Risk Factor Analysis and Prediction Model Construction and Validation of Depression During Pregnancy - Huiling Qu; Yanna Zhou; Yi Yu (Clinical and Experimental Obstetrics & Gynecology)
Background: Depression during pregnancy can have serious negative effects on the health of both the woman and the fetus. Therefore, studying the risk factors associated with depression in pregnancy is important for timely interventions and prevention. This study aimed to comprehensively identify the risk factors of depression during pregnancy and construct and verify the effectiveness of a prediction model to provide a basis for early prevention and intervention of depression during pregnancy. Methods: A total of 630 pregnant women who underwent regular prenatal checkups at Jinshan Central Hospital Affiliated to Shanghai University of Medicine & Health Sciences from January 2020 to October 2023 were included. The Edinburgh Postnatal Depression Scale (EPDS) and Generalized Anxiety Disorder (GAD-7) were utilized to assess the presence of depressive disorders in mid-pregnancy. A risk prediction nomogram model was constructed using the R program, and validation was performed using the Bootstrap method. The calibration curve chart was produced, and diagnostic efficacy was evaluated using the receiver operating characteristic (ROC) curve. Results: The prevalence of mid-pregnancy depression was found to be 19.37%. Moreover, no statistically significant differences were observed between the two groups in terms of age, gravidity, parity, pre-pregnancy body mass index (BMI), cultural level, smoking or drinking alcohol, and work cessation due to pregnancy (p > 0.05). However, statistically significant differences were noted in the incidence of spousal disharmony, discordant relations with parents, changes in sleep and diet, work-study stress, adverse maternal history, dissatisfactory living environments, assisted reproduction, unplanned pregnancy, adverse life events, lack of maternity knowledge, family income, and pregnancy comorbidities (p < 0.05). A nomogram model was developed based on the multifactor analysis, showing a mean absolute error of 0.011 in the calibration curve, indicating good predictive accuracy. The ROC analysis demonstrated an area under the curve (AUC) of 0.806 for the joint prediction model, with a sensitivity of 66.4% and a specificity of 83.5%, suggesting a strong clinical diagnostic value. The study sample was drawn from pregnant women in our hospital, which may have led to a limited representative sample. The timeframe of the study may also have led to the exclusion of specific periods of pregnant women. Conclusions: A nomogram model, which incorporates indicators such as spousal and parental disharmony, changes in sleep and dietary habits, work-study stress, adverse maternal history, unsatisfactory living environment, assisted reproduction, unplanned pregnancy, interference from adverse life events, and lack of maternity knowledge, can effectively predict depression during pregnancy.