ArticleInternational journal of reproductive biomedicine2025
Determining factors associated with the number of oocytes with appropriate morphology in infertile women: A cross-sectional study.
Article in International journal of reproductive biomedicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Infertility affects millions of couples worldwide. The number of morphologically normal oocytes (MNO) is a key determinant of assisted reproductive technology success. Accurate identification of influencing factors is limited by excess zeros in real-world clinical data. Objective: This study aimed to identify clinical factors associated with MNO count and to demonstrate the superiority of advanced statistical models, zero-inflated Poisson and semiparametric zero-inflated negative binomial regression, in analyzing high-quality clinical data with a high proportion of zeros. Materials and Methods: In this cross-sectional study, data of 950 infertile women who referred to the Royan Institute, Tehran, Iran, between January 2012 and December 2013 were extracted from their medical records. Zero-inflated Poisson and semiparametric zero-inflated negative binomial regression models were used to count data with a large number of zeros. Results: Ovarian surgery history (p = 0.045) and ovarian abnormalities (p = 0.041) significantly reduced MNO. Nonlinear inverse associations were observed with advancing age (p = 0.038), elevated luteinizing hormone/follicle-stimulating hormone ratio (p = 0.044), thyroid-stimulating hormone (p = 0.026), fasting blood sugar (p = 0.049), hirsutism score (p = 0.049), and increasing assisted reproductive technology cycles (p = 0.037). The semiparametric model provided the best fit and revealed complex nonlinear patterns not detectable by linear models. Conclusion: The results of this study enhance our understanding of the clinical and hormonal factors influencing oocyte morphology in infertile women and highlight the importance of applying advanced nonlinear statistical models in reproductive medicine research.
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