PDF(Pubmed)
Abstract：
Predicting postpartum hemorrhage (PPH) before delivery is crucial for enhancing patient outcomes, enabling timely transfer and implementation of prophylactic therapies. We attempted to utilize machine learning (ML) using basic pre-labor clinical data and laboratory measurements to predict postpartum Hemoglobin (Hb) in non-complicated singleton pregnancies. The local databases of two academic care centers on patient delivery were incorporated into the current study. Patients with preexisting coagulopathy, traumatic cases, and allogenic blood transfusion were excluded from all analyses. The association of pre-delivery variables with 24-h post-delivery hemoglobin level was evaluated using feature selection with Elastic Net regression and Random Forest algorithms. A suite of ML algorithms was employed to predict post-delivery Hb levels. Out of 2051 pregnant women, 1974 were included in the final analysis. After data pre-processing and redundant variable removal, the top predictors selected via feature selection for predicting post-delivery Hb were parity (B: 0.09 [0.05-0.12]), gestational age, pre-delivery hemoglobin (B:0.83 [0.80-0.85]) and fibrinogen levels (B:0.01 [0.01-0.01]), and pre-labor platelet count (B*1000: 0.77 [0.30-1.23]). Among the trained algorithms, artificial neural network provided the most accurate model (Root mean squared error: 0.62), which was subsequently deployed as a web-based calculator: https://predictivecalculators.shinyapps.io/ANN-HB . The current study shows that ML models could be utilized as accurate predictors of indirect measures of PPH and can be readily incorporated into healthcare systems. Further studies with heterogenous population-based samples may further improve the generalizability of these models.
摘要：
分娩前预测产后出血（PPH）对于提高患者预后至关重要。能够及时转移和实施预防性治疗。我们试图利用机器学习（ML），使用基本的产前临床数据和实验室测量来预测非复杂单胎妊娠的产后血红蛋白（Hb）。两个学术护理中心关于病人分娩的本地数据库被纳入本研究。预先存在凝血功能障碍的患者，创伤性病例,和同种异体输血被排除在所有分析之外.使用弹性网络回归和随机森林算法的特征选择评估了分娩前变量与分娩后24小时血红蛋白水平的关联。采用了一套ML算法来预测分娩后的Hb水平。2051名孕妇中,1974年被列入最终分析。经过数据预处理和冗余变量去除后,通过特征选择来预测分娩后Hb的最高预测因子是奇偶校验(B：0.09[0.05-0.12])，胎龄,分娩前血红蛋白（B：0.83[0.80-0.85]）和纤维蛋白原水平（B：0.01[0.01-0.01]），和产前血小板计数（B*1000：0.77[0.30-1.23]）。在经过训练的算法中，人工神经网络提供了最准确的模型(均方根误差：0.62)，它随后被部署为基于Web的计算器：https://predictivecalculators。shinyapps.io/ANN-HB.当前的研究表明，ML模型可以用作PPH间接测量的准确预测因子，并且可以很容易地纳入医疗保健系统。对基于异质群体的样本的进一步研究可能会进一步提高这些模型的泛化性。
