李叶, 王瑞, 朱家姝, 马淑琴, 关素珍. 基于LASSO回归分析构建妊娠相关焦虑发生风险的预测模型[J]. 环境与职业医学, 2023, 40(8): 910-917, 957. DOI: 10.11836/JEOM23016
引用本文: 李叶, 王瑞, 朱家姝, 马淑琴, 关素珍. 基于LASSO回归分析构建妊娠相关焦虑发生风险的预测模型[J]. 环境与职业医学, 2023, 40(8): 910-917, 957. DOI: 10.11836/JEOM23016
LI Ye, WANG Rui, ZHU Jiashu, MA Shuqin, GUAN Suzhen. Construction of predictive model for pregnancy-related anxiety risk in pregnant women based on LASSO regression[J]. Journal of Environmental and Occupational Medicine, 2023, 40(8): 910-917, 957. DOI: 10.11836/JEOM23016
Citation: LI Ye, WANG Rui, ZHU Jiashu, MA Shuqin, GUAN Suzhen. Construction of predictive model for pregnancy-related anxiety risk in pregnant women based on LASSO regression[J]. Journal of Environmental and Occupational Medicine, 2023, 40(8): 910-917, 957. DOI: 10.11836/JEOM23016

基于LASSO回归分析构建妊娠相关焦虑发生风险的预测模型

Construction of predictive model for pregnancy-related anxiety risk in pregnant women based on LASSO regression

  • 摘要: 背景

    妊娠相关焦虑对孕妇的身心健康和胎儿正常生长发育都会产生负面影响。妊娠相关焦虑预测模型的建立及预测因素的筛查可以为产前干预提供重要机会。

    目的

    构建孕期妇女妊娠相关焦虑风险的预测模型。

    方法

    于2021年1—7月,选择在宁夏某三甲医院产科门诊进行常规产前检查的妊娠期妇女进行妊娠相关焦虑及预测因素问卷调查。收集研究对象的社会人口学特征,并采用《生活事件量表》《领悟社会支持量表》《家庭关怀度指数》和《妊娠相关焦虑量表》问卷对孕妇进行调查。使用R 4.2.0软件拟合全变量进行最小绝对收缩和选择算子(LASSO)回归分析,筛选孕中期、孕晚期妊娠相关焦虑的预测因素。在logistic回归分析的基础上分别构建孕中期和孕晚期妊娠相关焦虑发生风险预测模型。绘制模型列线图以及受试者工作特征曲线(ROC)并计算曲线下面积(AUC)评价模型的预测效果。绘制校准图评价模型的校准度。

    结果

    本研究共发放1500份问卷,有效问卷1448份,问卷回收有效率为96.53%。1448名孕妇中,总妊娠相关焦虑发生率为28.80%(417/1448),孕中期和孕晚期发生率分别为29.52%(276/935)和27.49%(141/513)。孕中期进入模型的预测因素有婚龄、家庭关怀、社会支持、家人对胎儿的期待、孕期身体状况和孕期是否经历生活应激事件。孕晚期进入模型的预测因素有怀孕意愿、身体是否有不适和孕期是否经历生活应激事件。根据多因素logistic分析结果分别建立孕中期和孕晚期妊娠相关焦虑风险预测模型,孕中期妊娠相关焦虑发生风险=−0.07×婚龄+0.12×家庭关怀−0.03×社会支持−0.65×家人对胎儿性别的期待+0.42×孕期身体状况+0.47×孕期是否经历生活应激事件;孕晚期妊娠相关焦虑发生风险=−5.69+0.82×怀孕意愿+1.06×身体有无不适+0.94×孕期是否经历生活应激事件。绘制模型ROC曲线,孕中期妊娠相关焦虑风险预测模型的AUC值为0.71,验证模型的AUC值为0.68;孕晚期妊娠相关焦虑风险预测模型的AUC值为0.72,验证模型AUC值为0.66。

    结论

    基于LASSO回归分析和logistic回归分析构建的妊娠相关焦虑风险预测模型具有较好的预测能力,发现孕中期婚龄短、家庭关怀度高、社会支持度低、家人对胎儿性别有期待、孕期身体状况一般和孕期经历过生活应激事件,以及孕晚期怀孕意愿为顺其自然、意外妊娠,身体有不适和孕期经历过生活应激事件的孕妇是妊娠相关焦虑的高危人群。

     

    Abstract: Background

    Pregnancy-related anxiety has a negative impact on the physical and mental health of pregnant women and the normal growth and development of the fetus. Establishing prediction models for pregnancy-related anxiety to screen associated predictive factors may provide important opportunities for prenatal intervention.

    Objective

    To establish a prediction model of pregnancy-related anxiety risk of pregnant women.

    Methods

    From January to July 2021, a questionnaire survey on pregnancy-related anxiety and predictors was conducted among pregnant women having routine prenatal check-ups provided by an obstetrics clinic of a tertiary grade A hospital in Ningxia. The socio-demographic characteristics of the subjects were collected, and the pregnant women were evaluated by the Life Event Scale (LES), Perceived Social Support Scale (PSSS), Family APGAR Index (APGAR), and Pregnancy-related Anxiety Questionnaire (PAQ). R 4.2.0 software was used to fit all selected variables by least absolute shrinkage and selection operator (LASSO) regression to identify predictors of pregnancy-related anxiety in the second and third trimesters. On the basis of logistic regression analysis, prediction models of pregnancy-related anxiety in the second and third trimesters were constructed, and the model nomogram and receiver operating characteristic curve (ROC) were drawn. The prediction effect of the model was evaluated by area under the curve (AUC). A calibration chart was drawn to evaluate the calibration of the model.

    Results

    A total of 1500 questionnaires were distributed, and 1448 valid questionnaires were recovered, with an effective rate of 96.53%. Among the 1448 pregnant women, the overall positive rate of pregnancy-related anxiety was 28.80% (417/1448), and the positive rates in the second and third trimesters were 29.21% (276/935) and 27.49% (141/513), respectively. The predictors entering the the second trimester model were age of marriage, family care, social support, family expectations for the fetus, physical condition during pregnancy, and whether experiencing life stressful events during pregnancy. The predictors entering the the third trimester model were pregnancy intention, physical discomfort, and whether experiencing life stress during pregnancy. A risk prediction model of pregnancy-related anxiety for the second trimester was established: risk of pregnancy-related anxiety=−0.07× marriage age +0.12× family care −0.03× social support −0.65× family expectation of fetal sex +0.42× physical condition during pregnancy +0.47× whether experiencing life stressful events during pregnancy. A risk prediction model of pregnancy-related anxiety for the third trimester was established: risk of pregnancy-related anxiety=−5.69+0.82× pregnancy intention +1.06× physical discomfort +0.94× whether experiencing life stressful events during pregnancy. The ROC curves of the two models were drawn. The AUC of the second trimester model was 0.71, and the AUC of related validation model was 0.68. The AUC of the third trimester model was 0.72, and the AUC of related validation model was 0.66.

    Conclusion

    The risk prediction models of pregnancy-related anxiety constructed based on LASSO regression and logistic regression have good prediction ability, and they suggest that pregnant women in the second trimester with short marriage age, high family care, low social support, family expectations for fetal sex, average physical condition, and experiencing life stress during pregnancy, and pregnant women in the third trimester with spontaneous pregnant intention, unintended pregnancy, physical discomfort, and experiencing life stress during pregnancy are high-risk groups for pregnancy-related anxiety.

     

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