Intelligent prediction of RBC demand in trauma patients using decision tree methods

Yan-Nan Feng; Zhen-Hua Xu; Jun-Ting Liu; Xiao-Lin Sun; De-Qing Wang; Yang Yu

doi:10.1186/s40779-021-00326-3

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Intelligent prediction of RBC demand in trauma patients using decision tree methods

RESEARCH | Updated：2023-02-28

- Intelligent prediction of RBC demand in trauma patients using decision tree methods
- Intelligent prediction of RBC demand in trauma patients using decision tree methods
- 解放军医学杂志（英文版） 2022年9卷第2期页码：152-163
- Affiliations：
  
  1.Department of Transfusion Medicine, the First Medical Center of Chinese PLA General Hospital, No. 28, Fuxing Rd., Beijing 100853, China
  2.Beijing Hexing Chuanglian Health Technology Co., Ltd., Beijing 100176, China
- Author bio：
  
  * deqingw@vip.sina.com;
  yuyangpla301@163.com
- Funds：
  
  the Key Project-subtopic of the "13th Five-Year Plan" Military Logistics Service Research of China(BWS16J006)
- DOI：10.1186/s40779-021-00326-3
  中图分类号：
- 纸质出版：2022-04
- Accepted：
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Intelligent prediction of RBC demand in trauma patients using decision tree methods[J]. 解放军医学杂志（英文版）, 2022,9(2):152-163.

Feng et al.: Intelligent prediction of RBC demand in trauma patients using decision tree methods. Mil Med Res, 2021, 8: 33.
Intelligent prediction of RBC demand in trauma patients using decision tree methods[J]. 解放军医学杂志（英文版）, 2022,9(2):152-163. DOI： 10.1186/s40779-021-00326-3.

Feng et al.: Intelligent prediction of RBC demand in trauma patients using decision tree methods. Mil Med Res, 2021, 8: 33. DOI： 10.1186/s40779-021-00326-3.

摘要

Abstract

Background:

The vital signs of trauma patients are complex and changeable

and the prediction of blood transfusion demand mainly depends on doctors’ experience and trauma scoring system; therefore

it cannot be accurately predicted. In this study

a machine learning decision tree algorithm [classification and regression tree (CRT) and eXtreme gradient boosting (XGBoost)] was proposed for the demand prediction of traumatic blood transfusion to provide technical support for doctors.

Methods:

A total of 1371 trauma patients who were diverted to the Emergency Department of the First Medical Center of Chinese PLA General Hospital from January 2014 to January 2018 were collected from an emergency trauma database. The vital signs

laboratory examination parameters and blood transfusion volume were used as variables

and the non-invasive parameters and all (non-invasive + invasive) parameters were used to construct an intelligent prediction model for red blood cell (RBC) demand by logistic regression (LR)

CRT and XGBoost. The prediction accuracy of the model was compared with the area under curve (AUC).

Results:

For non-invasive parameters

the LR method was the best

with an AUC of 0.72 [95% confidence interval (CI) 0.657–0.775]

which was higher than the CRT (AUC 0.69

95%CI 0.633–0.751) and the XGBoost (AUC 0.71

95%CI 0.654–0.756)(

0.05). The trauma location and shock index are important prediction parameters. For all the prediction parameters

XGBoost was the best

with an AUC of 0.94 (95%CI 0.893–0.981)

which was higher than the LR (AUC 0.80

95%CI 0.744–0.850) and the CRT (AUC 0.82

95%CI 0.779–0.853)(

0.05). Haematocrit (Hct) is an important prediction parameter.

Conclusions:

The classification performance of the intelligent prediction model of red blood cell transfusion in trauma patients constructed by the decision tree algorithm is not inferior to that of the traditional LR method. It can be used as a technical support to assist doctors to make rapid and accurate blood transfusion decisions in emergency rescue environment

so as to improve the success rate of patient treatment.

关键词

Keywords

references

GBD 2016 Causes of Death Collaborators . Global, regional, and national agesex specific mortality for 264 causes of death, 1980–2016: A systematic analysis for the Global Burden of Disease Study 2016 . Lancet . 2017 ; 90 ( 10100 ): 1151 - 210 .

Demetriades D , Murray J , Charalambides K , Alo K , Velmahos G , Rhee P , et al . Trauma fatalities: time and location of hospital deaths . J Am Coll Surg . 2004 ; 198 ( 1 ): 20 - 6 . https://doi.org/10.1016/j.jamcollsurg.2003.09.003 https://doi.org/10.1016/j.jamcollsurg.2003.09.003 .

