Construction and effect evaluation of prediction model for red blood cell transfusion requirement in cesarean section based on artificial intelligence

BMC Medical Informatics and Decision Making
Hang Chen1, B. Cao2, Jiangcun Yang3, Rujie He4, Xingqiu Xia4, Xiaowen Zhang2, Weigang Yan2, Xiaodan Liang2, Li Chen1
1School of Computer Science and Technology, Xi’an Jiaotong University, Xi’an, 710049, Shaanxi, China
2Department of Information Service, Shaanxi Provincial People’s Hospital, Xi’an, 710068, Shaanxi, China
3Department of Transfusion Medicine, Shaanxi Provincial People’s Hospital, Xi’an, 710068, Shaanxi, China
4Beijing HealSci Technology Co., Ltd, Beijing, 100022, China

Tóm tắt

Abstract Objectives This study intends to build an artificial intelligence model for obstetric cesarean section surgery to evaluate the intraoperative blood transfusion volume before operation, and compare the model prediction results with the actual results to evaluate the accuracy of the artificial intelligence prediction model for intraoperative red blood cell transfusion in obstetrics. The advantages and disadvantages of intraoperative blood demand and identification of high-risk groups for blood transfusion provide data support and improvement suggestions for the realization of accurate blood management of obstetric cesarean section patients during the perioperative period. Methods Using a machine learning algorithm, an intraoperative blood transfusion prediction model was trained. The differences between the predicted results and the actual results were compared by means of blood transfusion or not, blood transfusion volume, and blood transfusion volume targeting postoperative hemoglobin (Hb). Results Area under curve of the model is 0.89. The accuracy of the model for blood transfusion was 96.85%. The statistical standard for the accuracy of the model blood transfusion volume is the calculation of 1U absolute error, the accuracy rate is 86.56%, and the accuracy rate of the blood transfusion population is 45.00%. In the simulation prediction results, 93.67% of the predicted and actual cases in no blood transfusion surgery; 63.45% of the same predicted blood transfusion in blood transfusion surgery, and only 20.00% of the blood transfusion volume is the same. Conclusions In conclusion, this study used machine learning algorithm to process, analyze and predict the results of a large sample of cesarean section clinical data, and found that the important predictors of blood transfusion during cesarean section included preoperative RBC, surgical method, the site of surgery, coagulation-related indicators, and other factors. At the same time, it was found that the overall accuracy of the AI model was higher than actual blood using. Although the prediction of blood transfusion volume was not well matched with the actual blood using, the model provided a perspective of preoperative identification of high blood transfusion risks. The results can provide good auxiliary decision support for preoperative evaluation of obstetric cesarean section, and then promote the realization of accurate perioperative blood management for obstetric cesarean section patients.

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