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
- Military Medical Research Vol. 9, Issue 2, Pages: 152-163(2022)
- 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：
- DOI：10.1186/s40779-021-00326-3
  CLC：
- Published：2022-04
- Accepted：
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Feng et al.: Intelligent prediction of RBC demand in trauma patients using decision tree methods. Mil Med Res, 2021, 8: 33.
DOI：

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

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