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<p>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 (<i>p</i> &lt; 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.</p>

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Machine learning center-specific models show improved IVF live birth predictions over US national registry-based model

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月17日

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  • Mylene W. M. Yao,
  • Elizabeth T. Nguyen,
  • Matthew G. Retzloff,
  • L. April Gago,
  • John E. Nichols,
  • John F. Payne,
  • Barry A. Ripps,
  • Michael Opsahl,
  • Jeremy Groll,
  • Ronald Beesley,
  • Gregory Neal,
  • Jaye Adams,
  • Lorie Nowak,
  • Trevor Swanson,
  • Xiaocong Chen

摘要

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.