Machine learning center-specific models show improved IVF live birth predictions over US national registry-based model
Abstract Expanding in vitro fertilization (IVF) access requires improved patient counseling and affordability via cost-success transparency. Clinicians ask how two types of live birth prediction (LBP) models perform: machine learning, center-specific (MLCS) models and the multicenter, US national re...
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| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-04-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-58744-z |
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| Summary: | Abstract Expanding in vitro fertilization (IVF) access requires improved patient counseling and affordability via cost-success transparency. Clinicians ask how two types of live birth prediction (LBP) models perform: machine learning, center-specific (MLCS) models and the multicenter, US national registry-based model produced by Society for Assisted Reproductive Technology (SART). In a retrospective model validation study, we tested whether MLCS performs better than SART using 4635 patients’ first-IVF cycle data from 6 centers. MLCS significantly improved minimization of false positives and negatives overall (precision recall area-under-the-curve) and at the 50% LBP threshold (F1 score) compared to SART (p < 0.05). To contextualize, MLCS more appropriately assigned 23% and 11% of all patients to LBP ≥ 50% and LBP ≥ 75% whereas SART gave lower LBPs. Here, we show MLCS improves model metrics relevant for clinical utility – personalizing prognostic counseling and cost-success transparency – and is externally validated. We recommend evaluating MLCS in a larger sample of fertility centers. |
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| ISSN: | 2041-1723 |