Prediction of postpartum hemorrhage in pregnant women with immune thrombocytopenia: development and validation of the MONITOR model in a nationwide multicenter study.

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Globally, postpartum hemorrhage (PPH) is the leading cause of maternal death. Women with immune thrombocytopenia (ITP) are at increased risk of developing PPH. Early identification of PPH helps to prevent adverse outcomes but is underused because clinicians do not have a tool to predict PPH for women with ITP. We therefore conducted a nationwide multicenter retrospective study to develop and validate a prediction model of PPH in patients with ITP. We included 432 pregnant women (677 pregnancies) with primary ITP from 18 academic tertiary centers in China from Jan 2008 to Aug 2018. A total of 157 (23.2%) pregnancies experienced PPH. The derivation cohort included 450 pregnancies. For the validation cohort, we included 117 pregnancies in the temporal validation cohort and 110 pregnancies in the geographical validation cohort. We assessed 25 clinical parameters as candidate predictors and used multivariable logistic regression to develop our prediction model. The final model included seven variables and was named MONITOR (maternal complication, WHO bleeding score, antepartum platelet transfusion, placental abnormalities, platelet count, previous uterine surgery, and primiparity). We established an easy-to-use risk heatmap and risk score of PPH based on the seven risk factors. We externally validated this model using both a temporal validation cohort and a geographical validation cohort. The MONITOR model had an AUC of 0.868 (95% CI 0.828-0.909) in internal validation, 0.869 (95% CI 0.802-0.937) in the temporal validation, and 0.811 (95% CI 0.713-0.908) in the geographical validation. Calibration plots demonstrated good agreement between MONITOR-predicted probability and actual observation in both internal validation and external validation. Therefore, we developed and validated a very accurate prediction model for PPH. We hope that the model will contribute to more precise clinical care, decreased adverse outcomes, and better health care resource allocation. This article is protected by copyright. All rights reserved.


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Authors: Qiu-Sha Huang, Xiao-Lu Zhu, Qing-Yuan Qu, Xiao Liu, Gao-Chao Zhang, Yan Su, Qi Chen, Feng-Qi Liu, Xue-Yan Sun, Mei-Ying Liang, Yi Liu, Ming Jiang, Hui Liu, Ru Feng, Hong-Xia Yao, Lei Zhang, Shen-Xian Qian, Tong-Hua Yang, Jing-Yu Zhang, Xu-Liang Shen, Lin-Hua Yang, Jian-Da Hu, Ren-Wei Huang, Zhong-Xing Jiang, Jing-Wen Wang, Hong-Yu Zhang, Zhen Xiao, Si-Yan Zhan, Hui-Xin Liu, Ying-Jun Chang, Qian Jiang, Hao Jiang, Jin Lu, Lan-Ping Xu, Xiao-Hong Zhang, Cheng-Hong Yin, Jian-Liu Wang, Xiao-Jun Huang, Xiao-Hui Zhang

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