Abstract
BackgroundThe 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.MethodsA 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 the curve (AUC).ResultsFor 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, P < 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, P < 0.05). Haematocrit (Hct) is an important prediction parameter.ConclusionsThe 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.
Highlights
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; it cannot be accurately predicted
There were no significant differences between the transfusion group and the nontransfusion group in sex, height, weight, R, blood oxygen saturation (SpO2), T, C-reactive protein (CRP), IL-6, international standardized ratio (INR), partial pressure of carbon dioxide (PCO2), trauma severity classification and trauma type (P > 0.05) (Table 1)
The results showed that the logistic regression (LR) model with basic information and non-invasive parameters was the best, but the sensitivity of the classification and regression tree (CRT) model was the highest, and the specificity and accuracy of the eXtreme gradient boosting (XGBoost) model were the highest
Summary
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; it cannot be accurately predicted. The study found that blood transfusion products pre-hospital within 15 min or 15 min after injury were associated with 24-h mortality (5.6% vs 20.2%) and 30-day mortality (11.8% vs 22.9%) compared with delayed or non-transfusion [6]. Kotwal et al [10] found that the death rate of the massive blood transfusion group was significantly lower than that of the non-massive blood transfusion group, especially in severe and extremely severe trauma [injury severity score (ISS) > 15]. Blood products should be given early in the pre-hospital transfer to improve the patients’ survival rate after trauma, and other interventions should occur as soon as possible to strictly control the amount of blood transfused
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