Prediction of low oocyte retrieval in young women undergoing IVF/ICSI: a multicenter external validation study

Abstract Objective To externally validate a previously developed pretreatment prediction nomogram for identifying young women at risk of low oocyte retrieval (LOR). Methods This multicenter retrospective study included 14,702 women aged ≤ 35 years undergoing their first in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI) cycle between January 2023 and December 2025 across six reproductive centers. The previously developed pretreatment prediction model for low oocyte retrieval was externally validated using both temporal validation in the original center and geographic validation across five independent centers. All predictors were obtained prior to ovarian stimulation, including age, anti-Müllerian hormone (AMH), antral follicle count (AFC), basal follicle-stimulating hormone (FSH), and the FSH to luteinizing hormone ratio. Model discrimination was assessed using the area under the receiver operating characteristic curve (AUC), while calibration was evaluated using calibration plots and mean absolute error (MAE). Clinical utility was assessed using decision curve analysis (DCA). Results The overall LOR rate was 47.2%. The model showed good discrimination across centers, with AUC values ranging from 0.800 to 0.888, and an overall AUC of 0.836 (95% CI 0.829–0.842). Calibration analysis demonstrated good agreement between predicted and observed probabilities, with mean absolute error values ranging from 0.004 to 0.040. Decision curve analysis indicated a higher net benefit of the nomogram compared with “treat-all” and “treat-none” strategies across a wide range of threshold probabilities. Although no statistically significant association was observed between center-level characteristics and model discrimination, variation in AUC across centers may reflect differences in patient characteristics. Conclusion This nomogram provides a practical tool for early risk stratification of low oocyte retrieval in young women using routinely available clinical parameters, which may facilitate individualized ovarian stimulation strategies and improve patient counseling in IVF practice.

Authors

Institutions

Publication Details

Journal
Journal of Ovarian Research
Published
2026-09-16
DOI
https://doi.org/10.1186/s13048-026-02266-9
Primary Topic
Ovarian function and disorders
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
article

Prediction of low oocyte retrieval in young women undergoing IVF/ICSI: a multicenter external validation study

Haiyan Yang, Chunxiu Dai, Yili Teng, Yanning Lv et al.
Journal of Ovarian Research
Ovarian function and disorders
article

Prediction of low oocyte retrieval in young women undergoing IVF/ICSI: a multicenter external validation study

Haiyan Yang, Chunxiu Dai, Yili Teng, Yanning Lv, Xianghong Fu, Zhengao Sun, Huan Zhang, Qin Zhu, Chang Liu, Shun Zhang
article en

Abstract

Abstract Objective To externally validate a previously developed pretreatment prediction nomogram for identifying young women at risk of low oocyte retrieval (LOR). Methods This multicenter retrospective study included 14,702 women aged ≤ 35 years undergoing their first in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI) cycle between January 2023 and December 2025 across six reproductive centers. The previously developed pretreatment prediction model for low oocyte retrieval was externally validated using both temporal validation in the original center and geographic validation across five independent centers. All predictors were obtained prior to ovarian stimulation, including age, anti-Müllerian hormone (AMH), antral follicle count (AFC), basal follicle-stimulating hormone (FSH), and the FSH to luteinizing hormone ratio. Model discrimination was assessed using the area under the receiver operating characteristic curve (AUC), while calibration was evaluated using calibration plots and mean absolute error (MAE). Clinical utility was assessed using decision curve analysis (DCA). Results The overall LOR rate was 47.2%. The model showed good discrimination across centers, with AUC values ranging from 0.800 to 0.888, and an overall AUC of 0.836 (95% CI 0.829–0.842). Calibration analysis demonstrated good agreement between predicted and observed probabilities, with mean absolute error values ranging from 0.004 to 0.040. Decision curve analysis indicated a higher net benefit of the nomogram compared with “treat-all” and “treat-none” strategies across a wide range of threshold probabilities. Although no statistically significant association was observed between center-level characteristics and model discrimination, variation in AUC across centers may reflect differences in patient characteristics. Conclusion This nomogram provides a practical tool for early risk stratification of low oocyte retrieval in young women using routinely available clinical parameters, which may facilitate individualized ovarian stimulation strategies and improve patient counseling in IVF practice.

Journal of Ovarian Research
Shandong University of Traditional Chinese Medicine (CN), Guilin Medical University (CN), Wenzhou Medical University (CN), First Affiliated Hospital of Wenzhou Medical University (CN), Lishui University (CN), Quzhou City People's Hospital (CN), Wenzhou Hospital of Traditional Chinese Medicine (CN), Lishui Central Hospital (CN), Lishui City People's Hospital (CN), First Hospital of Jiaxing (CN), Affiliated Hospital of Shandong University of Traditional Chinese Medicine (CN)
Gender equality
Openalex Percentile: Top 9%
Ovarian function and disorders
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.