ArticleBMC pregnancy and childbirth2024
Establishment and validation of a predictive model for spontaneous preterm birth in singleton pregnant women.
Article in BMC pregnancy and childbirth, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Development and Validation of an Early-Pregnancy Prediction Model for Indicated Preterm Birth Using Three-Dimensional Placental Ultrasound and Maternal Clinical Characteristics.International journal of women's health · 2026Article
- An explainable machine learning model for predicting preterm birth in pregnant women with gestational diabetes mellitus and hypertensive disorders of pregnancy: development and external validation.Frontiers in endocrinology · 2025Article
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6 authors.
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Abstract
introductionIn the current study, we screened for highly sensitive and specific predictors of premature birth, with the aim to establish an sPTB prediction model that is suitable for women in China and easy to operate and popularize, as well as to establish a sPTB prediction scoring system for early, intuitive, and effective assessment of premature birth risk.
methodsA total of 685 pregnant women with a single pregnancy during the second trimester (16-26 weeks) were divided into premature and non-premature delivery groups based on their delivery outcomes. Clinical and ultrasound information were collected for both groups, and risk factors that could lead to sPTB in pregnant women were screened and analyzed using a cut-off value. A nomogram was developed to establish a prediction model and scoring system for sPTB. In addition, 119 pregnant women who met the inclusion criteria for the modeling cohort were included in the external validation of the model. The accuracy and consistency of the model were evaluated using the area under the receiver operating characteristic (ROC) and C-calibration curves.
resultsMultivariate logistic regression analysis showed a significant correlation (P < 0.05) between the number of miscarriages in pregnant women, history of miscarriages in the first week of pregnancy, history of preterm birth, CL of pregnant women, open and continuous cervical opening, and the occurrence of sPTB in pregnant women. We drew a nomogram column chart based on the six risk factors mentioned above, obtained a predictive model for sPTB, and established a scoring system to divide premature birth into three risk groups: low, medium, and high. After validating the model, the Hosmer Lemeshow test indicated a good fit (p = 0.997). The modeling queue C calibration curve was close to diagonal (C index = 0.856), confirming that the queue C calibration curve was also close to diagonal (C index = 0.854). The AUCs of the modeling and validation queues were 0.850 and 0.881, respectively.
conclusionOur predictive model is consistent with China's national conditions, as well as being intuitive and easy to operate, with wide applicability, thus representing a helpful tool to assist with early detection of sPTB in clinical practice, as well as for clinical management in assessing low, medium, and high risks of sPTB.
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