The prediction of the survival in patients with severe trauma during prehospital care: Analyses based on NTDB database

Rhee P, Joseph B, Pandit V, et al. Increasing trauma deaths in the United States. Annals Surg. 2014;260(1):13–21. https://doi.org/10.1097/sla.0000000000000600.

Article  Google Scholar 

Gruen RL, Brohi K, Schreiber M, et al. Haemorrhage control in severely injured patients. Lancet (London, England). 2012;380(9847):1099–108. https://doi.org/10.1016/s0140-6736(12)61224-0.

Article  PubMed  Google Scholar 

Cunningham RM, Walton MA, Carter PM. The Major Causes of Death in Children and Adolescents in the United States. New England J Med. 2018;379(25):2468–75. https://doi.org/10.1056/NEJMsr1804754.

Article  Google Scholar 

Acosta JA, Yang JC, Winchell RJ, et al. Lethal injuries and time to death in a level I trauma center. J Ame College Surgeons. 1998;186(5):528–33. https://doi.org/10.1016/s1072-7515(98)00082-9.

Article  CAS  Google Scholar 

Clark DE, Qian J, Sihler KC, Hallagan LD, Betensky RA. The distribution of survival times after injury. World J Surg. 2012;36(7):1562–70. https://doi.org/10.1007/s00268-012-1549-5.

Article  PubMed  PubMed Central  Google Scholar 

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.

Article  CAS  PubMed  Google Scholar 

Champion HR, Sacco WJ, Carnazzo AJ, Copes W, Fouty WJ. Trauma score. Critical Care Med. 1981;9(9):672–6. https://doi.org/10.1097/00003246-198109000-00015.

Article  CAS  Google Scholar 

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.

Champion HR, Copes WS, Sacco WJ, et al. A new characterization of injury severity. J Trauma. 1990;30(5):539-45; discussion 45-6. https://doi.org/10.1097/00005373-199005000-00003

Osler T, Rutledge R, Deis J, Bedrick E. ICISS: an international classification of disease-9 based injury severity score. J Trauma. 1996;41(3):380-6; discussion 6-8. https://doi.org/10.1097/00005373-199609000-00002

Burd RS, Ouyang M, Madigan D. Bayesian logistic injury severity score: a method for predicting mortality using international classification of disease-9 codes. Academic Emergency Med: Official J Soc Acad Emerg Med. 2008;15(5):466–75. https://doi.org/10.1111/j.1553-2712.2008.00105.x.

Article  Google Scholar 

Champion HR, Sacco WJ, Copes WS, Gann DS, Gennarelli TA, Flanagan ME. A revision of the Trauma Score. J Trauma. 1989;29(5):623–9. https://doi.org/10.1097/00005373-198905000-00017.

Article  CAS  PubMed  Google Scholar 

Lavoie A, Emond M, Moore L, Camden S, Liberman M. Evaluation of the Prehospital Index, presence of high-velocity impact and judgment of emergency medical technicians as criteria for trauma triage. Cjem. 2010;12(2):111–8. https://doi.org/10.1017/s1481803500012136.

Article  PubMed  Google Scholar 

Gray A, Goyder EC, Goodacre SW, Johnson GS. Trauma triage: a comparison of CRAMS and TRTS in a UK population. Injury. 1997;28(2):97–101. https://doi.org/10.1016/s0020-1383(96)00170-2.

Article  CAS  PubMed  Google Scholar 

Morris RS, Karam BS, Murphy PB, Jenkins P, Milia DJ, Hemmila MR, et al. Field-triage, hospital-triage and triage-assessment: a literature review of the current phases of adult trauma triage. J Trauma Acute Care Surg. 2021;90(6):e138–45. https://doi.org/10.1097/TA.0000000000003125.

Article  PubMed  Google Scholar 

Osler T. Injury severity scoring: perspectives in development and future directions. Ame J Surg. 1993;165(2A Suppl):43s–51s. https://doi.org/10.1016/s0002-9610(05)81206-1.

Article  CAS  Google Scholar 

West TA, Rivara FP, Cummings P, Jurkovich GJ, Maier RV. Harborview assessment for risk of mortality: an improved measure of injury severity on the basis of ICD-9-CM. J Trauma. 2000;49(3):530-40; discussion 40-1. https://doi.org/10.1097/00005373-200009000-00022

Glance LG, Osler TM, Mukamel DB, Meredith W, Wagner J, Dick AW. TMPM-ICD9: a trauma mortality prediction model based on ICD-9-CM codes. Annals Sur. 2009;249(6):1032–9. https://doi.org/10.1097/SLA.0b013e3181a38f28.

Article  Google Scholar 

Gorczyca MT, Toscano NC, Cheng JD. The trauma severity model: An ensemble machine learning approach to risk prediction. Comput Biology Med. 2019;108:9–19. https://doi.org/10.1016/j.compbiomed.2019.02.025.

Article  Google Scholar 

Larsson A, Berg J, Gellerfors M, Gerdin Wärnberg M. The advanced machine learner XGBoost did not reduce prehospital trauma mistriage compared with logistic regression: a simulation study. BMC Med Inform Decision Making. 2021;21(1):192. https://doi.org/10.1186/s12911-021-01558-y.

