4.7 Article

An Ovarian Reserve Assessment Model Based on Anti-Mullerian Hormone Levels, Follicle-Stimulating Hormone Levels, and Age: Retrospective Cohort Study

期刊

出版社

JMIR PUBLICATIONS, INC
DOI: 10.2196/19096

关键词

ovarian reserve; poor ovarian response; AMH; AFC; FSH; logistic regression

资金

  1. National Key Research and Development Program of China [2016YFC1000201, 2018YFC1002104, 2018YFC1002106, 2016YFC1000302]
  2. National Natural Science Foundation of China [81300373, 81771650]
  3. Capital Health Research and Development of Special Project [2018-1-4091]
  4. Program for the Innovative Research Team of Yunnan, China [2017HC009]
  5. Major National R&D Projects of China [2017ZX09304012-012]

向作者/读者索取更多资源

Background: Previously, we reported a model for assessing ovarian reserves using 4 predictors: anti-Miillerian hormone (AMH) level, antral follicle count (AFC), follicle-stimulating hormone (FSH) level, and female age. This model is referred as the AAFA (anti-Mullerian hormone level-antral follicle count-follicle-stimulating hormone level-age) model. Objective: This study aims to explore the possibility of establishing a model for predicting ovarian reserves using only 3 factors: AMH level, FSH level, and age. The proposed model is referred to as the AFA (anti-Mullerian hormone level-follicle-stimulating hormone level-age) model. Methods: Oocytes from ovarian cycles stimulated by gonadotropin-releasing hormone antagonist were collected retrospectively at our reproductive center. Poor ovarian response (<5 oocytes retrieved) was defined as an outcome variable. The AFA model was built using a multivariable logistic regression analysis on data from 2017; data from 2018 were used to validate the performance of AFA model. Measurements of the area under the curve (AUC), sensitivity, specificity, positive predictive value, and negative predicative value were used to evaluate the performance of the model. To rank the ovarian reserves of the whole population, we ranked the subgroups according to the predicted probability of poor ovarian response and further divided the 60 subgroups into 4 clusters, A-D, according to cut-off values consistent with the AAFA model. Results: The AUCs of the AFA and AAFA models were similar for the same validation set, with values of 0.853 (95% CI 0.841-0.865) and 0.850 (95% CI 0.838-0.862), respectively. We further ranked the ovarian reserves according to their predicted probability of poor ovarian response, which was calculated using our AFA model. The actual incidences of poor ovarian response in groups from A-D in the AFA model were 0.037 (95% CI 0.029-0.046), 0.128 (95% CI 0.099-0.165), 0.294 (95% CI 0.250-0.341), and 0.624 (95% CI 0.577-0.669), respectively. The order of ovarian reserve from adequate to poor followed the order from A to D. The clinical pregnancy rate, live-birth rate, and specific differences in groups A-D were similar when predicted using the AFA and AAFA models. Conclusions: This AFA model for assessing the true ovarian reserve was more convenient, cost-effective, and objective than our original AAFA model.

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