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DATA

Anticipating Risks of Personalized Multiple Births After IVF

Benjamin M. Lannon, M.D., Bokyung Choi, M.S., Michele R. Hacker, S.D., Laura E. Dodge, M.P.H., Beth A. Malizia, M.D., C. Brent Barrett, Ph.D., Wing H. Wong, Ph.D., Mylene W. M. Yao, M.D., Alan S. Penzias, M.D

August 12, 2017
Emma Holt

Double Embryo Transfer

Risks of advanced reproductive technology 

Multiple gestation is the greatest risk of advanced reproductive technology (ART), according to this study. This is the presence of two or more embryos in the uterus and 3% of all births are multiple gestations. Patients and their physicians wrestle with how many embryos should be transferred during an IVF cycle because the transfer of multiple gives the couple a greater shot at pregnancy while increasing the risk of multiple birth. The problem with multiple births are the increase in costs and higher risks of neonatal and obstetric complications. Elective single embryo transfer eliminates this risk of multiple births but spontaneous identical twinning may occur.

In this study, the researchers address the issue of number of embryos to transfer during IVF by establishing a model to predict a patient’s individual risk of multiple live births. They further hypothesize that patients have unique probabilities that are influenced by their reproductive health data and the characteristics of their embryos.

Moving forward you’ll learn:

  • How many patients participated and clinic protocol for treatments 
  • Exclusions in the treatment cycles 
  • How the researchers predicted the possible risk of multiple births for each patient
  • The result of live births and multiple live births 

Materials & Methods

About the patients and treatments

The cohort included 33,741 IVF treatment cycles performed at Boston IVF beginning in January 2000 and ending December 2009. An ultrasound was performed 3-5 days after the oocyte retrieval according to clinic protocol, which was a preferred day-3 transfer. The presence of more than one heartbeat on an ultrasound confirmed multiple gestations. Then, the patients were followed for at least a full year from the start of their IVF cycles to confirm pregnancy outcomes.

The researchers excluded treatment cycles performed for a patient if any of her cycles fell within the following criteria: the first IVF cycles did not fall within the study period, the IVF cycles were performed after a patient already had a live birth resulting  from IVF, the IVF cycle was cancelled before oocyte retrieval, clinical outcome was unknown, or the patient was 43 years or older at the time of her first cycle. Further, treatment cycles that resulted in no live birth, occurred after 3 fresh cycles, or the number of transferred embryos did not equal 2 were not included as well.

Eligible cycles were computed by the log-likelihood based on the Bernoulli distribution and applied generalized boosted models (GBM). “Predictive power” is described as the improvement in the log-likelihood of predicting the probability of multiple births with MBP-BIVF relative to Age-BIVF prediction, in the context of Baseline-BIVF. Log-likelihoods were computed using generalized boosted models (GBM). Baseline-BIVF refers to the performance of a prediction model if no predictors were used - the overall multiple birth rate of those 2,413 treatments.

‍Results & Conclusion

The major findings of this study

Of the 33,741 treatment cycles performed, 25,595 cycles used fresh IVF and patients’ own eggs and involved 11,720 unique patients. 5,940 of the fresh, nondonar eggs, the IVF treatments resulted in live births and 1,682 were multiple births. The overall live and multiple rates were 22.8% and 28.3%. 

According to the authors, the major findings of this study are that patients have inherently different risks of multiple births and they can be predicted, no matter the age. By having the ability to know which patients are at risk of multiple pregnancy before placing embryos will improve how they decide the number of embryos to transfer. Even when only two embryos are transferred, the study shows that the patients’ risks of twins ranged from 12% to 55%. Estimating the risk and error for a particular patient can be computed by using available clinical data pertaining to the patients, their male partners, and their embryos.

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