes among trau- matic patients at the tertiary care hospital admitted for traffic accidents in Phuket Province. Srinagarind Med J. 2019;34(1):52-9. This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem T. Huabbangyang et al. 8 15. Huabbangyang T, Soion T, Promdee A, Nguanjinda K, Chamchan A, Chaisorn R, et al. Factors Associated with Successful Resuscitation during Out-of-Hospital Cardiac Arrest Performed By Surgico Medical Ambulance and Rescue Team (SMART), Division of Emergency Medical Service and Disaster, Faculty of Medicine Vajira Hospital, Navamindrad. J Med Assoc Thai. 2021;104(9):1488-96. 16. Tesfay K, Assefa M, Zenebe D, Gebremicael M, Kebede G, Gebrekirstos H. Road traffic injured patients with severe GCS and organ injury had a poor prognosis: a retrospec- tive cohort study. BMC public health. 2019;19(1):749. 17. Seid M, Azazh A, Enquselassie F, Yisma E. Injury characteristics and outcome of road traffic accident among victims at Adult Emergency Department of Tikur Anbessa specialized hospital, Addis Ababa, Ethiopia: a prospective hospital based study. BMC emerg med. 2015;15(1):10. 18. Pooncharoen T, Hoacharoensirichai P, Chaisorn R, Pong- pan P, Nithimathachoke A, Rojsaengroeng R. Factors Af- fecting the Survival Rate of Prehospital Traffic Accident Patients in Thailand. Thai J Emerg Med. 2019;1(1):27-39. 19. Yuksen C, Phattharapornjaroen P, Kreethep W, Suwan- mano C, Jenpanitpong C, Nonnongku R, et al. Bag- valve mask versus endotracheal intubation in out-of- hospital cardiac arrest on return of spontaneous circula- tion: a national database study. Open Access Emerg Med. 2020;12:43-6. 20. Hoffmann M, Czorlich P, Lehmann W, Spiro AS, Rueger JM, Lefering R. The impact of prehospital intubation with and without sedation on outcome in trauma pa- tients with a GCS of 8 or less. J Neurosurg Anesthesiol. 2017;29(2):161-7. 21. Denninghoff KR, Nuño T, Pauls Q, Yeatts SD, Silbergleit R, Palesch YY, et al. Prehospital intubation is associated with favorable outcomes and lower mortality in ProTECT III. Prehosp Emerg Care. 2017;21(5):539-44. 22. Ramesh G, Uma J, Farhath S. Fluid resuscitation in trauma: what are the best strategies and fluids? Int J Emerg Med. 2019;12(1):38. 23. Mane AS. Fluid resuscitation: ringer lactate versus nor- mal saline–a clinical study. Int J Contemp Med Res. 2017;4:2290-3. 24. Martini WZ, Cortez DS, Dubick MA. Comparisons of nor- mal saline and lactated Ringer’s resuscitation on hemo- dynamics, metabolic responses, and coagulation in pigs after severe hemorrhagic shock. Scand J Trauma, Resusc Emerg Med. 2013;21(1):86. This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem Introduction Methods Results Discussion Limitations Conclusion Declarations References