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Development and validation of models for predicting preterm birth and gestational latency following emergency
Cheng Chen1, Baihui Zhao1,2, Yu Pan1
1Department of Obstetrics, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Introduction:
Emergency cervical cerclage is a recognized method for preventing mid-trimester pregnancy loss and premature birth; however, its benefits remain controversial. This study aimed to establish preoperative models predicting preterm birth and gestational latency following emergency cervical cerclage in singleton pregnant patients with a high risk of preterm birth.
Material And Methods:
We retrospectively reviewed data from patients who received emergency cerclage between 2015 and 2023 in three institutions. Patients were grouped into a derivation cohort (n = 141) and an independent validation cohort (n = 61). Univariate and multivariate logistic and Cox regression analyses were used to identify independent predictive variables and establish the models. Harrell's C-index, time-dependent receiver operating characteristic curves and areas under the curves, calibration curve, and decision curve analyses were performed to assess the models.
Results:
The models incorporated gestational weeks at cerclage placement, history of prior second-trimester loss and/or preterm birth, cervical dilation, and preoperative C-reactive protein level. The C-index of the model for predicting preterm birth before 28 weeks was 0.87 (95% CI: 0.82-0.93) in the derivation cohort and 0.82 (95% CI: 0.71-0.92) in the independent validation cohort; The C-index of the model for predicting gestational latency was 0.70 (95% CI: 0.66-0.75) and 0.78 (95% CI: 0.71-0.84), respectively. In the derivation set, the areas under the curves were 0.84, 0.81, and 0.84 for predicting 1-, 3- and 5-week pregnancy prolongation, respectively. The corresponding values for the external validation were 0.78, 0.78, and 0.79, respectively. Calibration curves showed a good homogeneity between the observed and predicted ongoing pregnant probabilities. Decision curve analyses revealed satisfactory clinical usefulness.
Conclusions:
These novel models provide reliable and valuable prognostic predictions for patients undergoing emergency cerclage. The models can assist clinicians and patients in making personalized clinical decisions before opting for the cervical cerclage.
