摘要
目的探讨多个临床指标预测卵巢储备功能的效果。方法回顾性分析350例行体外受精-胚胎移植患者的临床资料,应用SPSS软件作ROC曲线,分别计算年龄、基础卵泡刺激素(bFSH)、基础卵泡刺激素/基础黄体生成素(bFSH/bLH)、窦卵泡计数(AFC)4个指标曲线下面积(AUC),并用Logistic回归分析及ROC曲线,综合多个指标对卵巢储备功能预测。结果 (1)年龄、bFSH、bFSH/bLH及AFC的AUC分别为0.687、0.643、0.815及0.867,均大于机会参考下面积,差异有统计学意义(P<0.05);(2)Logistic回归结果显示,综合年龄、bFSH、bFSH/bLH及AFC 4个指标的ROC曲线下AUC是0.918,差异有统计学意义(P<0.05);排除年龄的影响,综合bFSH、bFSH/bLH及AFC 3个指标的ROC曲线下AUC是0.913,差异有统计学意义。结论 AFC是预测卵巢储备功能价值较高的单个指标。排除年龄因素的干扰,综合bFSH、bFSH/bLH和AFC 3个指标较单一指标更能准确地预测卵巢储备功能。
Objective To compare the predictive value of multiple predictors and single one on ovarian reserve function.Methods Totally350females who had undergone in-vitro fertilization(IVF)were retrospectively analyzed.receiver operating characteristic curve(ROC)was applied to calculate the area under curves(AUC)of age,serum basal follicle-stimulating hormone(bFSH),bFSH/bLH ratio and antral follicle count(AFC).As well as,the AUC of comprehensive multiple predictors.Logistic regression analysis and ROC curve were used to analyze multiple indexes for prediction of ovarian reserve function.Results The AUC of age,bFSH,bFSH/bLH and AFC was0.687,0.643,0.815and0.867respectively,which was statistically larger than the reference area(P<0.05).Logistic regression model showed that the combination of age,AFC,bFSH and bFSH/bLH improved the predictive function for ovarian reserve and had the AUC of0.918(P<0.05).The AUC of the combination of AFC,bFSH and bFSH/bLH was0.913after elimination of the affect of age(P<0.05).Conclusions AFC is the strongest single predictor for ovarian reserve function.A multivariable modle has higher value than a single predictor on the predictive power and improve the veracity.
作者
莫凤媚
Feng-mei Mo(Department of Reprodutive Medcine Center, the Third Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi 530031, China)
出处
《中国现代医学杂志》
CAS
2018年第1期74-77,共4页
China Journal of Modern Medicine
基金
广西卫生计生委自筹经费科研课题(No:Z2010127)
关键词
多变量
卵巢储备功能
ROC曲线
Logistic回归分析
multivariable modle, ovarian reserve function, receiver operating characteristic curve, logistic regression analysis