Background: Traffic accident patients place a tremendous burden on health care services because they require substantial, rapid, and effective evaluation, management, and treatment by emergency medical services (EMS) to decrease morbidity and mortality rates. This study investigated the 1-month survival rate and factors related to the survival of traffic accident patients managed by EMS.
Patients And Methods: We retrospectively analyzed data of traffic accident patients serviced by the Surgico Medical Ambulance and Rescue Team (SMART) at Vajira Hospital, Bangkok, from January 1, 2018, to December 31, 2020. The data were collected from EMS patient care reports recorded using the emergency medical triage protocol as well as the criteria-based dispatch response codes in Thailand. Survival data at 1 month were obtained from electronic medical records.
Results: Of the 340 traffic accident patients who fulfilled the study criteria, 314 (92.35%) were alive at 1 month. A multivariable analysis using multiple logistic regression identified prehospital level of consciousness, airway management, and cardiopulmonary resuscitation as factors associated with survival. Unresponsive patients had a lower survival rate than responsive patients (adjusted odds ratio [OR] = 0.16, 95% confidence interval [CI]: 0.05-0.56, p = 0.004). Prehospital airway management and cardiopulmonary resuscitation reduced the survival rate by 0.30 and 0.10 times, respectively (OR = 0.30, 95% CI: 0.09-0.97, p = 0.045 and OR = 0.10, 95% CI: 0.02-0.47, p = 0.004, respectively).
Conclusion: Traffic accident patients had a high survival rate at 1 month. We identified three factors regarding EMS treatment which were related to increased survival: a prehospital responsive level of consciousness, no prehospital airway management, and no prehospital cardiopulmonary resuscitation. Therefore, the development of standard guidelines for the management of traffic accident patients by EMS is crucial to increase the survival rate of traffic accident patients.
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http://dx.doi.org/10.2147/OAEM.S344705 | DOI Listing |
Scand J Trauma Resusc Emerg Med
January 2025
Faculty of Pre-Hospital Care, Royal College of Surgeons Edinburgh, Edinburgh, UK.
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One Health Lesson Administrative Intern, Addis Ababa, Ethiopia.
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Accid Anal Prev
January 2025
School of Computer Science and Informatics, De Montfort University, Leicester LE1 9BH, UK.
With the continuous development of intelligent transportation systems, traffic safety has become a major societal concern, and vehicle trajectory anomaly detection technology has emerged as a crucial method to ensure safety. However, current technologies face significant challenges in handling spatiotemporal data and multi-feature fusion, including difficulties in big data processing, and have room for improvement in these areas. To address these issues, this paper proposes a novel method that combines autoencoders, Mahalanobis distance, and dynamic Bayesian networks for anomaly detection.
View Article and Find Full Text PDFDent Traumatol
January 2025
Department of Pediatric Dentistry, Faculty of Dentistry, Ataturk University, Erzurum, Turkey.
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