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作 者:张博雅 张雅宁 牛少欣 苏林 冯静[1] ZHANG Boya;ZHANG Yaning;NIU Shaoxin;SU Lin;FENG Jing(Department of Obstetrics and Gynecology,Hebei General Hospital)
出 处:《中国性科学》2023年第9期90-94,共5页Chinese Journal of Human Sexuality
基 金:河北省2021年度医学科学研究课题(20210076)。
摘 要:目的探究抗磷脂综合征(APS)孕妇不良妊娠结局发生率及影响因素,并构建预测模型。方法选取2018年1月至2022年1月就诊于河北省人民医院的300例APS孕妇的临床资料进行回顾性研究,根据妊娠结局分为妊娠不良组(n=131)和妊娠良好组(n=169)。分析APS孕妇不良妊娠结局发生的影响因素,并基于影响因素构建Nomogram预测模型。结果APS孕妇不良妊娠结局发生率为43.67%;年龄、孕前体重指数(BMI)、负性情绪、流产史、不良生活方式均为APS孕妇不良妊娠结局发生的独立危险因素,规范用药为保护因素(P<0.05);基于影响因素构建Nomogram预测模型,该模型预测APS孕妇发生不良妊娠结局的曲线下面积(AUC)为0.848。结论APS孕妇中不良妊娠结局发生率较高,其影响因素包括年龄、孕前BMI、负性情绪等多方面,根据影响因素构建的Nomogram预测模型可较好地预测不良妊娠结局发生风险。Objective To investigate the incidence and factors influencing adverse pregnancy outcomes in antiphospholipid syndrome(APS)and to construct a prediction model.Methods The clinical data of 300 pregnant women with APS in Hebei General Hospital from January 2018 to January 2022 were retrospectively studied.According to the pregnancy outcome,they were divided into adverse pregnancy group(n=131)and good pregnancy group(n=169).Influencing factors on the occurrence of adverse pregnancy outcomes in pregnant women with APS were analyzed and a Nomogram prediction model based on the influencing factors was constructed.Results The incidence of adverse pregnancy outcomes of pregnant women with APS was 43.67%.Age,pre-pregnancy body mass index(BMI),negative emotions,abortion history,and adverse lifestyle were independent risk factors for adverse pregnancy outcomes in pregnant women with APS,and standardized medication was the protective factor(P0.05).The nomogram prediction model was constructed based on the influencing factors.The area under the curve(AUC)of the model for predicting adverse pregnancy outcomes in pregnant women with APS was 0.848.Conclusions The incidence of adverse pregnancy outcomes in pregnant women with APS was high,and its influencing factors included age,pre-pregnancy BMI,negative emotions and other aspects.Nomogram prediction model based on influencing factors could better predict the risk of adverse pregnancy outcomes.
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