Prediction of postpartum hemorrhage in pregnant women with immune thrombocytopenia: Development and validation of the MONITOR model in a nationwide multicenter study
Prediction of postpartum hemorrhage in pregnant women with immune thrombocytopenia: Development and validation of the MONITOR model in a nationwide multicenter study
复制标题
免疫性血小板减少症孕妇产后出血的预测：全国多中心研究中 MONITOR 模型的开发和验证
DOI：
10.1002/ajh.26134
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发表时间：
2021-03-07
影响因子：
12.8
通讯作者：
Zhang, Xiao-Hui
中科院分区：
文献类型：
--
作者：
Huang, Qiu-Sha;Zhu, Xiao-Lu;Zhang, Xiao-Hui
在全球范围内，产后出血（PPH）是孕产妇死亡的主要原因。患有免疫性血小板减少症（ITP）的女性发生PPH的风险增加。早期发现PPH有助于预防不良结局，但由于临床医生没有预测ITP女性PPH的工具，因此未得到充分利用。因此，我们进行了一项全国性的多中心回顾性研究，以开发和验证ITP患者PPH的预测模型。我们纳入了2008年1月至2018年8月期间来自中国18个三级学术中心的432例原发性ITP孕妇（677例妊娠）。共有157例（23.2%）妊娠发生PPH。衍生队列包括450例妊娠。对于验证队列，我们在时间验证队列中纳入了117例妊娠，在地理验证队列中纳入了110例妊娠。我们评估了25个临床参数作为候选预测因子，并使用多变量逻辑回归来开发我们的预测模型。最终模型包括7个变量，命名为MONITOR（母体并发症、WHO出血评分、产前血小板输注、胎盘异常、血小板计数、既往子宫手术和产次）。我们根据7个风险因素建立了易于使用的PPH风险热图和风险评分。我们使用时间验证队列和地理验证队列对该模型进行了外部验证。MONITOR模型在内部验证中的AUC为0.868（95% CI 0.828-0.909），在时间验证中为0.869（95% CI 0.802-0.937），在地理验证中为0.811（95% CI 0.713-0.908）。校准图表明，在内部验证和外部验证中，MONITOR预测概率与实际观察结果之间具有良好的一致性。因此，我们开发并验证了一个非常准确的PPH预测模型。我们希望该模型将有助于更精确的临床护理，减少不良后果，并更好地分配医疗资源。
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 January 2008 to August 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.