Peking University People's Hospital, Peking University Institute of Hematology; National Clinical Research Center for Hematologic Disease; Collaborative Innovation Center of Hematology; Beijing Key Laboratory of Hematopoietic Stem Cell Transplantation, Beijing, China.
Department of Obstetrics and Gynecology, Peking University People's Hospital, Beijing, China.
Am J Hematol. 2021 May 1;96(5):561-570. doi: 10.1002/ajh.26134. Epub 2021 Mar 7.
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.
全球范围内，产后出血（PPH）是导致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。早期识别 PPH 有助于预防不良结局，但由于临床医生没有工具来预测 ITP 女性的 PPH，因此这种方法并未得到广泛应用。因此，我们进行了一项全国性多中心回顾性研究，旨在为 ITP 患者开发和验证 PPH 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 家学术性三级中心的 432 例原发性 ITP 孕妇（677 例妊娠）。共有 157 例（23.2%）妊娠发生了 PPH。推导队列纳入了 450 例妊娠。在验证队列中，我们纳入了 117 例妊娠进行时间验证，110 例妊娠进行地理验证。我们评估了 25 个临床参数作为候选预测指标，并使用多变量逻辑回归来建立预测模型。最终模型纳入了 7 个变量，命名为 MONITOR（产妇并发症、WHO 出血评分、产前血小板输注、胎盘异常、血小板计数、既往子宫手术和初产妇）。我们基于这 7 个危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），在时间验证中的 AUC 为 0.869（95%CI 0.802-0.937），在地理验证中的 AUC 为 0.811（95%CI 0.713-0.908）。校准图显示，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均具有良好的一致性。因此，我们开发并验证了一种非常准确的 PPH 预测模型。我们希望该模型能够为更精确的临床护理、降低不良结局和更好的医疗保健资源分配做出贡献。
