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Predicted probabilities of live birth following assisted reproductive technology using United States national surveillance data from 2016-2018

  • Audrey J. Gaskins
    Correspondence
    Corresponding Author: Audrey Gaskins, Sc.D., 1518 Clifton Road, CNR 3017, Atlanta, GA 30322. Phone: 404-727-5409;
    Affiliations
    Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, 30306

    Division of Reproductive Health, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, GA, 30341
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  • Yujia Zhang
    Affiliations
    Division of Reproductive Health, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, GA, 30341
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  • Jeani Chang
    Affiliations
    Division of Reproductive Health, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, GA, 30341
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  • Dmitry M. Kissin
    Affiliations
    Division of Reproductive Health, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, GA, 30341
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Published:January 22, 2023DOI:https://doi.org/10.1016/j.ajog.2023.01.014
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      ABSTRACT.

      Background

      As the use of in vitro fertilization (IVF) continues to increase in the US, up-to-date models that estimate cumulative live birth rates following multiple oocyte retrievals and embryo transfers (fresh and frozen) are valuable for patients and clinicians weighing treatment options.

      Objective

      To develop models that generate predicted probabilities of live birth in individuals considering IVF based on demographic and reproductive characteristics.

      Study Design

      Our population-based cohort study utilized data from the National Assisted Reproductive Technology Surveillance System 2016-2018, including 196,916 women who underwent 207,766 autologous embryo transfer cycles and 25,831 women who underwent 36,909 donor oocyte transfer cycles. We used data on autologous IVF cycles to develop models that estimate a patient’s cumulative live birth rate (CLBR) following all embryo transfers (fresh and frozen) within 12 months after one, two, and three oocyte retrievals in new and returning patients. Among patients using donor oocytes, we estimated CLBR after their first, second, and third embryo transfers. Multinomial logistic regression models adjusted for age, pre-pregnancy body mass index (BMI, imputed for 18% of missing values), parity, gravidity, and infertility diagnoses were used to estimate CLBR.

      Results

      Among new and returning patients undergoing autologous IVF, female age had the strongest association with CLBR. Other factors associated with higher CLBRs were lower BMI and parity or gravidity ≥1 although results were inconsistent. Infertility diagnoses of diminished ovarian reserve, uterine factor, and other reasons were associated with lower CLBR while male factor, tubal factor, ovulatory disorders, and unexplained infertility were associated with higher CLBR. Based on our models, a new patient who is 35 years, with a BMI of 25 kg/m2, no previous pregnancies, and unexplained infertility diagnoses has a 48%, 69%, and 80% CLBR following first, second, and third oocyte retrieval. CLBRs are 29%, 48%, 62% respectively if the patient had diminished ovarian reserve; and 25%, 41%, and 52% if the patient was 40 years (with unexplained infertility). Very few recipient characteristics were associated with CLBR in donor oocyte patients.

      Conclusions

      Our models provide estimates of CLBR based on demographic and reproductive characteristics to help inform patients and providers of a woman’s likelihood of success following IVF.

      Keywords

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