ArticleJournal of medical Internet research2020
An Ovarian Reserve Assessment Model Based on Anti-Müllerian Hormone Levels, Follicle-Stimulating Hormone Levels, and Age: Retrospective Cohort Study.
Article in Journal of medical Internet research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Enhanced predictive performance of artificial intelligence in individualized ovarian stimulation of in vitro fertilization: a retrospective cohort study.BMC medicine · 2026Article
- Ovarian intelligence: AI applications leveraging AMH and inhibin B.Frontiers in endocrinology · 2026Review
- Critical factors influencing live birth rates in fresh embryo transfer for IVF: insights from cluster ensemble algorithms.Scientific reports · 2025Article
- How to estimate the probability of a live birth after one or more complete IVF cycles? the development of a novel model in a single-center.BMC pregnancy and childbirth · 2025Article
- OvaRePred (HerTempo): an enhanced ovarian aging clock for personalized reserve assessment, endocrine age modeling, and predicting reproductive milestones across the female lifecycle.Frontiers in endocrinology · 2025Article
- Efficacy of Acupuncture for Diminished Ovarian Reserve: A Randomized Controlled Trial Protocol.International journal of women's health · 2025Article
- Obesity is associated with lower levels of negative emotions in polycystic ovary syndrome in clinical and animal studies.Annals of medicine · 2024Article
- Relationship between smoking, excessive androgen and negative emotions in women with polycystic ovary syndrome (PCOS).Journal of ovarian research · 2024Article
- Anti-Müllerian Hormone Level Determinants among Non-Polycystic-Ovary-Syndrome Women Undergoing In Vitro Fertilization: A Retrospective Cross-Sectional Study.Medicina (Kaunas, Lithuania) · 2024Article
- OvaRePred: Online tool for predicting the age of fertility milestones.Innovation (Cambridge (Mass.)) · 2023Review
- An online tool for predicting ovarian responses in unselected patients using dynamic inhibin B and basal antimüllerian hormone levels.Frontiers in endocrinology · 2023Article
- Article
- Assessment and quantification of ovarian reserve on the basis of machine learning models.Frontiers in endocrinology · 2023Article
- Development, promotion, and application of online OvAge calculator based on the WeChat applet: Clinical prediction model research.PloS one · 2023Article
- Ketone bodies determine the female reproductive lifespan.Life metabolism · 2022Article
- Relationship between body composition, insulin resistance, and hormonal profiles in women with polycystic ovary syndrome.Frontiers in endocrinology · 2022Article
- Homocysteine Level Related to Age Is Associated With Embryo Quality in Women Who Had IVF With Diminished Ovarian Reserve.Frontiers in reproductive health · 2022Article
- An online tool for predicting ovarian reserve based on AMH level and age: A retrospective cohort study.Frontiers in endocrinology · 2022Article
- A Model for Predicting Polycystic Ovary Syndrome Using Serum AMH, Menstrual Cycle Length, Body Mass Index and Serum Androstenedione in Chinese Reproductive Aged Population: A Retrospective Cohort Study.Frontiers in endocrinology · 2022Observational
- Clinical Applications of Serum Anti-Müllerian Hormone Measurements in Both Males and Females: An Update.Innovation (Cambridge (Mass.)) · 2021Review
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7 authors.
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Abstract
backgroundPreviously, we reported a model for assessing ovarian reserves using 4 predictors: anti-Müllerian hormone (AMH) level, antral follicle count (AFC), follicle-stimulating hormone (FSH) level, and female age. This model is referred as the AAFA (anti-Müllerian hormone level-antral follicle count-follicle-stimulating hormone level-age) model.
objectiveThis 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-Müllerian hormone level-follicle-stimulating hormone level-age) model.
methodsOocytes 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.
resultsThe 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.
conclusionsThis AFA model for assessing the true ovarian reserve was more convenient, cost-effective, and objective than our original AAFA model.
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