Identifying key predictive features for live birth rate in advanced maternal age patients undergoing single vitrified-warmed blastocyst transfer.

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  • Additional Information
    • Source:
      Publisher: BioMed Central Country of Publication: England NLM ID: 101153627 Publication Model: Electronic Cited Medium: Internet ISSN: 1477-7827 (Electronic) Linking ISSN: 14777827 NLM ISO Abbreviation: Reprod Biol Endocrinol Subsets: MEDLINE
    • Publication Information:
      Original Publication: London : BioMed Central, 2003-
    • Subject Terms:
      Maternal Age* ; Live Birth*/epidemiology ; Embryo Transfer*/methods ; Vitrification*; Humans ; Female ; Adult ; Pregnancy ; Retrospective Studies ; Birth Rate ; Cryopreservation/methods ; Pregnancy Rate ; Machine Learning
    • Abstract:
      Background: Infertility affects one in six couples worldwide, with advanced maternal age (AMA) posing unique challenges due to diminished ovarian reserve and reduced oocyte quality. Single vitrified-warmed blastocyst transfer (SVBT) has shown promise in assisted reproductive technology (ART), but success rates in AMA patients remain suboptimal. This study aimed to identify and refine predictive factors for live birth following SVBT in AMA patients, with the goal of enhancing clinical decision-making and enabling personalized treatment strategies.
      Methods: This retrospective cohort study analyzed 1,168 SVBT cycles conducted between June 2016 and December 2022 at the First Affiliated Hospital of Guangxi Medical University and Nanning Maternity and Child Health Hospital. Nineteen machine-learning models were applied to identify key predictive factors for live birth. Feature selection and 10-fold cross-validation were employed to validate the models.
      Results: The most significant predictors of live birth included inner cell mass quality, trophectoderm quality, number of oocytes retrieved, endometrial thickness, and the presence of 8-cell blastomeres on day 3. The stacking model demonstrated the best predictive performance (AUC: 0.791), followed by Extra Trees (AUC: 0.784) and Random Forest (AUC: 0.768). These models outperformed traditional methods, achieving superior accuracy, sensitivity, and specificity.
      Conclusion: Leveraging advanced machine-learning models and identifying critical predictive factors can improve the accuracy of live birth outcome predictions for AMA patients undergoing SVBT. These findings offer valuable insights for enhancing clinical decision-making and managing patient expectations. Further research is needed to validate these results in larger, multi-center cohorts and to explore additional factors, including fresh embryo transfers, to broaden the applicability of these models in clinical practice.
      (© 2024. The Author(s).)
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    • Grant Information:
      Z-A20230585 Self-funded scientific research project of the Health and Family Planning Commission of Guangxi Zhuang Autonomous Region; Z-A20230585 Self-funded scientific research project of the Health and Family Planning Commission of Guangxi Zhuang Autonomous Region; Z-A20230585 Self-funded scientific research project of the Health and Family Planning Commission of Guangxi Zhuang Autonomous Region; Z-A20230585 Self-funded scientific research project of the Health and Family Planning Commission of Guangxi Zhuang Autonomous Region; Z-A20230585 Self-funded scientific research project of the Health and Family Planning Commission of Guangxi Zhuang Autonomous Region; Z-A20230585 Self-funded scientific research project of the Health and Family Planning Commission of Guangxi Zhuang Autonomous Region
    • Contributed Indexing:
      Keywords: Advanced maternal age (AMA); Assisted Reproductive Technology (ART); Live birth rate; Machine-learning; Predictive modeling; Single vitrified-warmed blastocyst transfer (SVBT)
    • Publication Date:
      Date Created: 20241007 Date Completed: 20241008 Latest Revision: 20241010
    • Publication Date:
      20241010
    • Accession Number:
      PMC11457422
    • Accession Number:
      10.1186/s12958-024-01295-7
    • Accession Number:
      39375693