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The Accuracy of Prediction Models in Burn Patients

화상환자에서 사망예측모델의 성능 평가에 관한 연구

  • Woo, Jaeyeon (Department of Surgery, Hallym University Kangnam Sacred Heart Hospital) ;
  • Kym, Dohern (Department of Surgery, Hallym University Hangang Sacred Heart Hospital)
  • 우재연 (한림대학교 강남성심병원 외과학교실) ;
  • 김도헌 (한림대학교 한강성심병원 외과학교실)
  • Received : 2021.03.21
  • Accepted : 2021.04.13
  • Published : 2021.06.30

Abstract

Purpose: The purpose of this study was to evaluate the accuracy of four prediction models in adult burn patients. Methods: This retrospective study was conducted on 696 adult burn patients who were treated at burn intensive care unit (BICU) of Hallym University Hangang Sacred Heart Hospital from January 2017 to December 2019. The models are ABSI, APACHE IV, rBaux and Hangang score. Results: The discrimination of each prediction model was analyzed as AUC of ROC curve. AUC value was the highest with Hangang score of 0.931 (0.908~0.954), followed by rBaux 0.896 (0.867~0.924), ABSI 0.883 (0.853~0.913) and APACHE IV 0.851 (0.818~0.884). Conclusion: The results of evaluating the accuracy of the four models, Hangang score showed the highest prediction. But it is necessary to apply the appropriate prediction model according to characteristics of the burn center.

Keywords

References

  1. Cho YS, Yim H, Yang HT, Hur J, Chun W, Kim JH, et al. Use of parenteral colistin for the treatment of multiresistant Gram-negative organisms in major burn patients in South Korea. Infection. 2012;40:27-33. https://doi.org/10.1007/s15010-011-0192-7
  2. Smolle C, Cambiaso-Daniel J, Forbes AA, Wurzer P, Hundeshagen G, Branski LK, et al. Recent trends in burn epidemiology worldwide: A systematic review. Burns 2017;43:249-57. https://doi.org/10.1016/j.burns.2016.08.013
  3. Seo DK, Kym D, Yim H, Yang HT, Cho YS, Kim JH, et al. Epidemiological trends and risk factors in major burns patients in South Korea: a 10-year experience. Burns 2015;41:181-7. https://doi.org/10.1016/j.burns.2014.05.004
  4. Strassle PD, Williams FN, Napravnik S, van Duin D, Weber DJ, Charles A, et al. Improved Survival of Patients With Extensive Burns: Trends in Patient Characteristics and Mortality Among Burn Patients in a Tertiary Care Burn Facility, 2004-2013. J Burn Care Res 2017;38:187-93. https://doi.org/10.1097/BCR.0000000000000456
  5. Salehi SH, As'adi K, Abbaszadeh-Kasbi A, Isfeedvajani MS, Khodaei N. Comparison of six outcome prediction models in an adult burn population in a developing country. Ann Burns Fire Disasters 2017;30:13-7.
  6. Tobiasen J, Hiebert JM, Edlich RF. The abbreviated burn severity index. Ann Emerg Med 1982;11:260-2. https://doi.org/10.1016/S0196-0644(82)80096-6
  7. Korkmaz Toker M, Gulleroglu A, Karabay AG, Bicer I G, Demiraran Y. SAPS III or APACHE IV: Which score to choose for acute trauma patients in intensive care unit? Ulus Travma Acil Cerrahi Derg 2019;25:247-52.
  8. Zimmerman JE, Kramer AA, McNair DS, Malila FM. Acute Physiology and Chronic Health Evaluation (APACHE) IV:hospital mortality assessment for today's critically ill patients. Crit Care Med 2006;34:1297-310. https://doi.org/10.1097/01.CCM.0000215112.84523.F0
  9. Osler T, Glance LG, Hosmer DW. Simplified estimates of the probability of death after burn injuries: extending and updating the baux score. J Trauma 2010;68:690-7. https://doi.org/10.1097/TA.0b013e3181c453b3
  10. Kim Y, Kym D, Hur J, Jeon J, Yoon J, Yim H, et al. Development of a risk prediction model (Hangang) and comparison with clinical severity scores in burn patients. PLoS One 2019;14:e0211075. https://doi.org/10.1371/journal.pone.0211075
  11. Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 1982;143:29-36. https://doi.org/10.1148/radiology.143.1.7063747
  12. Brier GW. Verification of forecasts expressed in terms of probability. Monthey Weather Review 1950;78:1-3. https://doi.org/10.1175/1520-0493(1950)078<0001:VOFEIT>2.0.CO;2
  13. Steyerberg EW, Vickers AJ, Cook NR, Gerds T, Gonen M, Obuchowski N, et al. Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology (Cambridge, Mass) 2010;21:128-38. https://doi.org/10.1097/EDE.0b013e3181c30fb2
  14. Kym D, Cho YS, Yoon J, Yim H, Yang HT. Evaluation of diagnostic biomarkers for acute kidney injury in major burn patients. Ann Surg Treat Res 2015;88:281-8. https://doi.org/10.4174/astr.2015.88.5.281
  15. Walsh MB, Miller SL, Kagen LJ. Myoglobinemia in severely burned patients: correlations with severity and survival. J Trauma 1982;22:6-10. https://doi.org/10.1097/00005373-198201000-00002
  16. Guldogan CE, Kendirci M, Gundogdu E, Yasti A. Analysis of factors associated with mortality in major burn patients. Turk J Surg 2019;35:155-64.
  17. Liu ZJ, Zhang Y, Zhang XB, Yang X. Observation and identification of lactate dehydrogenase anomaly in a postburn patient. Postgrad Med J 2004;80:481-3. https://doi.org/10.1136/pgmj.2003.015420
  18. Wong TH, Tan BH, Ling ML, Song C. Multi-resistant Acinetobacter baumannii on a burns unit--clinical risk factors and prognosis. Burns 2002;28:349-57. https://doi.org/10.1016/S0305-4179(02)00012-8
  19. Tanaka Y, Shimizu M, Hirabayashi H. Acute physiology, age, and chronic health evaluation (APACHE) III score is an alternative efficient predictor of mortality in burn patients. Burns 2007;33:316-20. https://doi.org/10.1016/j.burns.2006.07.004
  20. Halgas B, Bay C, Foster K. A comparison of injury scoring systems in predicting burn mortality. Ann Burns Fire Disasters 2018;31:89-93.
  21. Moore EC, Pilcher DV, Bailey MJ, Cleland H, McNamee J. A simple tool for mortality prediction in burns patients: APACHE III score and FTSA. Burns 2010;36:1086-91. https://doi.org/10.1016/j.burns.2010.03.013
  22. Gomez M, Wong DT, Stewart TE, Redelmeier DA, Fish JS. The FLAMES score accurately predicts mortality risk in burn patients. J Trauma 2008;65:636-45. https://doi.org/10.1097/TA.0b013e3181840c6d