Construction of a prediction model of histologic chorioamnionitis based on machine learning

Xueyuan HU; Meiling TAN; Ge HUANG; Ping YAN; Xiaoxiai CHEN; Mingyu ZHENG; Dan WANG

doi:10.16016/j.2097-0927.202307052

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Construction of a prediction model of histologic chorioamnionitis based on machine learning

Clinical Medicine | 更新时间：2026-01-09

- Construction of a prediction model of histologic chorioamnionitis based on machine learning
- Journal of Army Medical University Vol. 45, Issue 23, Pages: 2476-2484(2023)
- 作者机构：
  
  1.400038 重庆，陆军军医大学(第三军医大学)第一附属医院妇产科
  2.400038 重庆，陆军军医大学(第三军医大学)第一附属医院药学部
- 作者简介：
- 基金信息：
  
  the Natural Science Foundation of Chongqing(CSTB2022NSCQ-MSX0169)
- DOI：10.16016/j.2097-0927.202307052
  CLC： R319;R711.32;R714.52
- Received：13 July 2023，
  
  Revised：2023-11-01，
  
  Published：15 December 2023
- 稿件说明：
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Xueyuan HU, Meiling TAN, Ge HUANG, et al. Construction of a prediction model of histologic chorioamnionitis based on machine learning[J]. Journal of Army Medical University, 2023, 45(23): 2476-2484.
DOI：

Xueyuan HU, Meiling TAN, Ge HUANG, et al. Construction of a prediction model of histologic chorioamnionitis based on machine learning[J]. Journal of Army Medical University, 2023, 45(23): 2476-2484. DOI： 10.16016/j.2097-0927.202307052.

摘要

目的

开发一项关于未足月胎膜早破(preterm premature rupture of membrane

PPROM)孕妇分娩后发生组织学绒毛膜羊膜炎(histologic chorioamnionitis

HCA)的机器学习预测模型。

方法

选取在我院住院分娩的孕28周~36

周胎膜早破的病例512例。收集研究对象的一般资料、孕产史、实验室数据、超声数据等共31项资料。将所有研究对象按7 ∶3随机分配为训练集(

=358)和测试集(

=154)。使用最小绝对收缩和选择算子算法(the least absolute shrinkage and selection operator

LASSO)算法在训练集中筛选出6项危险因素：生殖道感染、B超胎儿体重、破膜胎龄、使用抗生素前最高体温、既往宫腔操作次数、破膜距抗生素使用的间隔时间纳入模型。基于支持向量机(support vector machine

SVM)、逻辑回归(logistic regression

LR)、多层感知机(multilayer perceptron

MLP)、随机森林(random forest

RF)、极端梯度提升(extreme gradient boosting

XGB)、决策树(decision tree

DT)等机器学习算法绘制受试者工作特征(receiver operating characteristic

ROC)曲线，通过比较曲线下面积(area under curve

AUC)选取最佳机器学习模型。同时对最终模型的预测效益及临床适用性进行评估并计算危险因素的重要性。

结果

6种机器学习算法中SVM模型预测效能最高，在测试集中SVM的AUC为0.862，敏感度为84.0%，特异度为75.0%。其在校准曲线中辨别能力、临床决策(decision curve analysis

DCA)曲线中临床效益均获得良好评价。变量SHAP(SHapley Additive exPlanations)图按重要性依次排列呈现6项危险因素，其中最重要的是生殖道感染。

结论

基于SVM算法成功建立未足月胎膜早破孕产妇组织学绒毛膜羊膜炎的预测模型。该模型对HCA的良好预测能力可以指导临床诊疗，改善不良妊娠结局，促进母婴健康。

Abstract

Objective

To develop a machine learning prediction model for histologic chorioamnionitis (HCA) in patients with preterm premature rupture of membranes (PPROM) after delivery.

Methods

A total of 512 pregnant women who were diagnosed with PPROM at 28~36+6 weeks in our hospital were enrolled in this study. After 31 clinical items consisting of general information

maternal history

laboratory data

and imaging parameters were collected for each subject

they were randomly divided into a training set (

=358) and a testing set (

=154) in a ratio of 7:3. Least absolute shrinkage and selection operator (LASSO) algorithm was used to identify independent predictors in the training set. Based on these factors (reproductive

tract infection

ultrasound fetal weight

gestational age at PPROM

maximum body temperature before antibiotics

number of previous uterine operations

and interval between rupture of membranes and antibiotic use)

6 machine learning models

including support vector machine (SVM)

logistic regression (LR)

multilayer perceptron (MLP)

random forest (RF)

extreme gradient boosting (XGB) and decision tree (DT) were developed. Area under the curve (AUCs) value was used to compare the prediction efficiency of the built models. Then prediction efficiency and clinical applicability of optimal model were evaluated to calculate the importance of risk factors.

Results

Among 6 machine learning algorithms

the SVM model performed best with an accuracy of 0.862

sensitivity of 0.840

and specificity of 0.750 in the testing set. The predicted values derived from the model were highly consistent with the actual situations in discrimination of calibration curve and decision curve analysis. SHapley Additive exPlanations (SHAP) plot prioritized the predictive weights of 6 risk factors

and reproductive tract infection was of most significant importance.

Conclusion

An SVM prediction model of HCA occurrence in PPROM patients is successfully constructed. Its excellent predictive ability contributes to guiding clinical treatment

improving adverse pregnancy outcomes

and promoting maternal and infant health.

关键词

Keywords

references

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