AUTHOR=Xu Xiaohang , Wang Xue , Jiang Yilin , Sun Haoyue , Chen Yuanhui , Zhang Cuilian 

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

JOURNAL=Frontiers in Endocrinology

VOLUME=Volume 15 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2024.1340329

DOI=10.3389/fendo.2024.1340329

ISSN=1664-2392

ABSTRACT=Background: Identifying the 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 about the potential risk factors that may predict the occurrence of unexpected POR. The objective of this developed predictive model is to assess the individual probability of unexpected POR during in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI) treatments. Methods: The 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) of from January 2019 to December 2022. Univariate regression analyses and least absolute shrinkage and selection operator regression analysis were employed to ascertain characteristics that were associated with POR. Subsequently, the selected variables were utilized to construct the nomogram. Results: The 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 dosage 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 this model.The nomogram can anticipate the probability of unexpected POR in IVF/ICSI treatment, thereby assisting professionals in making appropriate clinical judgments and helping patients in effectively managing anticipations.