Development and validation of a nomogram to predict spontaneous preterm birth in singleton gestation with short cervix and no history of spontaneous preterm birth.

BACKGROUND

Spontaneous preterm birth (sPTB) stands as a leading cause of neonatal mortality. Consequently, preventing sPTB has emerged as a paramount concern in healthcare. Therefore, our study aimed to develop a nomogram, encompassing patient characteristics and cervical elastography, to predict sPTB in singleton pregnancies. Specifically, we targeted those with a short cervix length (CL), no history of sPTB, and who were receiving vaginal progesterone therapy.

METHODS

A total of 568 patients were included in this study. Data from 392 patients, collected between January 2016 and October 2019, constituted the training cohort. Meanwhile, records from 176 patients, spanning November 2019 to January 2022, formed the validation cohort. Following the univariate logistic regression analysis, variables exhibiting a P-value less than 0.05 were integrated into a multivariable logistic regression analysis. The primary objective of this subsequent analysis was to identify the independent predictors linked to sPTB in the training cohort. Next, we formulated a nomogram utilizing the identified independent predictors. This tool was designed to estimate the likelihood of sPTB in singleton pregnancies, particularly those with a short CL, devoid of any sPTB history, and undergoing vaginal progesterone therapy. The C-index, Hosmer-Lemeshow (HL) test, calibration curves, decision curve analysis (DCA), and receiver operating characteristic (ROC) were used to validate the performance of the nomogram.

RESULTS

Upon finalizing the univariate analysis, we progressed to a multivariable analysis, integrating 8 variables with P < 0.05 from the univariate analysis. The multivariable analysis identified 7 independent risk factors: maternal age (OR = 1.072; P < 0.001), cervical length (OR = 0.854; P < 0.001), uterine curettage (OR = 7.208; P < 0.001), GDM (OR = 3.570; P = 0.006), HDP (OR = 4.661; P = 0.003), C-reactive protein (OR = 1.138; P < 0.001), and strain of AI (OR = 7.985; P < 0.001). The nomogram, tailored for sPTB prediction, was grounded on these 7 independent predictors. In predicting sPTB, the C-indices manifested as 0.873 (95% CI, 0.827-0.918) for the training cohort and 0.916 (95%CI, 0.870-0.962) for the validation cohorts, underscoring a good discrimination of the model. Additionally, the ROC curves served to evaluate the discrimination of nomogram model across both cohorts. Calibration curves were delineated, revealing no statistically significant differences in both the training (χ = 5.355;  = 0.719) and validation (χ = 2.708;  = 0.951) cohorts as evidenced by the HL tests. Furthermore, the DCA underscored the model's excellence as a predictive tool for sPTB.

CONCLUSIONS

By amalgamating patient characteristics and cervical elastography data from the second trimester, the nomogram emerged as a visually intuitive and dependable tool for predicting sPTB. Its relevance was particularly pronounced for singleton pregnancies characterized by a short CL, an absence of prior sPTB incidents, and those receiving vaginal progesterone therapy.