Scerbo MH , Holcomb JB , Taub E , Gates K , Love JD , Wade CE , et al . The trauma center is too late: major limb trauma without a pre-hospital tourniquet has increased death from hemorrhagic shock . J Trauma Acute Care Surg . 2017 ; 83 ( 6 ): 1165 - 72 . https://doi.org/10.1097/TA.0000000000001666 https://doi.org/10.1097/TA.0000000000001666 .

Eastridge BJ , Mabry RL , Seguin P , Cantrell J , Tops T , Uribe P , et al . Death on the battlefield (2001-2011): implications for the future of combat casualty care . J Trauma Acute Care Surg . 2012 ; 73 ( 6 Suppl 5 ): S431 - 7 . https://doi.org/10.1097/TA.0b013e3182755dcc https://doi.org/10.1097/TA.0b013e3182755dcc .

Spahn DR , Bouillon B , Cerny V , Duranteau J , Filipescu D , Hunt BJ , et al . The European guideline on management of major bleeding and coagulopathy following trauma: fifth edition . Crit Care . 2019 ; 23 ( 1 ): 98 . https://doi.org/10.1186/s13054-019-2347-3 https://doi.org/10.1186/s13054-019-2347-3 .

Shackelford SA , Del Junco DJ , Powell-Dunford N , Mazuchowski EL , Howard JT , Kotwal RS , et al . Association of prehospital blood product transfusion during medical evacuation of combat casualties in Afghanistan with acute and 30-day survival . JAMA . 2017 ; 318 ( 16 ): 1581 - 91 . https://doi.org/10.1001/jama.2017.15097 https://doi.org/10.1001/jama.2017.15097 .

Croce MA , Tolley EA , Claridge JA , Fabian TC . Transfusions result in pulmonary morbidity and death after a moderate degree of injury . J Trauma . 2005 ; 59 ( 1 ): 19–23; discussion 23-4 . https://doi.org/10.1097/01.TA.0000171459.21450.DC https://doi.org/10.1097/01.TA.0000171459.21450.DC .

Marik PE , Corwin HL . Efficacy of red blood cell transfusion in the critically ill: a systematic review of the literature . Crit Care Med . 2008 ; 36 ( 9 ): 2667 - 74 . https://doi.org/10.1097/CCM.0b013e3181844677 https://doi.org/10.1097/CCM.0b013e3181844677 .

Malone DL , Dunne J , Tracy JK , Putnam AT , Scalea TM , Napolitano LM . Blood transfusion, independent of shock severity, is associated with worse outcome in trauma . J Trauma . 2003 ; 54 ( 5 ): 898–905; discussion 905-7 . https://doi.org/10.1097/01.TA.0000060261.10597.5C https://doi.org/10.1097/01.TA.0000060261.10597.5C .

Kotwal RS , Howard JT , Orman JA , Tarpey BW , Bailey JA , Champion HR , et al . The effect of a golden hour policy on the morbidity and mortality of combat casualties . JAMA Surg . 2016 ; 151 ( 1 ): 15 - 24 . https://doi.org/10.1001/jamasurg.2015.3104 https://doi.org/10.1001/jamasurg.2015.3104 .

Nunez TC , Voskresensky IV , Dossett LA , Shinall R , Dutton WD , Cotton BA . Early prediction of massive transfusion in trauma: simple as ABC (assessment of blood consumption) . J Trauma . 2009 ; 66 ( 2 ): 346 - 52 . https://doi.org/10.1097/TA.0b013e3181961c35 https://doi.org/10.1097/TA.0b013e3181961c35 .

Yücel N , Lefering R , Maegele M , Vorweg M , Tjardes T , Ruchholtz S , et al . Trauma associated severe hemorrhage (TASH)-score: probability of mass transfusion as surrogate for life threatening hemorrhage after multiple trauma . J Trauma . 2006 ; 60 ( 6 ): 1228–36; discussion 1236-7 . https://doi.org/10.1097/01.ta.0000220386. 84012.bf https://doi.org/10.1097/01.ta.0000220386. 84012.bf .

Schreiber MA , Perkins J , Kiraly L , Underwood S , Wade C , Holcomb JB . Early predictors of massive transfusion in combat casualties . J Am Coll Surg . 2007 ; 205 ( 4 ): 541 - 5 . https://doi.org/10.1016/j.jamcollsurg.2007.05.007 https://doi.org/10.1016/j.jamcollsurg.2007.05.007 .

Maegele M . Challenges to improving patient outcome following massive transfusion in severe trauma . Expert Rev Hematol . 2020 ; 13 ( 4 ): 323 - 30 . https://doi.org/10.1080/17474086.2020.1733404 https://doi.org/10.1080/17474086.2020.1733404 .

