Development and validation of a risk prediction model for spontaneous preterm birth.

Objectives: To identify the factors influencing spontaneous preterm birth (SPTB) and develop a prediction model for clinical practice.

Methods: This retrospective study included a total of 130 pregnant women with spontaneous preterm birth or full-term delivery at Fujian Maternity and Child Health Hospital between January 2020 and December 2023. The SPTB group consisted of 50 women with spontaneous preterm birth, while the full-term group included 70 women with full-term deliveries. Logistic regression analysis was performed to explore the factors associated with clinical prognosis, and a nomogram prediction model for SPTB risk was constructed and validated.

Results: Multivariate logistic regression analysis identified multiple pregnancies (95% CI: 1.415-8.926, P=0.006), abnormal fetal position (95% CI: 1.124-2.331, P=0.008), gestational diabetes (95% CI: 4.918-19.164, P=0.002), mode of conception (95% CI: 1.765-4.285,P=0.002), lower genital tract infection (95% CI: 1.076-2.867, P=0.032), and second trimester cervical length (95% CI: 1.071-2.991, P=0.031) as independent risk factors of SPTB. Using these six variables, a nomogram was developed to predict the incidence of SPTB, with an AUC value of 0.833 (95% CI: 0.665-0.847), demonstrating acceptable agreement between predicted and observed outcomes. Decision curve analysis (DCA) showed a good positive net benefit of the model.

Conclusions: Multiple pregnancies, abnormal fetal position, gestational diabetes, mode of conception, lower genital tract infection, and second-trimester cervical length are independent risk factors for the onset of SPTB. In addition, the nomogram prediction model demonstrated good predictive performance, high accuracy, and clinical applicability.