Department of Chemical Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
Transfusion. 2021 Feb;61(2):423-434. doi: 10.1111/trf.16216. Epub 2020 Dec 11.
Maternal hemorrhage protocols involve risk screening. These protocols prepare clinicians for potential hemorrhage and transfusion in individual patients. Patient-specific estimation and stratification of risk may improve maternal outcomes.
Prediction models for hemorrhage and transfusion were trained and tested in a data set of 74 variables from 63 973 deliveries (97.6% of the source population of 65 560 deliveries included in a perinatal database from an academic urban delivery center) with sufficient data at pertinent time points: antepartum, peripartum, and postpartum. Hemorrhage and transfusion were present in 6% and 1.6% of deliveries, respectively. Model performance was evaluated with the receiver operating characteristic (ROC), precision-recall curves, and the Hosmer-Lemeshow calibration statistic.
For hemorrhage risk prediction, logistic regression model discrimination showed ROCs of 0.633, 0.643, and 0.661 for the antepartum, peripartum, and postpartum models, respectively. These improve upon the California Maternal Quality Care Collaborative (CMQCC) accuracy of 0.613 for hemorrhage. Predictions of transfusion resulted in ROCs of 0.806, 0.822, and 0.854 for the antepartum, peripartum, and postpartum models, respectively. Previously described and new risk factors were identified. Models were not well calibrated with Hosmer-Lemeshow statistic P values between .001 and .6.
Our models improve on existing risk assessment; however, further enhancement might require the inclusion of more granular, dynamic data. With the goal of increasing translatability, this work was distilled to an online open-source repository, including a form allowing risk factor inputs and outputs of CMQCC risk, alongside our numerical risk estimation and stratification of hemorrhage and transfusion.
产妇出血预案涉及风险筛查。这些预案为临床医生处理个体患者的潜在出血和输血做好准备。针对患者的风险评估和分层可能会改善产妇结局。
在一个来自学术型城市分娩中心围产期数据库的 63560 例分娩中（97.6%的源人群包括在该数据库中）的 74 个变量数据集上训练和测试了出血和输血预测模型，该数据集在相关时间点（产前、产时和产后）具有足够的数据。出血和输血分别占分娩的 6%和 1.6%。使用接收者操作特征（ROC）曲线、精度-召回曲线和 Hosmer-Lemeshow 校准统计来评估模型性能。
对于出血风险预测，逻辑回归模型的判别力在产前、产时和产后模型中分别显示 ROC 为 0.633、0.643 和 0.661。这优于加利福尼亚产妇质量护理协作组织（CMQCC）对出血的准确率 0.613。输血预测的 ROC 分别为产前、产时和产后模型的 0.806、0.822 和 0.854。确定了以前描述和新的风险因素。Hosmer-Lemeshow 统计 P 值在 0.001 至 0.6 之间，模型校准效果不佳。
我们的模型提高了现有的风险评估水平；然而，进一步的改进可能需要包括更细粒度、动态的数据。为了提高可翻译性，这项工作被提炼成一个在线开源存储库，其中包括一个允许输入风险因素和输出 CMQCC 风险的表格，以及我们的出血和输血风险的数值风险估计和分层。
