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多囊卵巢综合征女性不良新生儿结局预测列线图的建立和验证:一项回顾性研究。

Development and validation of nomograms for predicting adverse neonatal outcomes in women with polycystic ovary syndrome: a retrospective study.

机构信息

Department of Medical Ultrasound, The Central Hospital of Enshi Prefecture, En Shi, China.

Department of Obstetrics and Gynecology, People's Hospital of Henan University, Henan Provincial People's Hospital, Zhengzhou, China.

出版信息

J Obstet Gynaecol. 2022 Aug;42(6):1922-1930. doi: 10.1080/01443615.2022.2054682. Epub 2022 May 23.

Abstract

In our study, we retrospectively enrolled 606 women with newly diagnosed polycystic ovary syndrome. Participants were divided into two cohorts: development cohort ( = 424) and validation cohort ( = 182). Multivariate logistic regression analyses were used to identify predictive indicators, and nomograms were developed and validated. We found that waist hip rate (WHR), testosterone levels, and fasting blood glucose (FBG) levels (WTF) could predict the small for gestational age; BMI, WHR and modified Ferriman-Gallwey Score (BWM) correlated with low Apgar scores; and BMI, WHR, modified Ferriman-Gallwey Score, testosterone levels, and FBG levels (BWMTF) correlated with adverse neonatal outcomes. The BWMTF nomogram was established, revealing perfect discrimination with the area under the receiver operating characteristic curve (AUC) and stratified five-fold cross-validation in development cohort (AUC = 0.75, Mean AUC = 0.75) and validation cohort (AUC = 0.68, Mean AUC = 0.75). Calibration plots showed good calibration. We established and validated three models for predicting adverse perinatal effects to guide preventive treatment protocols. Impact statement Many studies have identified a large number of predictors, but also lack a comprehensively quantified tool to predict adverse neonatal outcomes in women with PCOS to guide the development of clinical treatment programs. This article screened the high risks factors of adverse neonatal outcomes in women with PCOS, and three nomograms were established and validated. Also, the area under the receiver operating characteristic curve (AUC) and stratified five-fold cross-validation in development cohort and validation cohort showed good discrimination; Calibration plots showed good calibration. Our scoring system could help clinicians evaluate these risks and conduct proper screening, prevention, and management to ameliorate the risk of neonatal disease in these patients.

摘要

在我们的研究中,我们回顾性地招募了 606 名新诊断为多囊卵巢综合征的女性。参与者分为两个队列:发展队列(n=424)和验证队列(n=182)。多变量逻辑回归分析用于识别预测指标,并开发和验证了列线图。我们发现腰臀比(WHR)、睾酮水平和空腹血糖(FBG)水平(WTF)可以预测胎儿小于胎龄;BMI、WHR 和改良的 Ferriman-Gallwey 评分(BWM)与低 Apgar 评分相关;BMI、WHR、改良的 Ferriman-Gallwey 评分、睾酮水平和 FBG 水平(BWMTF)与不良新生儿结局相关。建立了 BWMTF 列线图,在发展队列和验证队列中,ROC 曲线下面积(AUC)和分层五折交叉验证的区分度均为完美(AUC=0.75,平均 AUC=0.75)和验证队列(AUC=0.68,平均 AUC=0.75)。校准图显示良好的校准度。我们建立并验证了三个预测不良围产期影响的模型,以指导预防治疗方案。

影响陈述

许多研究已经确定了大量的预测指标,但也缺乏一种全面量化的工具来预测 PCOS 女性不良新生儿结局,以指导临床治疗方案的制定。本文筛选了 PCOS 女性不良新生儿结局的高危因素,建立并验证了三个列线图,同时,发展队列和验证队列的 ROC 曲线下面积(AUC)和分层五折交叉验证显示出良好的区分度;校准图显示出良好的校准度。我们的评分系统可以帮助临床医生评估这些风险,并进行适当的筛查、预防和管理,以改善这些患者的新生儿疾病风险。

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