• Jieun Kang, Hye Sim Kim, Eun Bi Lee, Young Uh, Kyoung Hee Han, Eun Young Park, Hyang Ah Lee, Dae Ryong Kang, In Bai Chung, and Seong Jin Choi.
• Department of Obstetrics and Gynecology, Yonsei University Wonju College of Medicine, Wonju, Korea.
• Yonsei Med. J. 2020 Feb 1; 61 (2): 154-160.

PurposeRecently, obstetric massive transfusion protocols have shifted toward early intervention. This study aimed to develop a prediction model for transfusion of ≥5 units of packed red blood cells (PRBCs) during cesarean section in women with placenta previa.Materials And MethodsWe conducted a cohort study including 287 women with placenta previa who delivered between September 2011 and April 2018. Univariate and multivariate logistic regression analyses were used to test the association between clinical factors, ultrasound factors, and massive transfusion. For the external validation set, we obtained data (n=50) from another hospital.ResultsWe formulated a scoring model for predicting transfusion of ≥5 units of PRBCs, including maternal age, degree of previa, grade of lacunae, presence of a hypoechoic layer, and anterior placentation. For example, total score of 223/260 had a probability of 0.7 for massive transfusion. Hosmer-Lemeshow goodness-of-fit test indicated that the model was suitable (p>0.05). The area under the receiver operating characteristics curve (AUC) was 0.922 [95% confidence interval (CI) 0.89-0.95]. In external validation, the discrimination was good, with an AUC value of 0.833 (95% CI 0.70-0.92) for this model. Nomogram calibration plots indicated good agreement between the predicted and observed outcomes, exhibiting close approximation between the predicted and observed probability.ConclusionWe constructed a scoring model for predicting massive transfusion during cesarean section in women with placenta previa. This model may help in determining the need to prepare an appropriate amount of blood products and the optimal timing of blood transfusion.© Copyright: Yonsei University College of Medicine 2020.

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