廖宇佳,陈思宇,邓翔宇,甘艳琼,韩姝蕾,谭欣林,黄玥.产前抑郁风险预测简易模型的构建与验证[J].四川精神卫生杂志,2023,36(5):466-472.Liao Yujia,Chen Siyu,Deng Xiangyu,Gan Yanqiong,Han Shulei,Tan Xinlin,Huang Yue,Construction and validation of a simple model for predicting the risk of prenatal depression[J].SICHUAN MENTAL HEALTH,2023,36(5):466-472

产前抑郁风险预测简易模型的构建与验证

Construction and validation of a simple model for predicting the risk of prenatal depression

投稿时间：2023-03-03

DOI：10.11886/scjsws20230303001

中文关键词: 产前抑郁危险因素预测模型

英文关键词:Prenatal depression Risk factor Prediction model

基金项目:南充市社会科学研究“十四五”规划2021年度项目（项目名称：南充市育龄妇女产前抑郁规范化管理策略研究，项目编号：NC21B165）

作者	单位	邮编
廖宇佳	南充市身心医院	637770
陈思宇^*	南充市身心医院	637770
邓翔宇	西华师范大学	637001
甘艳琼	川北医学院附属医院	637002
韩姝蕾	北京仁心长和医疗科技技术有限公司	102600
谭欣林	川北医学院附属医院	637002
黄玥	川北医学院附属医院	637002

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中文摘要:

背景近年来，孕妇心理问题已成为我国重要的公共卫生问题，抑郁是孕期最常见心理问题，目前研究多集中在产前抑郁的治疗方面，缺少产前抑郁风险预测模型的构建。目的建立产前抑郁风险预测简易模型，为预防孕妇抑郁提供参考。方法于2021年5月-2022年2月，连续选取在南充市三所医院就诊的803名孕妇为研究对象。采用自制问卷收集孕妇的社会人口学信息、产科与医学信息、心理信息，采用抑郁自评量表（SDS）评定其抑郁症状。按照8∶2有放回地、随机将研究对象分为模型组（n=635）和检验组（n=168），采用二元Logistic回归分析孕妇抑郁的危险因素，构建预测模型，采用ROC曲线对预测模型的价值进行验证。结果 ①产前无伴侣陪伴（β=-0.692，OR=0.501，95% CI：0.289~0.868）、末次月经期情绪低落（β=-1.510，OR=0.221，95% CI：0.074~0.656）、末次月经期情绪紧张（β=-1.082，OR=0.339，95% CI：0.135~0.853）、婆媳关系不满意（β=-1.228，OR=0.293，95% CI：0.141~0.609）以及婆媳关系一般（β=-0.831，OR=0.436，95% CI：0.260~0.730）是孕妇产前抑郁的危险因素（P<0.05或0.01）。②模型组ROC曲线下面积（AUC）为0.698，95% CI：0.646~0.749，约登指数最大为0.357，灵敏度为0.606，特异度为0.751；检验组AUC=0.672，95% CI：0.576~0.767，约登指数最大值为0.263，灵敏度为0.556，特异度为0.707。结论本研究构建的产前抑郁风险预测简易模型具有较好的判别效度。

英文摘要:

Background Mental illness during pregnancy has become a major public health problem in China over the recent years， and depression is the most common psychological symptom during pregnancy. Current research efforts are directed towards the therapy on prenatal depression， whereas the construction of prediction model for prenatal depression risk has been little studied.Objective To construct a simple model for predicting the risk of prenatal depression， thus providing a valuable reference for the prevention of maternal depression during pregnancy.Methods A total of 803 pregnant women attending three hospitals in Nanchong city were consecutively recruited from May 2021 to February 2022. A self-administered questionnaire was developed for the assessment of social demographic variables， obstetrical and general medical indexes and psychological status of all participants， and Self-rating Depression Scale （SDS） was utilized to screen for the presence of maternal depression. Subjects were randomly assigned into modelling group （n=635） and validation group （n=168） at the ratio of 8∶2 under simple random sampling with replacement. The candidate risk factors of maternal depression during pregnancy were screened using binary Logistic regression analysis， and the predictive model was constructed. Then the performance of the predictive model was validated using receiver operating characteristics （ROC） curve.Results ① Lack of companionship （β=-0.692， OR=0.501， 95% CI： 0.289~0.868）， low mood during the last menstrual period （β=-1.510， OR=0.221， 95% CI： 0.074~0.656）， emotional stress during the last menstrual period （β=-1.082， OR=0.339， 95% CI： 0.135~0.853）， unsatisfactory relationship between mother-in-law and daughter-in-law （β=-1.228， OR=0.293， 95% CI： 0.141~0.609）， and indifferent generally relationship between mother-in-law and daughter-in-law （β=-0.831， OR=0.436， 95% CI： 0.260~0.730） were risk factors for prenatal depression in pregnant women （P<0.05 or 0.01）. ② Model for predicting the prenatal depression risk yielded an area under curve （AUC） of 0.698 （95% CI： 0.646~0.749）， the maximum Youden index was 0.357 in modelling group with the sensitivity and specificity was 0.606 and 0.751， and an AUC of 0.672 （95% CI： 0.576~0.767） and maximum Youden index of 0.263 in validation group with the sensitivity and specificity of 0.556 and 0.707.Conclusion The simple model constructed in this study has good discriminant validity in predicting of the risk of prenatal depression. ［Funded by Nanchong Social Science Research Project of the 14th Five-Year Plan （number， NC21B165）］

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