ArticleJournal of assisted reproduction and genetics2020
A novel mathematical model of true ovarian reserve assessment based on predicted probability of poor ovarian response: a retrospective cohort study.
Article in Journal of assisted reproduction and genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Modeling Diagnosis and Clinical Outcomes in Infertility Couples Considering Multiple Factors: A Retrospective Study.International journal of fertility & sterility · 2026Article
- Ovarian intelligence: AI applications leveraging AMH and inhibin B.Frontiers in endocrinology · 2026Review
- A Two-Hit Hypothesis for Chemotherapy-Induced Primary Ovarian Insufficiency in Asian Populations: A Population-Specific Mechanistic Framework Linking Genetic Susceptibility and Cytotoxic Stress via the PI3K-AKT-FOXO3 Axis.BioMed research international · 2026Review
- Artificial intelligence in primary ovarian insufficiency management: opportunities and challenges.Journal of assisted reproduction and genetics · 2025Review
- Study on the influence of the sY1192 gene locus in the AZFb/c region on sperm quality and pregnancy outcome.Asian journal of andrology · 2025Article
- Efficacy of Acupuncture for Diminished Ovarian Reserve: A Randomized Controlled Trial Protocol.International journal of women's health · 2025Article
- Harnessing Artificial Intelligence to Predict Ovarian Stimulation Outcomes in In Vitro Fertilization: Scoping Review.Journal of medical Internet research · 2024Article
- Development and validation of a prediction model for suboptimal ovarian response in polycystic ovary syndrome (PCOS) patients undergoing GnRH-antagonist protocol in IVF/ICSI cycles.Journal of ovarian research · 2024Article
- Article
- Development and validation of a prediction model for unexpected poor ovarian response during IVF/ICSI.Frontiers in endocrinology · 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
- Assessment and quantification of ovarian reserve on the basis of machine learning models.Frontiers in endocrinology · 2023Article
- Article
- Changes in the prevalence of polycystic ovary syndrome in China over the past decade.The Lancet regional health. Western Pacific · 2022Article
- The (decision) tree of fertility: an innovative decision-making algorithm in assisted reproduction technique.Journal of assisted reproduction and genetics · 2022Article
- An online tool for predicting ovarian reserve based on AMH level and age: A retrospective cohort study.Frontiers in endocrinology · 2022Article
- Clinical Applications of Serum Anti-Müllerian Hormone Measurements in Both Males and Females: An Update.Innovation (Cambridge (Mass.)) · 2021Review
- Metabolomic Profiling of Poor Ovarian Response Identifies Potential Predictive Biomarkers.Frontiers in endocrinology · 2021Article
- An Ovarian Reserve Assessment Model Based on Anti-Müllerian Hormone Levels, Follicle-Stimulating Hormone Levels, and Age: Retrospective Cohort Study.Journal of medical Internet research · 2020Article
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11 authors.
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Abstract
purposeTo establish a mathematical model for assessing the true ovarian reserve based on the predicted probability of poor ovarian response (POR).
methodsIn this retrospective cohort study, a total of 1523 GnRH-antagonist cycles in 2017 were firstly analyzed. The ovarian responses were calculated based on the number of retrieved oocytes. The continuous variables were converted into categorical variables according to cutoff values generated by the decision tree method. The optimal model was identified using forward stepwise multiple logistic regression with 5-fold cross-validation and further verified its performances using outer validation data.
resultsThe predictors in our model were anti-Müllerian hormone (AMH), antral follicle counts (AFC), basal follicle-stimulating hormone (FSH), and age, in order of their significance, named AAFA model. The AUC, sensitivity, specificity, positive predictive value, and negative predictive value of AAFA model in inner validation and outer validation data were 0.861 and 0.850, 0.603 and 0.519, 0.917 and 0.930, 0.655 and 0.570, and 0.899 and 0.915. Ovarian reserve of 16 subgroups was further ranked according to the predicted probability of POR and further divided into 4 groups of A-D using clustering analysis. The incidence of POR in the four groups was 0.038 (0.030-0.046), 0.139 (0.101-0.177), 0.362 (0.308-0.415), and 0.571 (0.525-0.616), respectively. The order of ovarian reserve from adequate to poor followed the order of A to D.
conclusionWe have established an easy applicable AAFA model for assessing true ovarian reserve and may have important implications in both infertile women and general reproductive women in Chinese or Asian population.
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