Volume 23 Issue 3
Mar.  2025
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Article Navigation >  Chinese Journal of General Practice  > 2025  >  23(3): 369-373
ZHU Dan, ZHANG Liqin, ZHANG Haibo, ZHU Lingling, CHEN Qianya. Construction and preliminary validation of a nomogram model for predicting the risk of postpartum depression in gestational diabetes mellitus patients[J]. Chinese Journal of General Practice, 2025, 23(3): 369-373. doi: 10.16766/j.cnki.issn.1674-4152.003906
Citation: ZHU Dan, ZHANG Liqin, ZHANG Haibo, ZHU Lingling, CHEN Qianya. Construction and preliminary validation of a nomogram model for predicting the risk of postpartum depression in gestational diabetes mellitus patients[J]. Chinese Journal of General Practice, 2025, 23(3): 369-373. doi: 10.16766/j.cnki.issn.1674-4152.003906
shu

Construction and preliminary validation of a nomogram model for predicting the risk of postpartum depression in gestational diabetes mellitus patients

doi: 10.16766/j.cnki.issn.1674-4152.003906

  • Department of Obstetrics, Nantong Maternal and Child Health Hospital Affiliated to Nantong University, Nantong, Jiangsu 226018, China

Funds:

 ZD2021036

 MSZ2023014

  • Received Date: 2024-11-08
    Available Online: 2025-05-14
    Abstract
  •   Objective  To construct a nomogram model for predicting the risk of postpartum depression (PPD) in women with gestational diabetes mellitus (GDM) and to preliminarily validate its predictive efficacy.  Methods  A total of 318 GDM patients admitted to Nantong Maternal and Child Health Care Hospital from January 2021 to December 2023 were retrospectively enrolled in this study. Six weeks after delivery, the Edinburgh postnatal depression scale (EPDS) was used to assess PPD, and the patients were divided into a PPD group (n=76, 23.9%) and a non-PPD group (n=242, 76.1%). Demographic characteristics, gestational clinical features, blood glucose levels, and complications were collected. Independent risk factors for PPD were screened by multivariate logistic regression analysis, and a nomogram model was constructed based on the regression results. The discrimination and calibration of the model were evaluated using the receiver operating characteristic (ROC) curve and calibration curve, respectively. A clinical decision curve was also plotted.  Results  Multivariate logistic regression analysis showed that pre-pregnancy BMI, gestational weight gain, being an only child, average monthly family income, fasting blood glucose, 2-hour postprandial blood glucose, and the occurrence of gestational complications were independent risk factors for PPD in GDM patients (P < 0.05). The nomogram model based on these variables had an area under the ROC curve of 0.958 (95% CI: 0.893-0.989), indicating good discrimination. The model also showed good calibration, with the predicted values being consistent with the actual observed values (Hosmer & Lemeshow χ2=5.399, P=0.714). The clinical decision curve showed that when the risk of PPD in GDM patients predicted by this model was >0.3, timely intervention based on the patient's clinical characteristics could achieve higher net benefits.  Conclusion  The nomogram model for predicting the risk of PPD in GDM patients constructed in this study is simple, easy to use, and has good predictive efficacy. It can provide a reference for the early identification and intervention of PPD in GDM patients in clinical practice.

     

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