Abstract
BACKGROUND: Women display marked variability in ovarian reserve, which is pivotal for fertility and menopausal timing. Traditional criteria, such as Bologna and Poseidon, classify women into broad groups but do not provide individualized predictions for ovarian aging or reproductive milestones. This study aims to refine the AA model (AMH + age) to enhance clinical usability, robustness, and interpretability. MATERIALS AND METHODS: Single-center retrospective ART cohort (GnRH-antagonist cycles, 2017-2021). Training: 15,241 cycles (2017-2019); Testing: 14,498 cycles (2020-2021). Poor ovarian response (POR) was defined as <5 oocytes. Three logistic-regression specifications were compared: categorical (Model-0), continuous (Model-1), and polynomial (age quadratic, AMH cubic; Model-2). Discrimination (AUC), calibration, and net reclassification improvement (NRI) were evaluated. A two-parameter logistic curve was fitted to age versus predicted POR (used population-level as "predicted DOR") to construct an ovarian-aging trajectory and derive an interpretable "endocrine-age" index. Sensitivity analyses assessed cycle-day AMH variation; a community dataset was used to compare age-stratified AMH distributions. RESULTS: While all models achieved comparable discrimination (AUC ≈ 0.85), a cubic transformation model (Model-2) demonstrated superior calibration and was selected as the final algorithm. A two-parameter logistic curve allowed translation of ovarian reserve scores into an "endocrine age" and enabled individualized prediction of future milestones, such as diminished reserve with ovarian score of 50 and perimenopause, the lowest ovarian reserve score in our ART population. AMH sampling on different cycle days showed only modest effects from minor fluctuations; only substantial AMH decreases significantly affected prediction accuracy. Age-stratified AMH distributions were similar between ART and community cohorts in women <40, supporting external relevance. The updated OvaRePred (HerTempo) model is cost-effective, scalable, and operationally simple. CONCLUSION: OvaRePred (HerTempo) delivers individualized, well-calibrated estimates of ovarian reserve and an interpretable endocrine-age index and future fertility milestone onset. While the tool can inform personalized fertility planning and may have broader public-health utility, the algorithm is trained on ART endpoints. Any projections of future reproductive milestones derived from the population ovarian-aging curve-and the fixed-interval hypothesis that underpins that curve-are hypothesis-generating and require prospective validation, particularly in non-ART cohorts with longitudinal follow-up.