Development and validation of a prediction model for unexpected poor ovarian response during IVF/ICSI

Abstract

BackgroundIdentifying poor ovarian response (POR) among patients with good ovarian reserve poses a significant challenge within reproductive medicine. Currently, there is a lack of published data on the potential risk factors that could predict the occurrence of unexpected POR. The objective of this study was to develop a predictive model to assess the individual probability of unexpected POR during in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI) treatments.MethodsThe development of the nomogram involved a cohort of 10,404 patients with normal ovarian reserve [age, ≤40 years; antral follicle count (AFC), ≥5; and anti-Müllerian hormone (AMH), ≥1.2 ng/ml] from January 2019 to December 2022. Univariate regression analyses and least absolute shrinkage and selection operator regression analysis were employed to ascertain the characteristics associated with POR. Subsequently, the selected variables were utilized to construct the nomogram.ResultsThe predictors included in our model were body mass index, basal follicle-stimulating hormone, AMH, AFC, homeostasis model assessment of insulin resistance (HOMA-IR), protocol, and initial dose of gonadotropin. The area under the receiver operating characteristic curve (AUC) was 0.753 [95% confidence interval (CI) = 0.7257–0.7735]. The AUC, along with the Hosmer–Lemeshow test (p = 0.167), demonstrated a satisfactory level of congruence and discrimination ability of the developed model.ConclusionThe nomogram can anticipate the probability of unexpected POR in IVF/ICSI treatment, thereby assisting professionals in making appropriate clinical judgments and in helping patients to effectively manage expectations.