Do morphokinetic data sets inform pregnancy potential?

PURPOSE: The aim of this study was to create a model to predict the implantation of transferred embryos based on information contained in the morphokinetic parameters of time-lapse monitoring. METHODS: An analysis of time-lapse recordings of 410 embryos transferred in 343 cycles of in vitro fertilization (IVF) treatment was performed. The study was conducted between June 2012 and November 2014. For each embryo, the following data were collected: the duration of time from the intracytoplasmic sperm injection (ICSI) procedure to further division for two, three, four, and five blastomeres, time intervals between successive divisions, and the level of fragmentation assessed in successive time-points. Principal component analysis (PCA) and logistic regression were used to create a predictive model. RESULTS: Based on the results of principal component analysis and logistic regression analysis, a predictive equation was constructed. Statistically significant differences (p < 0.001) in the size of the created parameter between the implanted group (the median value: Me = -5.18 and quartiles: Q 1= -5.61; Q 3 = -4.79) and the non-implanted group (Me = -5.69, Q 1 = -6.34; Q 3 = -5.16) were found. A receiver operating characteristic (ROC) curve constructed for the considered model showed the good quality of this predictive equation. The area under the ROC curve was AUC = 0.70 with a 95% confidence interval (0.64, 0.75). The presented model has been validated on an independent data set, illustrating that the model is reliable and repeatable. CONCLUSIONS: Morphokinetic parameters contain information useful in the process of creating pregnancy prediction models. However, embryo quality is not the only factor responsible for implantation, and, thus, the power of prediction of the considered model is not as high as in models for blastocyst formation. Nevertheless, as illustrated by the results of this study, the application of advanced data-mining methods in reproductive medicine allows one to create more accurate and useful models.

Morphokinetic parameters as a source of information concerning embryo developmental and implantation potential.

OBJECTIVES: The aim of the study was to present the results of time-lapse observation and to verify whether morphokinetic parameters are associated with embryo developmental and implantation potential. MATERIAL AND METHODS: The analysed data concern the development of 1,060 embryos, 898 of which (84.72%) achieved the blastocyst stage and 307 were transferred into the uterine cavity. As a result, 126 (41.04%) biochemical pregnancies and 109 (35.50%) clinical pregnancies were observed. Time from fertilisation to further divisions into 2-9 blastomeres, first to fourth round of cleavage, second to third synchronisation parameters and the duration of stages after the first, second and third division were analysed. RESULTS: Most of the parameters in the group of embryos developed to the blastocyst stage reached lower values than in the non-developed group. Moreover, parameters in the first group clearly had less dispersion. The differences between the groups with and without a biochemical pregnancy were smaller than the differences in the analysis of development to the blastocyst stage. However, in the case of clinical pregnancy analysis, there were again larger differences between both groups. A strong correlation was found between the majority of absolute morphokinetic parameters. A weaker, but still statistically significant correlation, was established between relative and other parameters. CONCLUSIONS: Morphokinetic parameters are associated with embryo developmental and implantation potential and can be considered as predictors of their quality. However, the development of efficient pregnancy prediction models needs further research utilising information from all available parameters and using advanced biostatistical methods.

A predictive model for blastocyst formation based on morphokinetic parameters in time-lapse monitoring of embryo development.

