Journal of the Institute of Electronics Engineers of Korea SC (전자공학회논문지SC)

The Institute of Electronics and Information Engineers (대한전자공학회)
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Blood Loss Prediction of Rats in Hemorrhagic Shock Using a Linear Regression Model

출혈성 쇼크를 일으킨 흰쥐에서 선형회귀 분석모델을 이용한 출혈량 추정

  • Lee, Tak-Hyung (Graduate Program in Biomedical Engineering, Yonsei University) ;
  • Lee, Ju-Hyung (Graduate Program in Biomedical Engineering, Yonsei University) ;
  • Choi, Jae-Rim (Graduate Program in Biomedical Engineering, Yonsei University) ;
  • Yang, Dong-In (Graduate Program in Biomedical Engineering, Yonsei University) ;
  • Kim, Deok-Won (Graduate Program in Biomedical Engineering, Yonsei University)
  • 이탁형 (연세대학교 생체공학협동과정) ;
  • 이주형 (연세대학교 생체공학협동과정) ;
  • 최재림 (연세대학교 생체공학협동과정) ;
  • 양동인 (연세대학교 생체공학협동과정) ;
  • 김덕원 (연세대학교 생체공학협동과정)
  • Published : 2010.01.25
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Abstract

Hemorrhagic shock is a common cause of death in the emergency department. The purpose of this study was to investigate the relationship between blood loss as a percent of the total estimated blood volume (% blood loss) and changes in several physiological parameters. The other goal was to achieve an accurate prediction of percent blood loss for hemorrhagic shock in rats using a linear regression model. We allocated 60 Sprague-Dawley rats into four groups: 0ml, 2ml, 2.5ml, 3 mL/100 g during 15 min. We analyzed the heart rate, systolic and diastolic blood pressure, respiration rate, and body temperature in relation to the percent blood loss. We generated a linear regression model predicting the percent blood loss using a randomly chosen 360 data set and the R-square value of the model was 0.80. Root mean square error of the tested 360 data set using the linear regression was 5.7%. Even though the linear regression model is not directly applicable to clinical situation, our method of predicting % blood loss could be helpful in determining the necessary fluid volume for resuscitation in the future.

출혈성 쇼크는 응급실에서 일어나는 사망 원인의 많은 부분을 차지하고 있다. 본 연구의 목적은 출혈량에 따라 변화하는 생리적인 변수들의 특징을 알아보는 것이다. 또한 이를 이용하여 전체 혈액량 대비 손실된 혈액의 비율을 산출하는 선형회귀분석 모델을 만드는 것이다. 총 60마리의 흰쥐를 출혈량에 따라 체중 100g 당 15분 동안 0ml, 2ml, 2.5ml 3ml로 정하여 총 4그룹으로 나누었다. 출혈 중에 변화하는 심박수, 수축기혈압, 이완기혈압, 호흡수, 체온 등을 분석하였다. 분석한 데이터를 무작위로 나누어 360개의 데이터 세트를 선형회귀 분석모델을 만드는데 사용했고 이 모델의 R (결정계수) 제곱 값은 0.80이었다. 나머지 360개의 데이터를 이용하여 만든 모델을 시험한 결과, 추정된 손실 혈액의 비율의 RMS (root mean square) 오차 값은 5.7%가 나왔다. 비록 선형회귀분석모델이 직접적으로 실제 임상에서 사용될 수 없지만 추가적인 연구를 통해 이 방법이 출혈성 쇼크의 소생술을 시행하는데 필요한 용액의 양을 결정하는데 도움을 줄 수 있을 것으로 생각된다.

Keywords

References

  1. World Health Organization, "World Health Statistics 2008," World Health Organization Press, pp. 46-47, 2008.
  2. Deakin CD, Hicks IR. "AB or ABC: pre-hospital fluid management in major trauma," J Accid Emerg Med, Vol. 11, no. 3, pp. 154–157,September 1994. https://doi.org/10.1136/emj.11.3.154
  3. Collins. T, "Understanding Shock". Nurs Stand, Vol. 14, no. 49, pp. 35-41, August 2000.
  4. Wilson M, Davis DP, Coimbra R, "Diagnosis and monitoring of hemorrhagic shock during the initial resuscitation of multiple trauma patients: a review," J Emerg Med, Vol 24, no. 4, pp. 413-422, May 2003. https://doi.org/10.1016/S0736-4679(03)00042-8
  5. Gutierrez G, Reines HD, Wulg-Gutierrez ME, "Clinical review: hemorrhagic shock," Crit Care, Vol 8, no. 5, pp. 373-381, October 2004. https://doi.org/10.1186/cc2851
  6. Yang S, Zou LY, Bounelis P, Chaudry I, ChathamJC, MarchaseRB, "Glucosamine administration during resuscitation improves organ function after trauma hemorrhage," Shock, Vol. 25, no. 6, pp. 600-607, June 2006. https://doi.org/10.1097/01.shk.0000209563.07693.db
  7. Akira Takasu, Toshihisa Sakamoto, Yoshiaki Okada, "Effect of induction rate for mild hypothermia on survival time during uncontrolled hemorrhagic shock in rats." J Trauma, Vol. 61 no. 6, pp. 1330-1335, December 2006. https://doi.org/10.1097/01.ta.0000245980.12711.6a
  8. Chen L, McKenna TM, Reisner AT, Gribok A, Reifman J, "Decision tool for the early diagnosis of trauma patient hypovolemia." J Biomed Inform, Vol 41, no. 3, pp. 469-478, January 2008. https://doi.org/10.1016/j.jbi.2007.12.002
  9. Jefferson MF, Pendleton N, Mohamed S, Kirkman E, Little RA, Lucas SB, Horan MA, "Prediction of hemorrhagic blood loss with a genetic algorithm neural network." J Appl Physiol, Vol. 84, no. 1, pp. 357-361, January 1998. https://doi.org/10.1152/jappl.1998.84.1.357