Predicting cumulative live birth after IVF with growth hormone supplementation: the role of pretreatment duration and machine learning-based models.
- 1Department of Gynaecology, The First People's Hospital of Jiashan, Jiashan Hospital Affiliated to Jiaxing University, Jiaxing, Zhejiang, China.
- 2Department of Orthopedics, Wenzhou Hospital of Integrated Chinese and Western Medicine, Wenzhou, Zhejiang, China.
- 3Reproductive Medicine Center, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
Abstract
Aim
To investigate the association between growth hormone (GH) pretreatment duration and cumulative live birth (CLB), identify key prognostic factors, and develop predictive models using logistic regression (LR) and machine learning (ML) algorithms among women undergoing in vitro fertilization (IVF) with GH supplementation.Methods
We conducted a retrospective cohort study of women undergoing IVF/intracytoplasmic sperm injection (ICSI) with GH between February 2015 and June 2023 at a single tertiary center. Five models were developed: LR, random forest (RF), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and support vector machine (SVM). Performance was assessed with cross-validation and bootstrap resampling. AUROC and Brier scores evaluated discrimination and calibration.Results
Among 1,782 women, 763 (42.82%) achieved at least one live birth. Maternal age, anti-Müllerian hormone (AMH), antral follicle count (AFC), the assisted reproductive technology (ART) cycle number, and GH duration were significant predictors. LR performed best (AUROC 0.782; Brier 0.187), showing strong calibration and interpretability. ML models had moderate discrimination but inferior calibration. AMH, AFC, and GH duration were key predictors across all methods.Conclusions
Both logistic regression and machine learning effectively predicted CLB after GH-pretreated IVF, but LR provided favorable discrimination, calibration, and interpretability. GH pretreatment ≥2 months was independently associated with higher odds of cumulative live birth.
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BibTeX
@article{chen2026predicting,
title = {{Predicting cumulative live birth after IVF with growth hormone supplementation: the role of pretreatment duration and machine learning-based models.}},
author = {Yufeng Chen and Ziyi Dai and Peiyu Wang and Chang Liu},
journal = {Frontiers in endocrinology},
year = {2026},
month = oct,
doi = {10.3389/fendo.2026.1967517},
url = {https://europepmc.org/article/MED/42908533},
note = {CC BY},
}
RIS
TY - JOUR
TI - Predicting cumulative live birth after IVF with growth hormone supplementation: the role of pretreatment duration and machine learning-based models.
AU - Yufeng Chen
AU - Ziyi Dai
AU - Peiyu Wang
AU - Chang Liu
PY - 2026
DA - 2026/10/07
JO - Frontiers in endocrinology
DO - 10.3389/fendo.2026.1967517
UR - https://europepmc.org/article/MED/42908533
AB - Aim To investigate the association between growth hormone (GH) pretreatment duration and cumulative live birth (CLB), identify key prognostic factors, and develop predictive models using logistic regression (LR) and machine learning (ML) algorithms among women undergoing in vitro fertilization (IVF) with GH supplementation.Methods We conducted a retrospective cohort study of women undergoing IVF/intracytoplasmic sperm injection (ICSI) with GH between February 2015 and June 2023 at a single tertiary center. Five models were developed: LR, random forest (RF), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and support vector machine (SVM). Performance was assessed with cross-validation and bootstrap resampling. AUROC and Brier scores evaluated discrimination and calibration.Results Among 1,782 women, 763 (42.82%) achieved at least one live birth. Maternal age, anti-Müllerian hormone (AMH), antral follicle count (AFC), the assisted reproductive technology (ART) cycle number, and GH duration were significant predictors. LR performed best (AUROC 0.782; Brier 0.187), showing strong calibration and interpretability. ML models had moderate discrimination but inferior calibration. AMH, AFC, and GH duration were key predictors across all methods.Conclusions Both logistic regression and machine learning effectively predicted CLB after GH-pretreated IVF, but LR provided favorable discrimination, calibration, and interpretability. GH pretreatment ≥2 months was independently associated with higher odds of cumulative live birth.
SN - 1664-2392
ER -