Preimplantation genetic testing for aneuploidy (PGT-A) has aided in selecting embryos for SET but has limitations including cost, embryologist expertise required to perform a trophectoderm biopsy, turnaround time which makes fresh embryo transfer logistically challenging, and possible embryo damage from the biopsy. According to the CDC 2016 ART National Summary Report, the average number of embryos transferred for different age groups ranged from 1.4 to 1.6 embryos in autologous frozen embryo transfer (FET) cycles and from 1.5 to 2.4 embryos in autologous fresh cycles 1. Some patients may desire multiple embryo transfer for cost effectiveness, after failing one or more SETs, or for a cleavage stage embryo transfer. Single embryo transfer (SET) is a great option to limit the risk of multiple pregnancies but carries important limitations. There is clearly a need for a simple, accurate, and quantitative model that can be updated with current data and easily applied to individual clinic data. This begs the question: why have these models not been adopted for use in clinical practice? Several possible explanations include complexity that limits implementation, the information provided in some studies is not clinically useful, rapidly advancing IVF technology outdates models quickly, and reluctance to use a model that is not easy to understand. This concept of universal factors is supported by previous research that found higher rates of multiple pregnancy than predicted by a model which did not account for universal factors 9.ĭespite the availability of these models, routine use of prediction models to guide the number of embryos to transfer is not practiced and current guidelines are based on general rather than individual clinic data 3. Taking universal factors into account results in higher predicted rates of multiples and lower predicted rates of singleton pregnancy than would be expected if embryos simply combined according to individual embryo implantation rates (as if each embryo is modeled as a coin flip). If universal factors are favorable all embryos are more likely to implant than if universal factors are not favorable. These factors have been described in both general and specific terms such as “uterine receptivity”, “endometrial receptivity”, and “transfer efficiency” 4, 5. Evidence supports the main assumption of the Speirs model that an individual embryo’s probability of implantation and live birth is not directly impacted by other embryos but there are universal factors that affect all embryos transferred concurrently 4, 9, 11, 12. Typical models build on the Speirs model which assumes that embryos implant independently when uterine factors are favorable 4. Many previous models have been developed to guide the number of embryos to transfer 4, 5, 6, 7, 8, 9, 10. A recent American Society for Reproductive Medicine (ASRM) practice committee document gives recommended limits to the number of embryos to transfer and encourages individual clinics to use their own data in order to minimize multiple gestations 3. Multiple gestations increase maternal and fetal morbidity and mortality not only through preterm labor and delivery but also preeclampsia, gestational diabetes, and other pregnancy complications 2. According to the 2016 Center for Disease Control (CDC) Assisted Reproductive Technology (ART) National Summary Report, of the 19,137 live deliveries from ART cycles using fresh embryos with fresh nondonor eggs, 18.8% were twin deliveries and 0.6% were triplets or more 1. This quantitative model or a simplified version can be used for clinics to generate and analyze their own data to guide the number of embryos to transfer to limit the risk of multiple gestations.Īlthough the goal of in vitro fertilization (IVF) and other fertility treatments is a single healthy intrauterine pregnancy and delivery, twins and higher order multiples are a frequent occurrence. The live birth rates per embryo varied from as high as 43% for fresh blastocysts in the 35-year-old age group to as low as 1% for frozen cleavage stage embryos in the 43-year-old age group. The standardized errors for rates of singleton and twin deliveries were normally distributed and the mean errors were not significantly different from zero (all p > 0.05). The model presented accounts for patient age, embryo stage (cleavage or blastocyst), type of transfer cycle (fresh or frozen) and uterine/universal factors. The predicted and observed rates of singleton and twin deliveries were compared in a tenfold cross-validation study using data from a single clinic. We developed a model that can be fit to individual clinic data for predicting singleton, twin, and total live birth rates after human embryo transfer. Accurately predicting the probability of live birth and multiple gestations is important for determining a safe number of embryos to transfer after in vitro fertilization.
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