Construction of a risk prediction model for vasovagal reactions in whole-blood donors: A case-control cross-sectional...

China faces a tight balance between clinical blood supply and demand. Vasovagal reactions (VVRs) hinder donor recruitment and retention. This study aimed to develop and validate a predictive model for the early identification of high-risk donors to guide targeted interventions.

This unmatched case-control study enrolled whole-blood donors from a blood station in Xinjiang, China. VVR was defined according to the International Society of Blood Transfusion (ISBT) criteria for donation-related complications. Significant predictors identified by multivariable logistic regression were used to develop a prediction model. Model performance and calibration were evaluated using receiver operating characteristic (ROC) analysis and the Hosmer-Lemeshow (H-L) test.

A total of 319 donors experienced VVRs, and 1276 served as controls. The final prediction model incorporated nine significant predictors, categorized as follows-demographic factors: age, pulse, systolic blood pressure (SBP) and body mass index (BMI); psychological factors: perceived stress level; and behavioural factors: number of donations, regular exercise, postprandial interval (≥4 h) and average sleep duration. The model demonstrated good discrimination, with areas under the ROC curve of 0.830 and 0.827 in the modelling and validation sets, respectively. Good calibration was indicated by H-L test results (modelling set: χ² = 13.817, p = 0.129; validation set: χ² = 8.698, p = 0.466).

The constructed prediction model is effective, providing a reference for blood station staff to assess the risk of VVRs in whole-blood donors and implement early interventions.