Leal-Noval SR , Rincón-Ferrari MD , Múñoz-Gómez M . Red blood cell transfusion may be more detrimental than anemia for the clinical outcome of patients with severe traumatic brain injury . Crit Care . 2019 ; 23 ( 1 ): 189 . https://doi.org/10.1186/s13054-019-2470-1 https://doi.org/10.1186/s13054-019-2470-1 .

Chang R , Cardenas JC , Wade CE , Holcomb JB . Advances in the understanding of trauma-induced coagulopathy . Blood . 2016 ; 128 ( 8 ): 1043 - 9 . https://doi.org/10.1182/blood-2016-01-636423 https://doi.org/10.1182/blood-2016-01-636423 .

Zhao YZ , Wang JM , Pan F , Li PY , Jia LJ , Li KY , et al . Pilot research: construction of emergency rescue database . Chin Crit Care Med . 2018 ; 30 ( 6 ): 609 - 12 .

Li K , Wu H , Pan F , Chen L , Feng C , Liu Y , et al . A machine learning-based model to predict acute traumatic coagulopathy in trauma patients upon emergency hospitalization . Clin Appl Thromb Hemost . 2020 ; 26 : 1076029619897827 .

Chen LF , Li H , Zhuang Y , Luo Q , Yu Y , Pan JC , et al . Application and construction of a clinical transfusion intelligent management and evaluation system . Chin J Blood Transfus . 2015 ; 28 ( 9 ): 1167 - 73 .

Zhang S , Hu Z , Ye L , Zheng Y . Application of logistic regression and decision tree analysis in prediction of acute myocardial infarction events . Zhejiang Da Xue Xue Bao Yi Xue Ban . 2019 ; 48 ( 6 ): 594 - 602 .

El-Menyar A , Mekkodathil A , Abdelrahman H , Latifi R , Galwankar S , Al-Thani H , et al . Review of existing scoring systems for massive blood transfusion in trauma patients: where do we stand . Shock . 2019 ; 52 ( 3 ): 288 - 99 . https://doi.org/10.1097/SHK.0000000000001359 https://doi.org/10.1097/SHK.0000000000001359 .

Galvagno SM Jr , Hu P , Yang S , Gao C , Hanna D , Shackelford S , et al . Accuracy of continuous noninvasive hemoglobin monitoring for the prediction of blood transfusions in trauma patients . J Clin Monit Comput . 2015 ; 29 ( 6 ): 815 - 21 . https://doi.org/10.1007/s10877-015-9671-1 https://doi.org/10.1007/s10877-015-9671-1 .

El-Menyar A , Goyal P , Tilley E , Latifi R . The clinical utility of shock index to predict the need for blood transfusion and outcomes in trauma . J Surg Res . 2018 ; 227 : 52 - 9 . https://doi.org/10.1016/j.jss.2018.02.013 https://doi.org/10.1016/j.jss.2018.02.013 .

Schroll R , Swift D , Tatum D , Couch S , Heaney JB , Llado-Farrulla M , et al . Accuracy of shock index versus ABC score to predict need for massive transfusion in trauma patients . Injury . 2018 ; 49 ( 1 ): 15 - 9 . https://doi.org/10.1016/j.injury.2017.09.015 https://doi.org/10.1016/j.injury.2017.09.015 .

Rainer TH , Ho AMH , Yeung JHH , Cheung NK , Wong RSM , Tang N , et al . Early risk stratification of patients with major trauma requiring massive blood transfusion . Resuscitation . 2011 ; 82 ( 6 ): 724 - 9 . https://doi.org/10.1016/j.resuscitation.2011.02.016 https://doi.org/10.1016/j.resuscitation.2011.02.016 .

Thorup L , Koch KU , Upton RN , Østergaard L , Rasmussen M . Effects of vasopressors on cerebral circulation and oxygenation: a narrative review of pharmacodynamics in health and traumatic brain injury . J Neurosurg Anesthesiol . 2020 ; 32 ( 1 ): 18 - 28 . https://doi.org/10.1097/ANA.0000000000000596 https://doi.org/10.1097/ANA.0000000000000596 .

Levy JH , Welsby I , Goodnough LT . Fibrinogen as a therapeutic target for bleeding: a review of critical levels and replacement therapy . Transfusion . 2014 ; 54 ( 5 ): 1389–405; quiz 1388 . https://doi.org/10.1111/trf.12431 https://doi.org/10.1111/trf.12431 .

Halldorsdottir HD , Eriksson J , Persson BP , Herwald H , Lindbom L , Weitzberg E , et al . Heparin-binding protein as a biomarker of post-injury sepsis in trauma patients . Acta Anaesthesiol Scand . 2018 ; 62 ( 7 ): 962 - 73 . https://doi.org/10.1111/aas.13107 https://doi.org/10.1111/aas.13107 .

