Predicting Live Birth Rates for Women Using Assisted Reproductive Technology

Using data containing demographic and reproductive information in the United States, researchers were able to create models that predicted cumulative live birth rates (CLBR) for women using assisted reproductive technologies (ART), including in vitro fertilization (IVF).

The results of this population-based cohort study were published in the American Journal of Obstetrics & Gynecology.

“Although previous IVF prediction models have been developed,6 most models are using older data (before 2010) and predict the probability of a live birth after a fresh embryo transfer (ET)," the researchers wrote. They added that the "the practice of IVF is rapidly changing."

Assisted reproductive technologies such as IVF with or without intracytoplasmic sperm injection have become a main treatment option for individuals and couples facing fertility problems. This study aimed to provide up-to-date and accurate models that could predict the probability of success after IVF, to help patients choose the best treatment option available to them.

This study included data from the National Assisted Reproductive Technology Surveillance System from 2016 to 2018, containing approximately 98% of all IVF cycles that were performed in the United States. The data included 96,916 women who underwent 207,766 autologous ET cycles and 25,831 women who underwent 36,909 donor oocyte transfer cycles.

The researchers focused their models on 3 patient populations: new patients with no history of ovarian stimulation, undergoing autologous ART who had an intended oocyte retrieval in 2016 or 2017; return patients with a history of ovarian stimulation, undergoing autologous ART who had at least 1 additional intended oocyte retrieval in 2016 or 2017; patients who used fresh or frozen donor oocytes for the first time for an ET cycle in 2016 or 2017.

Using IVF data cycle data, the researchers estimated patient cumulative live birth rate after all ETs within 12 months after 1, 2, and 3 oocyte retrievals in new and returning patients.

Characteristics of these woman that were included in the study were age, body mass index (BMI), race and ethnicity, number of previous pregnancies, number of previous live births, previous IVF treatments, and infertility diagnosis or reason for IVF (male factor, endometriosis, tubal factor, and ovulatory disorder).

As a result, among both new and returning patients undergoing autologous IVF, female age showed the highest association with CLBR, having a lower BMI and parity (the number of times that she has given birth to a fetus with a gestational age of 24 weeks or more) or gravidity (the number of times that a woman has been pregnant) of 1 or more. Male factor, tubal factor, ovulatory disorders, and unexplained infertility was also associated with a higher CLBR.

In comparison, an infertility diagnosis of diminished ovarian reserve or uterine factor was associated with a lower CLBR.

Using the models created by the researchers, a new patient aged 35 years, with a BMI of 25kg/m², no previous pregnancy, and an unexplained infertility diagnosis, had a 48%, 69%, and 80% CLBR after the first, second, and third oocyte retrieval, respectively.

If the patient had diminished ovarian reserve, their CLBRs were 29%, 48%, and 62%. If the patient was 40 years old with unexplained infertility, their CLBRs were 25%, 41%, and 52%.

Lastly, the researchers found that very few recipient characteristics were associated with CLBR in donor oocyte patients, which they believe may have happened because the model only weakly associated with CLBR, and the sample size was more limited.

Despite limitations, the researchers believe these probability models are important tools that help to inform patients and providers regarding a woman’s chance of giving birth after IVF.

“Using a large, national dataset encompassing nearly all IVF cycles in the United States, our study developed clinical models for use in new and return patients undergoing autologous IVF and donor oocyte recipient patients to provide individualized estimates of cumulative probability of live birth after multiple fresh and frozen ETs,” concluded the researchers.

Reference

Gaskins AJ, Zhang Y, Chang J, Kissin DM. Predicted probabilities of live birth following assisted reproductive technology using United States National Surveillance Data from 2016 to 2018. American Journal of Obstetrics and Gynecology. Published online January 23, 2023. doi:10.1016/j.ajog.2023.01.014