Document Type : Research articles

Authors

1 Trauma Research Center, Shahid Rajaee Hospital, Shiraz University of Medical Sciences, Shiraz, Iran

2 Trauma Research Center, Shahid Rajaee Hospital, Shiraz University of Medical Sciences, Shiraz, Iran.

Abstract

Background: Simulation studies present an important statistical tool to investigate the performance, properties, and adequacy of statistical models in pre-specified situations. The proportional hazards model of survival analysis is one of the most important statistical models in medical studies. This study aimed to investigate the underlying one-month survival of road traffic accident (RTA) victims in a Level 1 Trauma Center in Iran using parametric and semi-parametric survival analysis models from the viewpoint of post-crash care-provider in 2017.
Materials and Methods: This retrospective cohort study (restudy) was conducted at Level-I Trauma Center of Shiraz, Iran, from January to December 2017. Considering the fact that certain covariates acting on survival may take a non-homogenous risk pattern leading to the violation of proportional hazards assumption in Cox-PH, the parametric survival modeling was employed to inspect the multiplicative effect of all covariates on the hazard. Distributions of choice were Exponential, Weibull and Lognormal. Parameters were estimated using the Akaike
Results: Survival analysis was conducted on 8,621 individuals for whom the length of stay (observation period) was between 1 and 89 days. In total, 141 death occurred during this time. The log-rank test revealed inequality of survival functions across various categories of age, injury mechanism, injured body region, injury severity score, and nosocomial infections. Although the risk level in the Cox model is almost the same as that in the results of the parametric models, the Weibull model in the multivariate analysis yields better results, according to the Akaike criterion.
Conclusion: In multivariate analysis, parametric models were more efficient than other models. Some results were similar in both parametric and semi-parametric models. In general, parametric models and among them the Weibull model was more efficient than other models.

