Analysis of Factors Related to Vasovagal Response in Apheresis Blood Donors and the Establishment of Prediction Model Based on BP Neural Network Algorithm

Abstract

Objective: To analyze the factors related to vessel vasovagal reaction (VVR) in apheresis donors, establish a mathematical model for predicting the correlation factors and occurrence risk, and use the prediction model to intervene in high-risk VVR blood donors, improve the blood donation experience, and retain blood donors. Methods: A total of 316 blood donors from the Xi’an Central Blood Bank from June to September 2022 were selected to statistically analyze VVR-related factors. A BP neural network prediction model is established with relevant factors as input and DRVR risk as output. Results: First-time blood donors had a high risk of VVR, female risk was high, and sex difference was significant (P value < 0.05). The blood pressure before donation and intergroup differences were also significant (P value < 0.05). After training, the established BP neural network model has a minimum RMS error of 0.116, a correlation coefficient R = 0.75, and a test model accuracy of 66.7%. Conclusion: First-time blood donors, women, and relatively low blood pressure are all high-risk groups for VVR. The BP neural network prediction model established in this paper has certain prediction accuracy and can be used as a means to evaluate the risk degree of clinical blood donors.