Esteva A , Kuprel B , Novoa RA , Ko J , Swetter SM , Blau HM , et al . Dermatologist-level classification of skin cancer with deep neural networks . Nature . 2017 ; 542 ( 7639 ): 115 - 8 . https://doi.org/10.1038/nature21056 https://doi.org/10.1038/nature21056 .

Liang H , Tsui BY , Ni H , Valentim CCS , Baxter SL , Liu G , et al . Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence . Nat Med . 2019 ; 25 ( 3 ): 433 - 8 . https://doi.org/10.1038/s41591-018-0335-9 https://doi.org/10.1038/s41591-018-0335-9 .

Chilamkurthy S , Ghosh R , Tanamala S , Biviji M , Campeau NG , Venugopal VK , et al . Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study . Lancet . 2018 ; 392 ( 10162 ): 2388 - 96 . https://doi.org/10.1016/S0140-6736(18)31645-3 https://doi.org/10.1016/S0140-6736(18)31645-3 .

Al'Aref SJ , Maliakal G , Singh G , van Rosendael AR , Ma X , Xu Z , et al . Machine learning of clinical variables and coronary artery calcium scoring for the prediction of obstructive coronary artery disease on coronary computed tomography angiography: analysis from the CONFIRM registry . Eur Heart J . 2020 ; 41 ( 3 ): 359 - 67 . https://doi.org/10.1093/eurheartj/ehz565 https://doi.org/10.1093/eurheartj/ehz565 .

Bertsimas D , Dunn J , Steele DW , Trikalinos TA , Wang Y . Comparison of machine learning optimal classification trees with the pediatric emergency care applied research network head trauma decision rules . JAMA Pediatr . 2019 ; 173 ( 7 ): 648 - 56 . https://doi.org/10.1001/jamapediatrics.2019.1068 https://doi.org/10.1001/jamapediatrics.2019.1068 .

Ruffle JK , Farmer AD , Aziz Q . Artificial intelligence-assisted gastroenterology - promises and pitfalls . Am J Gastroenterol . 2019 ; 114 ( 3 ): 422 - 8 . https://doi.org/10.1038/s41395-018-0268-4 https://doi.org/10.1038/s41395-018-0268-4 .

Afshar M , Phillips A , Karnik N , Mueller J , To D , Gonzalez R , et al . Natural language processing and machine learning to identify alcohol misuse from the electronic health record in trauma patients: development and internal validation . J Am Med Inform Assoc . 2019 ; 26 ( 3 ): 254 - 61 . https://doi.org/10.1093/jamia/ocy166 https://doi.org/10.1093/jamia/ocy166 .

Hodgman EI , Cripps MW , Mina MJ , Bulger EM , Schreiber MA , Brasel KJ , et al . External validation of a smartphone app model to predict the need for massive transfusion using five different definitions . J Trauma Acute Care Surg . 2018 ; 84 ( 2 ): 397 - 402 . https://doi.org/10.1097/TA.0000000000001756 https://doi.org/10.1097/TA.0000000000001756 .

Seheult JN , Anto VP , Farhat N , Stram MN , Spinella PC , Alarcon L , et al . Application of a recursive partitioning decision tree algorithm for the prediction of massive transfusion in civilian trauma: the MTPitt prediction tool . Transfusion . 2019 ; 59 ( 3 ): 953 - 64 . https://doi.org/10.1111/trf.15078 https://doi.org/10.1111/trf.15078 .

Liu NT , Salinas J . Machine learning for predicting outcomes in trauma . Shock . 2017 ; 48 ( 5 ): 504 - 10 . https://doi.org/10.1097/SHK.0000000000000898 https://doi.org/10.1097/SHK.0000000000000898 .

Rau CS , Wu SC , Chuang JF , Huang CY , Liu HT , Chien PC , et al . Machine learning models of survival prediction in trauma patients . J Clin Med . 2019 ; 8 ( 6 ): 799 . https://doi.org/10.3390/jcm8060799 https://doi.org/10.3390/jcm8060799 .

Xu Y , Ju L , Tong J , Zhou CM , Yang JJ . Machine learning algorithms for predicting the recurrence of stage IV colorectal cancer after tumor resection . Sci Rep . 2020 ; 10 ( 1 ): 2519 . https://doi.org/10.1038/s41598-020-59115-y https://doi.org/10.1038/s41598-020-59115-y .

Lu Y , Fu X , Chen F , Wong KKL . Prediction of fetal weight at varying gestational age in the absence of ultrasound examination using ensemble learning . Artif Intell Med . 2020 ; 102 : 101748 . https://doi.org/10.1016/j.artmed.2019.101748 https://doi.org/10.1016/j.artmed.2019.101748 .

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