宗子昱, 王芬. 围产期抑郁障碍产妇妊娠晚期社会心理因素调查分析及预测模型构建[J]. 实用临床医药杂志, 2024, 28(10): 121-125. DOI: 10.7619/jcmp.20233994
引用本文: 宗子昱, 王芬. 围产期抑郁障碍产妇妊娠晚期社会心理因素调查分析及预测模型构建[J]. 实用临床医药杂志, 2024, 28(10): 121-125. DOI: 10.7619/jcmp.20233994
ZONG Ziyu, WANG Fen. Investigation of social and psychological factors in late pregnancy among women with perinatal depressive disorder and construction of a predictive model[J]. Journal of Clinical Medicine in Practice, 2024, 28(10): 121-125. DOI: 10.7619/jcmp.20233994
Citation: ZONG Ziyu, WANG Fen. Investigation of social and psychological factors in late pregnancy among women with perinatal depressive disorder and construction of a predictive model[J]. Journal of Clinical Medicine in Practice, 2024, 28(10): 121-125. DOI: 10.7619/jcmp.20233994

围产期抑郁障碍产妇妊娠晚期社会心理因素调查分析及预测模型构建

Investigation of social and psychological factors in late pregnancy among women with perinatal depressive disorder and construction of a predictive model

  • 摘要:
    目的 调查分析围产期抑郁障碍(PDD)产妇妊娠晚期的社会心理因素,并构建PDD预测模型。
    方法 选取88例确诊PDD的产妇纳入研究组,另按照1∶1的比例选取正常建档产检的健康产妇88例纳入对照组,收集2组产妇的一般资料和产前社会心理因素相关情况,进行单因素及多因素Logistic回归分析,构建产妇PDD预测模型并评估其预测效能。
    结果 单因素及多因素Logistic回归分析结果显示,妊娠年龄、经期情绪、不良妊娠史、产妇性别歧视、公婆性别歧视、收入满意情况、两系三代抑郁史均为产妇发生PDD的独立影响因素(P<0.05)。据此构建产妇PDD预测模型,模型方程为P=\frac11+e-\operatornamelogit(P),其中logit(P)=1.599×妊娠年龄+1.744×经期情绪不良+0.837×不良妊娠史+1.589×产妇性别歧视+0.820×公婆性别歧视+1.089×收入不满意+2.163×两系三代抑郁史-3.211。受试者工作特征曲线显示,该模型预测产妇PDD的曲线下面积为0.955, 95%CI为0.907~0.998, 灵敏度为0.964, 特异度为0.731, 此时最佳截断值为5.154。
    结论 妊娠年龄、经期情绪、不良妊娠史、产妇性别歧视、公婆性别歧视、收入满意情况、两系三代抑郁史均为产妇发生PDD的独立影响因素,基于这些因素构建的预测模型对产妇PDD具有良好的预测效能,有助于PDD的早期防治。

     

    Abstract:
    Objective To investigate and analyze the social and psychological factors in late pregnancy among women with perinatal depressive disorder (PDD) and construct a predictive model for PDD
    Methods A total of 88 women diagnosed with PDD were selected as study group, and another 88 healthy women with normal prenatal care were selected as control group in a ratio of 1 to 1. General information and prenatal social and psychological factors related to the two groups were collected. Univariate and multivariate Logistic regression analysis was performed to construct a predictive model for PDD and assess its predictive efficacy.
    Results Univariate and multivariate Logistic regression analysis showed that gestational age, menstrual mood, history of adverse pregnancy, gender discrimination against women, gender discrimination by parents-in-law, income satisfaction, and depression history in paternal and maternal lineage and three generations were independent influencing factors for PDD in women (P<0.05). Based on these factors, a predictive model for PDD in women was constructed, the model equation was P=\frac11+e-\operatornamelogit(P), logit(P)=1.599×gestational age+1.744×poormenstrual mood+0.837×adverse pregnancy history+1.589×gender discrimination against women + 0.820×gender discrimination by parents-in-law+1.089×dissatisfaction with income+2.163×depression history in paternal and maternal lineage and three generations-3.211. The receiver operating characteristic curve showed that the area under the curve for predicting PDD in women using this model was 0.955, with a 95%CI of 0.907 to 0.998, a sensitivity of 0.964, and a specificity of 0.731. The optimal cutoff value was 5.154.
    Conclusion Gestational age, menstrual mood, adverse pregnancy history, gender discrimination against women, gender discrimination by parents-in-law, income satisfaction, and depression history in paternal and maternal lineage and three generations are independent influencing factors for PDD in women. The predictive model constructed based on these factors has good predictive efficacy for PDD in women, which can contribute to the early prevention and treatment of PDD.

     

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