Keywords

  1. Kaplan EL, Meier P. Nonparametric estimation from incomplete observations. J Am Statist Assoc. 1958;53(282):457-81.
  2. Bazargan-Hejazi S, Ahmadi A, Shirazi A, Ainy E, Djalalinia S, Fereshtehnejad SM, et al. The burden of road traffic injuries in Iran and 15 surrounding countries: 1990-2016. Arch Iran Med. 2018;21(12):556-65. [PubMed: 30634852].
  3. Wilson C, Willis C, Hendrikz JK, Le Brocque R, Bellamy N. Speed cameras for the prevention of road traffic injuries and deaths. Cochrane Database Syst Rev. 2010;11:Cd004607. doi: 10.1002/14651858.CD004607.pub4. [PubMed: 21069682].
  4. Goldman S, Siman-Tov M, Bahouth H, Kessel B, Klein Y, Michaelson M, et al. The contribution of the Israeli trauma system to the survival of road traffic casualties. Traffic Inj Prev. 2015;16(4):368-73. doi: 10.1080/15389588.2014.940458. [PubMed: 25133878].
  5. Chokotho LC, Matzopoulos R, Myers JE. Assessing quality of existing data sources on road traffic injuries (RTIs) and their utility in informing injury prevention in the Western Cape Province, South Africa. Traffic Inj Prev. 2013;14(3):267-73. doi: 10.1080/15389588.2012.706760. [PubMed: 23441945].
  6. Karkee R, Lee AH. Epidemiology of road traffic injuries in Nepal, 2001-2013: systematic review and secondary data analysis. BMJ Open. 2016;6(4):e010757. doi: 10.1136/bmjopen-2015-010757. [PubMed: 27084283].
  7. Hu G, Baker T, Baker SP. Comparing road traffic mortality rates from police-reported data and death registration data in China. Bull World Health Organ. 2011;89(1):41-5. doi: 10.2471/BLT.10.080317. [PubMed: 21346889].
  8. Lefering R. Trauma scoring systems. Curr Opin Crit Care. 2012;18(6):637-40. doi: 10.1097/MCC.0b013e3283585356. [PubMed: 22918259].
  9. Baker SP, O'Neill B, Haddon W Jr, Long WB. The injury severity score: a method for describing patients with multiple injuries and evaluating emergency care. J Trauma. 1974;14(3):187-96. [PubMed: 4814394].
  10. Boyd CR, Tolson MA, Copes WS. Evaluating trauma care: the TRISS method. Trauma score and the injury severity score. J Trauma. 1987;27(4):370-8. [PubMed: 3106646].
  11. Champion HR, Copes WS, Sacco WJ, Lawnick MM, Keast SL, Bain LW Jr, et al. The major trauma outcome study: establishing national norms for trauma care. J Trauma. 1990;30(11):1356-65. [PubMed: 2231804].
  12. Yadollahi[S1]  M, Paydar S, Sabetianfard Jahromi G, Khalili H, Etemadi S, Abbasi H, et al. Types and causalities in dead patients due to traumatic injuries. Arch Trauma Res. 2015;4(1):e26028. doi: 10.5812/atr.26028. [PubMed: 25798419].
  13. Loftis KL, Price JP, Gillich PJ, Cookman KJ, Brammer AL, St Germain T, et al. Development of an expert based ICD-9-CM and ICD-10-CM map to AIS 2005 update 2008. Traffic Inj Prev. 2016;17(Suppl 1):1-5. doi: 10.1080/15389588.2016.1191069. [PubMed: 27586094].
  14. Sadeghi-Bazargani H, Ayubi E, Azami-Aghdash S, Abedi L, Zemestani A, Amanati L, et al. Epidemiological patterns of road traffic crashes during the last two decades in Iran: a review of the literature from 1996 to 2014. Arch Trauma Res. 2016;5(3):e32985. doi: 10.5812/atr.32985. [PubMed: 27800461].
  15. Yadollahi M, Paydar S, Sabetianfard Jahromi G, Khalili H, Etemadi S, Abbasi H, et al. Types and causalities in dead patients due to traumatic injuries. Arch Trauma Res. 2015;4(1):e26028. doi: 10.5812/atr.26028. [PubMed: 25798419].
  16. Chung Y. Development of an accident duration prediction model on the Korean Freeway Systems. Accid Anal Prev. 2010;42(1):282-9. doi: 10.1016/j.aap.2009.08.005. [PubMed: 19887169].
  17. Li MD, Doong JL, Huang WS, Lai CH, Jeng MC. Survival hazards of road environment factors between motor-vehicles and motorcycles. Accid Anal Prev. 2009;41(5):938-47. doi: 10.1016/j.aap.2009.05.009. [PubMed: 19664430].
  18. 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. doi: 10.1186/s12873-015-0035-4. [PubMed: 25990560].
  19. Mansuri FA, Al-Zalabani AH, Zalat MM, Qabshawi RI. Road safety and road traffic accidents in Saudi Arabia: a systematic review of existing evidence. Saudi Med J. 2015;36(4):418-24. doi: 10.15537/smj.2015.4.10003. [PubMed: 25828277].
  20. DiRusso SM, Chahine AA, Sullivan T, Risucci D, Nealon P, Cuff S, et al. Development of a model for prediction of survival in pediatric trauma patients: comparison of artificial neural networks and logistic regression. J Pediatr Surg. 2002;37(7):1098-104. doi: 10.1053/jpsu.2002.33885. [PubMed: 12077780].
  21. Hachamovitch R, Hayes SW, Friedman JD, Cohen I, Berman DS. Comparison of the short-term survival benefit associated with revascularization compared with medical therapy in patients with no prior coronary artery disease undergoing stress myocardial perfusion single photon emission computed tomography. Circulation. 2003;107(23):2900-7. doi: 10.1161/01.CIR.0000072790.23090.41. [PubMed: 12771008].
  22. Askarishahi M, Keshavarzi F, AfkhamiArdakani M, Falahzadeh H. Using parametric and Cox models in analysis of factors influencing the diagnosis of retinopathy in type II diabetes. J Mazandaran Univ Med Sci. 2014;24(113):28-35.
  23. Pourhoseingholi M, Pourhoseingholi A, Vahedi M, Dehkordi BM, Safaee A, Ashtari S, et al. Alternative for the cox regression model: using parametric models to analyze the survival of cancer patients. Int J Cancer Manage. 2011;4(1):1-9.
  24. Moghimi-Dehkordi B, Safaee A, Pourhoseingholi MA, Fatemi R, Tabeie Z, Zali MR. Statistical comparison of survival models for analysis of cancer data. Asian Pac J Cancer Prev. 2008;9(3):417-20. [PubMed: 18990013].