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+6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(n=358)和测试集(n=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。
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(n=358)and a testing set(n=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 promoti
作者
胡雪源
谭美玲
黄革
阎萍
陈晓霞
郑明昱
王丹
HU Xueyuan;TAN Meiling;HUANG Ge;YAN Ping;CHEN Xiaoxiai;ZHENG Mingyu;WANG Dan(Department of Obstetrics and Gynecology,First Affiliated Hospital,Army Medical University(Third Military Medical University),Chongqing,400038,China;Department of Pharmacy,First Affiliated Hospital,Army Medical University(Third Military Medical University),Chongqing,400038,China)
出处
《陆军军医大学学报》
CAS
CSCD
北大核心
2023年第23期2476-2484,共9页
Journal of Army Medical University
基金
重庆市自然科学基金(CSTB2022NSCQ-MSX0169)。
关键词
未足月胎膜早破
绒毛膜羊膜炎
机器学习
预测模型
preterm premature rupture of membranes
chorioamnionitis
machine learning
prediction model