Article  Google Scholar 

Hashmi ZG, Kaji AH, Nathens AB. Practical Guide to Surgical Data Sets: National Trauma Data Bank (NTDB). JAMA Surg. 2018;153(9):852–3. https://doi.org/10.1001/jamasurg.2018.0483.

Article  PubMed  Google Scholar 

Collins GS, Reitsma JB, Altman DG, Moons KG. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMJ (Clinical research ed.). 2015;350:g7594. https://doi.org/10.1136/bmj.g7594

Chester JG, Rudolph JL. Vital signs in older patients: age-related changes. J Ame Med Directors Assoc. 2011;12(5):337–43. https://doi.org/10.1016/j.jamda.2010.04.009.

Article  Google Scholar 

Miller PJ, McArtor DB, Lubke GH. A Gradient Boosting Machine for Hierarchically Clustered Data. Multivariate Behav Res. 2017;52(1):117. https://doi.org/10.1080/00273171.2016.1265433.

Article  PubMed  PubMed Central  Google Scholar 

Harrell FE Jr, Califf RM, Pryor DB, Lee KL, Rosati RA. Evaluating the yield of medical tests. Jama. 1982;247(18):2543–6.

Article  PubMed  Google Scholar 

Mogensen UB, Ishwaran H, Gerds TA. Evaluating Random Forests for Survival Analysis using Prediction Error Curves. Journal of statistical software. 2012;50(11):1-23. https://doi.org/10.18637/jss.v050.i11

Martin AB, Hartman M, Washington B, Catlin A. National Health Spending: Faster Growth In 2015 As Coverage Expands And Utilization Increases. Health affairs (Project Hope). 2017;36(1):166–76. https://doi.org/10.1377/hlthaff.2016.1330.

Article  PubMed  Google Scholar 

Vittinghoff E, McCulloch CE. Relaxing the rule of ten events per variable in logistic and Cox regression. Ame J Epidemiol. 2007;165(6):710–8. https://doi.org/10.1093/aje/kwk052.

Article  Google Scholar 

Gauss T, Ageron FX, Devaud ML, et al. Association of Prehospital Time to In-Hospital Trauma Mortality in a Physician-Staffed Emergency Medicine System. JAMA Surg. 2019;154(12):1117–24. https://doi.org/10.1001/jamasurg.2019.3475.

Article  PubMed  PubMed Central  Google Scholar 

Brown JB, Rosengart MR, Forsythe RM, et al. Not all prehospital time is equal: Influence of scene time on mortality. J Trauma Acute Care Surg. 2016;81(1):93–100. https://doi.org/10.1097/ta.0000000000000999.

Article  PubMed  PubMed Central  Google Scholar 

Nasser AAH, Nederpelt C, El Hechi M, et al. Every minute counts: The impact of pre-hospital response time and scene time on mortality of penetrating trauma patients. Ame J Surg. 2020;220(1):240–4. https://doi.org/10.1016/j.amjsurg.2019.11.018.

Article  Google Scholar 

Kay R. Goodness of fit methods for the proportional hazards regression model: a review. Revue d’epidemiologie et de sante publique. 1984;32(3–4):185–98.

CAS  PubMed  Google Scholar 

Du M, Haag DG, Lynch JW, Mittinty MN. Comparison of the Tree-Based Machine Learning Algorithms to Cox Regression in Predicting the Survival of Oral and Pharyngeal Cancers: Analyses Based on SEER Database. Cancers. 2020;12(10). https://doi.org/10.3390/cancers12102802

Ishwaran H, Kogalur UB, Blackstone EH, Lauer MS. Random survival forests. Annals Appli Stat. 2008;2(3):841-60, 20.

Strobl C, Malley J, Tutz G. An introduction to recursive partitioning: rationale, application, and characteristics of classification and regression trees, bagging, and random forests. Psycho Meth. 2009;14(4):323–48. https://doi.org/10.1037/a0016973.

Article  Google Scholar 

Ishwaran H, Gerds TA, Kogalur UB, Moore RD, Gange SJ, Lau BM. Random survival forests for competing risks. Biostatistics (Oxford, England). 2014;15(4):757–73. https://doi.org/10.1093/biostatistics/kxu010.

Article  PubMed  PubMed Central  Google Scholar 

Wang H, Li G. A Selective Review on Random Survival Forests for High Dimensional Data. Quantitative bio-science. 2017;36(2):85-96. https://doi.org/10.22283/qbs.2017.36.2.85

Li Y, Wang L, Liu Y, et al. Development and Validation of a Simplified Prehospital Triage Model Using Neural Network to Predict Mortality in Trauma Patients: The Ability to Follow Commands, Age, Pulse Rate, Systolic Blood Pressure and Peripheral Oxygen Saturation (CAPSO) Model. Front Med. 2021;8:810195. https://doi.org/10.3389/fmed.2021.810195.

Article  ADS  Google Scholar 

留言 (0)

沒有登入
gif