Analysis of factors affecting embryo implantation

A validated model for predicting live birth after 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. 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. The predicted and observed rates of singleton and twin deliveries were compared in a tenfold cross-validation study using data from a single clinic. The model presented accounts for patient age, embryo stage (cleavage or blastocyst), type of transfer cycle (fresh or frozen) and uterine/universal factors. 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 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. 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.

Predictive Modeling of Implantation Outcome in an In Vitro Fertilization Setting

Background. Multiple embryo transfers in in vitro fertilization (IVF) treatment increase the number of successful pregnancies while elevating the risk of multiple gestations. IVF-associated multiple pregnancies exhibit significant financial, social, and medical implications. Clinicians need to decide the number of embryos to be transferred considering the tradeoff between successful outcomes and multiple pregnancies. Objective. To predict implantation outcome of individual embryos in an IVF cycle with the aim of providing decision support on the number of embryos transferred. Design. Retrospective cohort study. Data Source. Electronic health records of one of the largest IVF clinics in Turkey. The study data set included 2453 embryos transferred at day 2 or day 3 after intracytoplasmic sperm injection (ICSI). Each embryo was represented with 18 clinical features and a class label, +1 or -1, indicating positive and negative implantation outcomes, respectively. Methods. For each classifier tested, a model was developed using two-thirds of the data set, and prediction performance was evaluated on the remaining one-third of the samples using receiver operating characteristic (ROC) analysis. The training-testing procedure was repeated 10 times on randomly split (two-thirds to one-third) data. The relative predictive values of clinical input characteristics were assessed using information gain feature weighting and forward feature selection methods. Results. The naïve Bayes model provided 80.4% accuracy, 63.7% sensitivity, and 17.6% false alarm rate in embryo-based implantation prediction. Multiple embryo implantations were predicted at a 63.8% sensitivity level. Predictions using the proposed model resulted in higher accuracy compared with expert judgment alone (on average, 75.7% and 60.1%, respectively). Conclusions. A machine learning–based decision support system would be useful in improving the success rates of IVF treatment.

Quality of additional embryos transferred on pregnancy outcomes in IVF: predictions using a mathematical approach

This study assessed the influence of adding embryos with different embryo quality on pregnancy rate and multiple pregnancy rate (MPR). The study included 1891 IVF transfers performed at two centres with different embryo transfer policies. Pregnancy rate and MPR were analysed following three models and then including embryo quality. A predictive mathematical model and two scatter plots were constructed. The model based on embryo independence was incompatible with the observed data, while both the ground and collaborative models provided excellent fits. The collaborative model, however, predicted multiple pregnancies, especially triplets, more accurately. Transfer of additional embryos, irrespective of embryo quality, always increased pregnancy rate and MPR. When implantation rate was low, there was a marked increase in pregnancy rate but only a relatively small increase in MPR. In contrast, with higher implantation rates, the increase in pregnancy rate was mainly due to the increase in MPR, with the same singleton pregnancy rate. Transfer of additional embryos, irrespective of embryo quality, follows a collaborative pattern and always results in an increase in pregnancy rate and MPR. The scatter plots accurately predicted the influence of the different combinations of number and embryo quality on pregnancy rate and MPR.

Reducing multiples: a mathematical formula that accurately predicts rates of singletons, twins, and higher-order multiples in women undergoing in vitro fertilization

OBJECTIVE

To develop a mathematical formula that accurately predicts the probability of a singleton, twin, and higher-order multiple pregnancy according to implantation rate and number of embryos transferred.

DESIGN

A total of 12,003 IVF cycles from a single center resulting in ET were analyzed. Using mathematical modeling we developed a formula, the Combined Formula, and tested for the ability of this formula to accurately predict outcomes.

SETTING

Academic hospital.

PATIENT(S)

Patients undergoing IVF.

INTERVENTION(S)

None.

MAIN OUTCOME MEASURE(S)

Goodness of fit of data from our center and previously published data to the Combined Formula and three previous mathematical models.

RESULT(S)

The Combined Formula predicted the probability of singleton, twin, and higher-order pregnancies more accurately than three previous formulas (1.4% vs. 2.88%, 4.02%, and 5%, respectively) and accurately predicted outcomes from five previously published studies from other centers. An online applet is provided (https://secure.ivf.org/ivf-calculator.html).

CONCLUSION(S)

The probability of pregnancy with singletons, twins, and higher-order multiples according to number of embryos transferred is predictable and not random and can be accurately modeled using the Combined Formula. The embryo itself is the major predictor of pregnancy outcomes, but there is an influence from "barriers," such as the endometrium and collaboration between embryos (embryo-embryo interaction). This model can be used to guide the decision regarding number of embryos to transfer after IVF.

The implantation of every embryo facilitates the chances of the remaining embryos to implant in an IVF programme: a mathematical model to predict pregnancy and multiple pregnancy rates

BACKGROUND

We aimed to assess the validity of a theoretical mathematical model to predict the pregnancy rate and the multiple pregnancy rate in IVF/oocyte donation programmes on the basis of the implantation rate and the number of transferred embryos.

METHODS

A total of 1835 embryo transfers corresponding to three different programmes in two centres with different implantation rates were analysed. Pregnancy and multiple pregnancy rates observed in the aforementioned programmes were compared with those obtained following different mathematical models. Four models were tested: binomial model, ground model, maternal variability model and collaborative model. The goodness of fit was performed by means of the maximum likelihood fit method.

RESULTS

The binomial model could not predict the pregnancy rate, and especially the multiple pregnancy rate. The multiple pregnancy rate predicted following the binomial model was much lower than observed, up to 40-fold reduced. Ground model and maternal variability model adjusted to the data with more precision, but were still not accurate. Finally, the collaborative model reproduced with very great accuracy both pregnancy rate and the multiple pregnancy rate. A collaborative parameter of 22% was found, implying that the implantation probability of each embryo is increased by 22% for every embryo previously implanted.

CONCLUSIONS

Embryonic implantation does not follow a binomial law, showing that the implantation is not independent from the number of embryos implanted. The best fit to the data is obtained following a collaborative model by which the implantation of one embryo is facilitated by the implantation of other embryo(s). The mathematical formula of the collaborative model predicts very accurately the pregnancy rate and the multiple pregnancy rate in IVF/oocyte donation programmes, based on the implantation rate of this specific programme and the number of embryos transferred up to five embryos. We recommend using the aforementioned formula to quantify the pregnancy rate and the risk of multiple pregnancy in the counselling of the infertile couple at embryo transfer. Such a formula is freely available at www.ifca.unican.es/matorras/mathpreg/.

Factors predicting IVF treatment outcome: a multivariate analysis of 5310 cycles

The objective of this study was to analyse factors predicting live birth rate following IVF. A computerized database of 1928 women who underwent 5310 consecutive IVF cycles in a single IVF unit was evaluated. Data on the women's age, number of retrieved oocytes, performance of intracytoplasmic sperm injection (ICSI), aetiology of infertility, number of transferred embryos and option of choosing embryos for transfer were evaluated. There were 1126 pregnancies that resulted in 689 live births. Transferring two embryos doubled the chances of delivery compared with one embryo, but transferring three embryos was not significantly superior to two embryos. Moreover, following a three-embryo transfer, the multiple delivery rates were significantly higher (P < 0.01) compared with transferring two embryos. Optimal delivery rates were observed in women aged 26-30 years, with gradual decline with advanced age. The performance of ICSI resulted in higher delivery rates compared with conventional insemination. According to these data, the best live birth results following IVF treatment were achieved when the maternal age was 26-30 years, in couples with male factor infertility undergoing ICSI, and when two embryos were transferred.