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Published on: October 2, 2018
Development and validation of a prediction model for premature progesterone elevation during early-follicular phase
Tian Ye1,2, Wenqian Fan1,2, Linqing Du1,2
1Reproductive Medical Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Background:
In controlled ovarian stimulation (COS) cycles, premature progesterone elevation (PPE) on the HCG trigger day is associated with adverse pregnancy outcomes after in vitro fertilization-embryo transfer (IVF-ET). The aim of this study was to analyze the influencing factors of PPE with the early-follicular phase long-acting GnRH-a long protocol (EFLL) and to explore a dynamic risk-assessment nomogram for PPE to inform risk stratification and early clinical adjustment.
Methods:
This was a single-center, retrospective cohort study. Patients who underwent their first IVF cycle at our center from January 2019 to December 2019 were included. All patients were treated with the EFLL protocol. Patients were randomly assigned to the training or validation cohort for nomogram development and testing at a ratio of 8:2. After excluding collinear variables, the remaining candidate predictors were screened by the LASSO regression model, and the nomogram was constructed by multivariate logistic regression analysis. Receiver operating characteristic (ROC) curves and calibration curves with bootstrap were used to evaluate the performance of the nomogram. DCA was used to analyze the clinical efficacy of the model.
Results:
A total of 5193 cycles were included-4154 cycles in the training cohort and 1039 cycles in the validation cohort. The incidence of PPE in the training cohort was 20.51%. Multivariate logistic regression showed that BMI, basal FSH, basal progesterone (bP), AMH, AFC, total duration of Gn, total dosage of exogenous HMG, FSH and P on the 6th day of Gn were found to be independent influencing factors of PPE, and a nomogram model was constructed accordingly. The areas under the ROC curves of the training cohort and the validation cohort were 0.683 (95% CI = 0.663-0.702) and 0.622 (95% CI = 0.583-0.662), respectively. The calibration curve showed that the predicted risk of the model was in good agreement with the actual results in both cohorts. Decision curve analysis (DCA) demonstrated the clinical value of this nomogram.
Conclusions:
Our nomogram for estimating PPE demonstrates modest discriminative ability and may serve as a preliminary risk stratification tool in COS cycles. It may assist clinicians in identifying patients at higher risk of PPE, thereby informing clinical judgment.
