A risk prediction model of perinatal blood transfusion for patients who underwent cesarean section: a case control study - Yao Wang; Juan Xiao; Fanzhen Hong (BMC, 2022)
Abstract Background Severe obstetric hemorrhage is a leading cause of severe maternal morbidity. A perinatal blood transfusion is the key factor in the treatment of severe obstetric hemorrhage. Our aim is to identify patients with a high risk of perinatal blood transfusions before Cesarean Section, which can promote the effectiveness of the treatment of severe obstetric hemorrhage, as well as improve obstetric preparations. Methods This study retrospectively analyzed the data of 71 perinatal blood transfusion patients and 170 controls, who were both underwent Cesarean Section from July 2018 to September 2019. These data were included in the training set to build the risk prediction model of needing blood transfusion. Additionally, the data of 148 patients with the same protocol from October 2019 to May 2020 were included in the validation set for model validation. A multivariable logistic regression model was used. A risk prediction nomogram was formulated per the results of the multivariate analysis. Results The strongest risk factors for perinatal blood transfusions included preeclampsia (OR = 6.876, 95% CI: 2.226-23.964), abnormal placentation (OR = 5.480, 95% CI: 2.478-12.591), maternal age (OR = 1.087, 95% CI: 1.016-1.166), predelivery hemoglobin (OR = 0.973, 95% CI: 0.948-0.998) and predelivery fibrinogen (OR = 0.479, 95% CI: 0.290-0.759). A risk prediction model of perinatal blood transfusions for cesarean sections was developed (AUC = 0.819; sensitivity: 0.735; specificity: 0.848; critical value: 0.287). Conclusions The risk prediction model can identify the perinatal blood transfusions before Cesarean Section. With the nomogram, the model can be further quantified and visualized, and clinical decision-making can subsequently be further simplified and promoted.