Huo Lanting, Yu Xingfeng, Nisar Anum, Yang Lei, Li Xiaomei
Faculty of Nursing, Health Science Center, Xi'an Jiaotong University, Xi'an, Shaanxi, China.
The Nursing Department, Shaanxi Provincial People's Hospital, Xi'an, Shaanxi, China.
Front Psychiatry. 2024 Nov 29;15:1478565. doi: 10.3389/fpsyt.2024.1478565. eCollection 2024.
Nomograms are superior to traditional multivariate regression models in the competence of quantifying an individual's personalized risk of having a given condition. To date, no literature has been found to report a quantified risk prediction model for prenatal depression. Therefore, this study was conducted to investigate the prevalence and associated factors of prenatal depression. Moreover, two novel nomograms were constructed for the quantitative risk prediction.
In this cross-sectional study, the participants were recruited using convenience sampling and administered with the research questionnaires. The prevalence of prenatal depression was calculated with a cutoff point of ≥ 10 in the 8-item Patient Health Questionnaire. Univariate and multivariate binomial logistic regression models were subsequently employed to identify the associated factors of prenatal depression. Two nomograms for the risk prediction were constructed and multiple diagnostic parameters were used to examine their performances.
The prevalence of prenatal depression was 9.5%. Multivariate binomial logistic regression model based on sociodemographic, health-related, and pregnancy-related variables (model I) suggested that unemployment, poor relationship with partners, antecedent history of gynecologic diseases, unplanned pregnancy, an earlier stage of pregnancy, and more severe vomiting symptoms were associated with increased risk of prenatal depression. In the regression model that further included psychosocial indicators (model II), unemployment, antecedent history of gynecologic diseases, unplanned pregnancy, an earlier stage of pregnancy, and a higher total score in the Pregnancy Stress Rating Scale were found to be associated with prenatal depression. The diagnostic parameters suggested that both nomograms for the risk prediction of prenatal depression have satisfactory discriminative and predictive efficiency and clinical utility. The nomogram based on model II tended to have superior performances and a broader estimating range and that based on model I could be advantageous in its ease of use.
The prevalence of prenatal depression was considerably high. Risk factors associated with prenatal depression included unemployment, poor relationship with partners, antecedent history of gynecologic diseases, unplanned pregnancy, an earlier stage of pregnancy, more severe vomiting symptoms, and prenatal stress. The risk prediction model I could be used for fasting screening, while model II could generate more precise risk estimations.
列线图在量化个体患特定疾病的个性化风险方面优于传统多变量回归模型。迄今为止，尚未发现有文献报道产前抑郁的量化风险预测模型。因此，本研究旨在调查产前抑郁的患病率及相关因素。此外，构建了两个新的列线图用于定量风险预测。
在这项横断面研究中，采用便利抽样招募参与者并对其进行研究问卷施测。使用8项患者健康问卷中≥10分的临界值计算产前抑郁的患病率。随后采用单变量和多变量二项式逻辑回归模型确定产前抑郁的相关因素。构建了两个用于风险预测的列线图，并使用多个诊断参数检验其性能。
产前抑郁的患病率为9.5%。基于社会人口统计学、健康相关和妊娠相关变量的多变量二项式逻辑回归模型（模型I）表明，失业、与伴侣关系不佳、妇科疾病既往史、意外怀孕、妊娠早期以及更严重的呕吐症状与产前抑郁风险增加相关。在进一步纳入心理社会指标的回归模型（模型II）中，发现失业、妇科疾病既往史、意外怀孕、妊娠早期以及妊娠压力评定量表总分较高与产前抑郁相关。诊断参数表明，两个产前抑郁风险预测列线图均具有令人满意的鉴别、预测效率和临床实用性。基于模型II的列线图往往具有更优的性能和更宽的估计范围，而基于模型I的列线图在易用性方面具有优势。
产前抑郁的患病率相当高。与产前抑郁相关的风险因素包括失业、与伴侣关系不佳、妇科疾病既往史、意外怀孕、妊娠早期、更严重的呕吐症状以及产前压力。风险预测模型I可用于快速筛查，而模型II可生成更精确的风险估计。
