Nomogram for predicting the risk of preterm birth in women undergoing in vitro fertilization cycles.

Background: The aim of this study was to develop a nomogram for predicting the risk of preterm birth in women undergoing in vitro fertilization (IVF) cycles.

Methods: A retrospective study of 4266 live birth cycles collected from January 2016 to October 2021 at the Center for Reproductive Medicine, First Hospital of Jilin University was performed. The sample size was sufficient based on the minimal ten events per variable (EPV) rule. The primary outcome of this study was preterm birth. The cycles were divided into the preterm birth group (n = 827) and the full-term delivery group (n = 3439). A nomogram was established based on the multivariate logistic regression analysis results. The area under the curve (AUC) was calculated to assess the prediction accuracy of the nomogram model. The calibration curve was used to measure the calibration of the nomogram.

Results: Multivariate logistic regression analyses showed that female obesity or overweight (OR = 1.366, 95% CI: 1.111-1.679; OR = 1.537, 95% CI: 1.030-2.292), antral follicle count (AFC) of more than 24 (OR = 1.378, 95% CI: 1.035-1.836), multiple pregnancies (OR = 6.748, 95% CI: 5.559-8.190), gestational hypertension (OR = 9.662, 95% CI: 6.632-14.078) and gestational diabetes (OR = 4.650, 95% CI: 2.289-9.445) were the independent risk factors for preterm birth in IVF patients. The area under curve (AUC) under the receiver operating characteristic (ROC) curve in the prediction model was 0.781(95%CI: 0.763-0.799). The calibration curve of the nomogram showed that the prediction model had a good calibration.

Conclusions: We used five risk factors to conduct a nomogram to predict preterm birth rates for patients undergoing IVF cycles. This nomogram can provide a visual assessment of the risk of preterm birth for clinical consultation.