孕妇自然分娩产后出血的风险列线图预测模型构建与验证

高怡平; 水杰; 张晓燕

doi:10.13898/j.cnki.issn.1000-2200.2024.02.021

孕妇自然分娩产后出血的风险列线图预测模型构建与验证

Construction and validation of a risk nomogram prediction model for postpartum hemorrhage in pregnant women during spontaneous delivery

摘要

摘要:
目的探讨孕妇自然分娩产后出血(PPH)的危险因素，并建立风险列线图预测模型。
方法收集在产科进行产检并最终阴道分娩的3 067例产妇的临床信息。以产妇分娩后24 h内是否发生PPH分为PPH组(n=229)和非PPH组(n=2 838)。对2组的临床信息进行比较，使用logistic回归分析筛选孕妇自然分娩PPH的危险因素并构建预测模型。用ROC曲线下面积(AUC)、灵敏度、特异度评价模型效能，用Bootstrap自抽样法对模型进行内部验证。
结果PPH组与非PPH组的年龄、多胎、妊娠期高血压疾病、临产前D-二聚体、临产前Fib、第三产程时间、胎盘粘连、胎儿体质量指标的比较差异均有统计学意义(P < 0.01)。多因素logistic回归分析显示，年龄高、多胎、有妊娠期高血压疾病、临产前D-二聚体升高、第三产程时间长、有胎盘粘连、胎儿体质量大均是孕妇自然分娩发生PPH的危险因素(P < 0.01)，临产前Fib升高为孕妇自然分娩发生PPH的保护因素(P < 0.01)。利用上述8个指标构建列线图预测模型并验证，发现模型预测孕妇自然分娩PPH的AUC为0.824、灵敏度为0.819、特异度为0.715。经Bootstrap法自1 000次抽样对模型验证发现校准曲线的MAE为0.004，表明模型在预测孕妇自然分娩PPH发生风险与实际发生具有良好的一致性。
结论临床整合孕妇的年龄、胎数、妊娠期高血压疾病、临产前D-二聚体、临产前Fib、第三产程时间、胎盘粘连、胎儿体质量指标构建预测模型，可提高评估孕妇自然分娩PPH的准确性。

Abstract:
ObjectiveTo explore the risk factors for postpartum hemorrhage (PPH) in pregnant women during spontaneous delivery, and to establish a risk nomogram prediction model.
MethodsThe clinical information of 3 067 parturients who underwent an obstetric examination and eventually delivered vaginally in the department of obstetrics was collected.According to the occurence of PPH within 24 hours after delivery, the parturients were divided into PPH group (n=229) and non-PPH group (n=2 838).The clinical information of the two groups was compared, and logistic regression analysis was used to screen the risk factors for PPH in spontaneous delivery and a prediction model was established.The area under the ROC curve, sensitivity and specificity were used to evaluate the performance of the model, and Bootstrap self-sampling method was used to verify the model internally.
ResultsThere were significant differences between PPH group and non-PPH group in age, multiple pregnancy, pregnancy induced hypertension, prenatal D-dimer, prenatal Fib, the third stage of labor, placental adhesion, and fetal body mass (P < 0.01).Multivariate logistic regression analysis showed that high age, multiple births, pregnancy induced hypertension, pre-partum D-dimer elevation, long third stage of labor, placenta adhesion, and large fetal body mass were all risk factors for PPH in spontaneous delivery of pregnant women (P < 0.01), and pre-partum Fib elevation was a protective factor for PPH in spontaneousl delivery of pregnant women (P < 0.01).By using the above 8 indicators to construct and verify the nomogram prediction model, it was found that the AUC of the model was 0.824, the sensitivity was 0.819, and the specificity was 0.715 for predicting PPH in spontaneous delivery of pregnant women.After 1 000 samples of Bootstrap method, the MAE of the calibration curve was 0.004, which showed that the model had a good consistency in predicting the risk of spontaneous delivery PPH in pregnant women and the actual occurrence.
ConclusionsThe clinical integration of pregnant women's age, number of fetuses, pregnancy-induced hypertension, pre-partum D-dimer, pre-partum Fib, the third stage of labor, placental adhesions, and fetal weight indicators are used to construct a prediction model that can improve the accuracy of evaluating pregnant women's spontaneous delivery PPH.

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