Establishment and validation of a predictive model for spontaneous preterm birth in singleton pregnant women

Abstract

Introduction: In 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.

Methods: A 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.

Results: Multivariate 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.

Conclusion: Our 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.

Keywords: Prediction model; Predictive factors; Spontaneous premature birth; Ultrasound.

Fig. 1

Binary logstic regression analysis forest map

Fig. 2

ROC curves for various risk factors and joint predictions

Fig. 3

Nomogram prediction model for spontaneous premature birth in pregnant women

Fig. 4

Calibration curves of modeling queue and validation queue C. Note: Figure (a) shows the calibration curve of modeling queue C, and Figure (b) shows the calibration curve of validation queue C

Fig. 5

Modeling queue and validation queue ROC curves. Note: Figure (a) shows the area under the modeling queue curve (AUC), while Figure (b) shows the area under the validation queue curve (AUC)

Fig. 6

Analysis of clinical decision curves (DCA) for modeling and validation queues. Note: Figure (a) shows the modeling queue DCA, while Figure (b) shows the validation queue DCA. The green line represents the net benefit of patients who were not considered to have sPTB; The red diagonal represents the net benefit for all patients diagnosed with sPTB. The farther the curve in the model is from the green and red lines, the greater the benefit the model brings to patients when used to predict the diagnosis of sPTB