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| Content Provider | Springer Nature : BioMed Central |
|---|---|
| Author | Zhang, Jiani Cao, Qi Mao, Chihui Xu, Jinfeng Li, Yaqian Mu, Yi Huang, Guiqiong Chen, Daijuan Deng, Xixi Xu, Tingting Zhou, Fan Wang, Xiaodong |
| Abstract | Objectives To develop a clinically applicable and promotable prediction model for assessing the risk of gestational diabetes mellitus (GDM) within the context of primary healthcare institutions. Methods The construction and the internal validation of the prediction model involved a cohort of 6,216 pregnant women observed from January 2019 to June 2019 in a Class A tertiary hospital in western China. External validation was subsequently conducted with 443 pregnant women from October 2020 to June 2021. Core characteristics were identified and the model was established using the least absolute shrinkage and selection operator (LASSO) regression. Internal validation was performed using the Bootstrap method. Model evaluation included discrimination and calibration tests, decision curve analysis (DCA), and the clinical impact curve. Visualization of the model was achieved through a static nomogram and a risk-scoring model. Results The simplified prediction model possessed seven variables, including age, prepregnancy body mass index (BMI), polycystic ovary syndrome (PCOS), history of GDM, family history of diabetes, fasting plasma glucose (FPG), and urine glucose. This model exhibited a predictive accuracy, as reflected by a C-index of 0.736 (95% CI: 0.720 ~ 0.753) in the training set. The C-indexes were 0.735 and 0.694 in the internal and external testing set. Well-fitted calibration curves, the DCA curve, and the clinical impact curve demonstrated the feasibility of the simplified prediction model. For enhanced clinical application, the static nomogram and the risk-scoring model were employed to visualize the model. Conclusions This study developed a prediction model for assessing the risk of GDM among women from 8 to 14 weeks of gestation in western China. The model demonstrated moderate discriminatory ability, well-fitted calibration, and convenient visualization, suggesting its suitability for implementation and widespread adoption, particularly within the context of primary healthcare institutions. |
| Related Links | https://bmcpregnancychildbirth.biomedcentral.com/counter/pdf/10.1186/s12884-025-07442-1.pdf |
| Ending Page | 12 |
| Page Count | 12 |
| Starting Page | 1 |
| File Format | HTM / HTML |
| ISSN | 14712393 |
| DOI | 10.1186/s12884-025-07442-1 |
| Journal | BMC Pregnancy and Childbirth |
| Issue Number | 1 |
| Volume Number | 25 |
| Language | English |
| Publisher | BioMed Central |
| Publisher Date | 2025-04-02 |
| Access Restriction | Open |
| Subject Keyword | Reproductive Medicine Maternal and Child Health Gynecology Clinical prediction model Gestational diabetes mellitus Nomogram Screening Women |
| Content Type | Text |
| Resource Type | Article |
| Subject | Obstetrics and Gynecology |
| Journal Impact Factor | 2.8/2023 |
| 5-Year Journal Impact Factor | 3.4/2023 |
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