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. 2023 Nov 15:14:1238092.
doi: 10.3389/fendo.2023.1238092. eCollection 2023.

A prediction model for high ovarian response in the GnRH antagonist protocol

Yilin Jiang  1   2 , Chenchen Cui  2 , Jiayu Guo  1   2 , Ting Wang  2   3 , Cuilian Zhang  2
Affiliations

A prediction model for high ovarian response in the GnRH antagonist protocol

Yilin Jiang et al. Front Endocrinol (Lausanne). .

Abstract

Backgrounds: The present study was designed to establish and validate a prediction model for high ovarian response (HOR) in the GnRH antagonist protocol.

Methods: In this retrospective study, the data of 4160 cycles were analyzed following the in vitro fertilization (IVF) at our reproductive medical center from June 2018 to May 2022. The cycles were divided into a training cohort (n=3121) and a validation cohort (n=1039) using a random sampling method. Univariate and multivariate logistic regression analyses were used to screen out the risk factors for HOR, and the nomogram was established based on the regression coefficient of the relevant variables. The area under the receiver operating characteristic curve (AUC), the calibration curve, and the decision curve analysis were used to evaluate the performance of the prediction model.

Results: Multivariate logistic regression analysis revealed that age, body mass index (BMI), follicle-stimulating hormone (FSH), antral follicle count (AFC), and anti-mullerian hormone (AMH) were independent risk factors for HOR (all P< 0.05). The prediction model for HOR was constructed based on these factors. The AUC of the training cohort was 0.884 (95% CI: 0.869-0.899), and the AUC of the validation cohort was 0.884 (95% CI:0.863-0.905).

Conclusion: The prediction model can predict the probability of high ovarian response prior to IVF treatment, enabling clinicians to better predict the risk of HOR and guide treatment strategies.

Keywords: GnRH antagonist protocol; controlled ovarian stimulation; high ovarian response; nomogram; prediction model.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
A flow-chart of cycles’ selection and exclusions.
Figure 2
Figure 2
A nomogram to predict the risk of high ovarian response in the GnRH antagonist protocol.
Figure 3
Figure 3
Receiver operating characteristic (ROC) curves and calibration plots of the training and validation cohorts. (A) The area under the ROC curve (AUC) of the training cohort was 0.884 (95% CI: 0.869–0.899). (B) Calibration curve for the training cohort. (C) The AUC of the validation cohort was 0.884 (95% CI: 0.863–0.905). (D) Calibration curve for the validation cohort. Calibration curves were used to evaluate the calibration of the model. The horizontal axis is the predicted probability provided by this model, and the vertical axis is the observed incidence of pregnancy failure. The ideal line with a 45° slope represents a perfect prediction (the predicted probability equals the observed probability).
Figure 4
Figure 4
Decision curve analysis of the model with the net benefit as the vertical axis and the threshold probability as the horizontal axis. (A) Decision curve analysis for the training cohort. (B) Decision curve analysis for the validation cohort.
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