ArticleJournal of assisted reproduction and genetics2025
Predictive model for live birth outcomes in single euploid frozen embryo transfers: a comparative analysis of logistic regression and machine learning approaches.
Article in Journal of assisted reproduction and genetics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Developing a live birth prediction model for single euploid frozen embryo transfer using machine learning: a novel approach.Journal of assisted reproduction and genetics · 2025Article
- The Influence of Pre-IVF Day 2 TSH Levels on Treatment Success and Obstetric Outcomes: A Retrospective Single-Center Analysis with Machine Learning-Based Data Evaluation.Journal of clinical medicine · 2025Article
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7 authors.
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Abstract
purposeTo develop and validate a predictive model for live birth (LB) outcomes in single euploid frozen embryo transfers (seFET) based on patient's characteristics and embryo parameters.
methodsA retrospective cohort study was performed including 1979 seFET performed between March 2017 and December 2023. Prediction models were built using logistic regression (LR), random forest classifier (RFC), support vector machines (SVM), and a gradient booster (XGBoost). Considered variables associated with LB outcomes were blastocyst expansion, blastocyst inner cell mass (ICM) and TE quality, day (D) of TE biopsy (D5, D6, and D7), female age and body mass index (BMI), distance from the uterine fundus at embryo transfer, endometrial preparation as natural cycles (NC) or hormonal replacement therapy (HRT), and endometrial thickness. Model performance was evaluated using area under the precision-recall curve and calibration metrics.
resultsVariables that were negatively associated with LB rate were BMI (OR = 0.79 [0.64-0.96], P = 0.020 for overweight and OR = 0.76 [0.60-0.95], P = 0.015 for obese class I/II), ICM grade B (OR = 0.72 [0.57-0.90], P = 0.005) or C (OR = 0.21 [0.15-0.30], P < 0.001), TE grade C (OR = 0.32 [0.24-0.43], P < 0.001), and blastocyst biopsied on D6 (OR = 0.66 [0.55-0.80], P < 0.001 or D7 (OR = 0.19[0.09-0.37], P < 0.001). The LR model was the best in terms of overall classification performance (C-statistics: 0.626 ± 0.018 vs. 0.606 ± 0.018, 0.581 ± 0.018, 0.601 ± 0.017, LR vs. RFC, XGBoost, and SVM, respectively, P < 0.001). A prediction model of LB outcome was developed and is free to access: https://artfertilityclinics.shinyapps.io/ABLE/ .
conclusionLR demonstrated a stable validation performance and superior LB prediction, aiding as a predictive tool for patient counselling and assessing success in seFET cycles.
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