Cheng Chen1, Xiaoyan Liu1, Dan Chen1, Song Huang1, Xiaoli Yan1, Heying Liu1, Qing Chang2, Zhiqing Liang3. 1. Department of Gynecology and Obstetrics, the First Affiliated Hospital, Army, Military Medical University, Chongqing 400038, China. 2. Department of Gynecology and Obstetrics, the First Affiliated Hospital, Army, Military Medical University, Chongqing 400038, China. cqli99@126.com. 3. Department of Gynecology and Obstetrics, the First Affiliated Hospital, Army, Military Medical University, Chongqing 400038, China. zhi_lzliang@163.com.
Abstract
BACKGROUND: The study aimed to establish a predictive risk model for severe postpartum hemorrhage in placenta previa using clinical and placental ultrasound imaging performed prior to delivery. METHODS: Postpartum hemorrhage patients were retrospectively enrolled. Severe postpartum hemorrhage was defined as exceeding 1,500 mL. Data collected included clinical and placental ultrasound images. RESULTS: Age of pregnancy, time of delivery, time of miscarriage, history of vaginal delivery, gestational weeks at pregnancy termination, depth of placenta invading the uterine muscle wall were independent risk factors for severe postpartum hemorrhage in placenta previa. A model to predict severe postpartum hemorrhage in placenta previa was established: P=Log(Y/1-Y), where Y =-6.942 + 0.075 X1 (age) +1.531 X2 (times of delivery) + 0.223 X3 (time of miscarriage) - 3.557X4 (vaginal delivery: 1 for yes, 0 for no) + 1.753 X5 (0 for <37 weeks, 1 for ≥37 weeks) + 1.574 X6 (Depth of placenta invading uterine muscle wall: 0 for normal, 1 for placenta adhesion, 2 for placenta implantation, 3 for placenta penetration); discriminant boundary value of the prediction model (probability: P) was 0.268. Predicting sensitivity (Se) =0.765 (negative predicting accuracy rate), specificity (Sp) =0.900 (positive predicting accuracy rate), total accuracy rate =0.8000, and AUC of ROC curve =0.840. CONCLUSIONS: The risk prediction model which had clinical and ultrasound imaging information prior to delivery had a high decision accuracy. However, before it can be used in the clinic, multicenter large-sample clinical studies should be performed to verify its accuracy and reliability.
BACKGROUND: The study aimed to establish a predictive risk model for severe postpartum hemorrhage in placenta previa using clinical and placental ultrasound imaging performed prior to delivery. METHODS: Postpartum hemorrhagepatients were retrospectively enrolled. Severe postpartum hemorrhage was defined as exceeding 1,500 mL. Data collected included clinical and placental ultrasound images. RESULTS: Age of pregnancy, time of delivery, time of miscarriage, history of vaginal delivery, gestational weeks at pregnancy termination, depth of placenta invading the uterine muscle wall were independent risk factors for severe postpartum hemorrhage in placenta previa. A model to predict severe postpartum hemorrhage in placenta previa was established: P=Log(Y/1-Y), where Y =-6.942 + 0.075 X1 (age) +1.531 X2 (times of delivery) + 0.223 X3 (time of miscarriage) - 3.557X4 (vaginal delivery: 1 for yes, 0 for no) + 1.753 X5 (0 for <37 weeks, 1 for ≥37 weeks) + 1.574 X6 (Depth of placenta invading uterine muscle wall: 0 for normal, 1 for placenta adhesion, 2 for placenta implantation, 3 for placenta penetration); discriminant boundary value of the prediction model (probability: P) was 0.268. Predicting sensitivity (Se) =0.765 (negative predicting accuracy rate), specificity (Sp) =0.900 (positive predicting accuracy rate), total accuracy rate =0.8000, and AUC of ROC curve =0.840. CONCLUSIONS: The risk prediction model which had clinical and ultrasound imaging information prior to delivery had a high decision accuracy. However, before it can be used in the clinic, multicenter large-sample clinical studies should be performed to verify its accuracy and reliability.