PURPOSE: The aim of the study was to create a predictive model of blastocyst development based on morphokinetic parameters of time-lapse embryoscope monitoring. METHODS: Time-lapse recordings of 432 embryos (obtained from 77 patients), monitored in Embryoscope, were involved in the study. Patients underwent in vitro fertilization according to standard procedure between June 2012 and April 2013. A retrospective analysis of morphokinetic features, focused on duration of time from the Intracytoplasmic Sperm Injection (ICSI) procedure to consecutive embryo division for 2, 3, 4 and 5 blastomeres, as well as time intervals between each division, was conducted. All embryos were observed for 5 days. RESULTS: Based on the distribution of analyzed morphokinetic parameters and number of embryos developed to blastocyst, a range denoting the possibility of an embryo reaching blastocyst stage was determined. According to the obtained results, univariate and multivariate logistic regression analyses were performed. Based on the times of division for two and five blastomeres and intervals between the second and third division, a multivariate predictive model was created. The predictive equation was constructed based on the parameters of logistic regression analysis (odds ratios). Statistically significant differences (p < 0.001) in the size of the prediction parameter between the group of embryos developed to blastocyst (the median value: Me = 9.95, and quartiles: Q1 = 7.59, Q3 = 12.30) and embryos that did not develop to the blastocyst stage (Me = 4.66, Q1 = 2.33, Q3 = 8.19) were found. A Receiver Operating Characteristic (ROC) curve was created for the constructed predictive model. The Area Under the Curve was AUC = 0.806 with a 95 % confidence interval (0.747, 0.864). The predictive model constructed in this study has been validated using an independent data set, which indicates that the model is reliable and repeatable. CONCLUSIONS: Time-lapse imaging presents a new diagnostic tool for parametric evaluation of embryo development, from the oocyte stage, through fertilization, up to the blastocyst stage. The assessment of morphokinetic parameters can help us to provide more accurate information about the reproductive potential of embryos. It allows for early selection of embryos with high reproductive potential and shortens embryo incubation.

How much information about embryo implantation potential is included in morphokinetic data? A prediction model based on artificial neural networks and principal component analysis.

PURPOSE: The aim of this study was to answer the question of how much information about embryo implantation potential can be obtained from morphokinetic parameters through the creation a predictive model based on morphokinetic information and using advanced data-mining and artificial intelligence methods. MATERIALS AND METHODS: Time-lapse recordings of 610 embryos were included in the analysis. For each embryo, absolute (t2, t3, t4, t5) and relative (cc2 and s2) morphokinetic parameters were collected. Additionally, fragmentation levels assessed at t2, t3, t4 and t5 time-points, presence of multinucleation, evenness of blastomeres after the first and second division and women's age were included in the analysis. RESULTS: The created predictive model of implantation was constructed on the basis of two advanced data-mining methods: principal component analysis (PCA) and artificial neural networks (ANN). The receiver operating characteristic (ROC) curve constructed for the created model demonstrated its good quality. The area under the ROC curve was AUC=0.75 with a 95% confidence interval (0.70, 0.80). Finally, the model was verified on a validation set and the results were also good, although slightly weaker: AUC=0.71, with a 95% confidence interval (0.59, 0.84). CONCLUSIONS: The combination of two data-mining algorithms: PCA and ANN may be considered a method which can extract virtually all the available information from data. This methodology is indeed efficient, but models presented in the literature are also effective and close to the limit of the maximal information which can be extracted from morphokinetic data. The limit can be determined at the level of AUC value marginally above 0.7.

Vitrification vs. slow cooling protocol using embryos cryopreserved in the 5th or 6th day after oocyte retrieval and IVF outcomes.

Modifying cryopreservation protocols may be seen as a way to simplify cryobanking procedure and increase satisfying outcomes. The aim of the study was to evaluate the influence of slow cooling protocol and vitrification on IVF outcomes using embryos preserved in the 5th or 6th day after oocyte retrieval. The study compared 2 groups of human embryos underwent slow cooling protocol (n=189) and vitrification (n=58). All embryos were cryopreserved in the 5th or 6th day after oocyte retrieval. Pre- and postfreezing embryo evaluation was performed in 2 or 3 steps scale, respectively. The study evaluates the effectiveness of two freezing methods and influence of the freezing day, pre- and postfreezing embryo grading on clinical pregnancy rate. Study showed higher pregnancy rate after vitrification (50.4%) than slow cooling protocol (25.9%). Significantly higher pregnancy rate was observed, when embryo preserved in the 5th day after oocyte retrieval (50.3%) than in the 6th day (22.7%). Postfreezing embryos evaluation showed that high quality blastocysts gave nearly four times better pregnancy outcomes than the ones evaluated as poor quality, and three times better than the ones evaluated as moderate. Prospective trials are needed to evaluate pregnancy and neonatal outcomes after vitrification. The number of controlled studies concerning vitrification has not been large enough, yet.