Ovarian response to controlled stimulation varies considerably among patients, and this variability remains one of the more consequential challenges in in vitro fertilization (IVF) practice.1 Even when protocols are appropriately selected and administered, differences in follicular sensitivity, age-related decline in oocyte competence, ovarian reserve, and underlying reproductive diagnosis can all contribute to lower-than-expected oocyte yield and cycle outcomes.1–3
Ovarian reserve markers, including anti-Müllerian hormone (AMH) and antral follicle count (AFC), have become standard tools for predicting response category and guiding protocol selection. However, these markers do not fully capture the biological heterogeneity that leads to suboptimal follicular recruitment in some patients despite adequate stimulation.1,2 False-positive predictions occur at meaningful rates, underscoring the limitations of relying on any single marker in isolation.2 This gap is clinically significant: a patient with reassuring AMH and AFC values may still experience unexpected poor response, leaving both clinician and patient without a clear mechanistic explanation or an obvious path to protocol adjustment.
Emerging evidence supports the value of multivariate prediction models that integrate ovarian reserve markers with clinical variables such as BMI, insulin metabolism, and prior stimulation response.2 These models have demonstrated improved predictive accuracy compared with individual markers alone, suggesting that a more comprehensive assessment framework may better identify patients at risk before cycle initiation. Incorporating gonadotropin starting dose and hormonal response patterns from prior cycles may further refine these models, though prospective validation across diverse populations remains an important next step.
For reproductive endocrinologists, this has practical implications. Patients who fall outside expected response ranges despite standard protocols may benefit from earlier, more structured risk stratification.4 As the field moves toward greater individualization, refining the tools available for pre-cycle assessment represents an important opportunity to improve outcomes and reduce cycle-to-cycle unpredictability.
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