There is an increasing interest in using statistical methods to predict ART outcome in clinical research.In the current study, we have developed an approach to rank embryos according to their implantation potential taking into account both embryological and clinical features.The proposed model is expected to assist embryologists to determine which embryo to transfer.The model established a moderate discriminative performance, both in the training set and in the separate validation set.Our data also suggested that prediction models based on computer assisted scoring system using morphometric data, rather than standard scoring system, had significantly more stability and reliability of predictive capability.The TCV from Day 1 to Day 3, number of blastomeres, COD and fragmentation on Day 2 and Day 3 are considered as important embryo features to evaluate the implantation potential.It was indicated that a CASS may be superior to a SSS in the prediction of implantation and live birth [16].To further refine and validate, using an external validation dataset, the model proposed by Paternot et al.

[35], we decided to use logistic regression and multivariate adaptive regression splines in both SSS data and CASS data, to develop prediction models.mobel hannover alter flughafenFor each of the four models retained, the AUC of training data and validation data were compared [31, 32] to evaluate the generalizability.billige mobel in karlsruheIn the two models derived from SSS data, we found a significant decline in prediction accuracy in the external validation data set, suggesting that both LR model and MARS model based on SSS had poor predictive capability and considerable variation.gastronomie mobel gebraucht verkaufenModels using CASS data showed a more stable discriminative capability.mobile crane course singapore

According to mutual information analysis (Fig.kostenlose möbel duisburg2) which reflect the correlation between variables, morphological characteristics on Day 3 are supposed to be the most powerful embryo features to determine treatment outcome.möbel paderborn dealConsequently, low correlation coefficients of evaluations on Day 3 [15] may result in the poor performance of the external validation data set.In the CASS dataset, the MARS model presented a better predictive power compared with LR model.Results of univariate analysis have showed that not all of the characteristics were linearly correlated to clinical pregnancy, such as number of blastomeres and COD on Day 3.In such cases, MARS model, regardless of the certain assumption about the underlying functional relationship between the dependent and independent variables, constructed the relation from a set of coefficients and basic functions that are entirely “driven” from the data itself and consequently resulted in better AUC [36].

Mutual information analysis of correlation between characteristics and outcome.Each bar represents the correlation power of corresponding characteristic.1: number of blastomeres on Day 2; 2: fragmentation on Day 2; 3: size difference between blastomeres on Day 2; 4: even or uneven blastomere number on Day 2; 5 number of blastomeres on Day 3; 6: fragmentation on Day 3; 7: size difference between blastomeres on Day 3.Embryo characteristics on Day 3 were more important to predict the implantation outcome compared with characteristics on Day 2 Number of blastomeres on Day 2 and Day 3 were found to be a powerful predictor clinical pregnancy.On Day 2, the presence of 4 blastomeres was considered the ideal cleavage rate [10, 37].The study by Van Loendersloot et al.[24] showed that faster cleaving embryos had a lower chance of implantation.However, in our dataset, the presence of 6 blastomeres on Day 2 resulted in a higher implantation rate (42.1 %) than 4 blastomeres (39.7 %) but these results have to be further confirmed in future studies because only 19 embryos at the 6-cell stage on Day 2 were transferred, with 8 embryos implanted.

The ideal number of blastomeres on Day 3 was 8.A marked reduction in implantation rate was found to be associated with slow cleavage rate and a slight reduction was correlated with rapid cleavage rate.This finding corroborates other studies where numerous authors have reported that too slow or too fast embryo cleavage has a negative impact on implantation rate [8, 13, 38].This non-linear relationship between number of blastomeres and IVF/ICSI outcome makes the transformation (abs(number-8.4)) essential model fitting.The study showed that the difference in blastomere size had strong predictive power for implantation.This is in line with other predictive models [39].We compared coefficient of diversity (COD) of synchronously divided embryos (2-, 4- or 8-cell stage embryos) to embryos in intermediate steps (3-, 5-, 6-, 7-, 9- or 10-cell embryos).Significant lower COD was observed to be associated with synchrony.This finding confirmed a previous study [35], where the lowest coefficient of diversity was found for 8-cell stage embryos on Day 3.

The variation in blastomere size has been reported to be negatively correlated with implantation [38].In our study, implanted embryos tended to have more uniform blastomere size than non-implanted embryos except for 6-cell stage embryos, although significant difference was only found in 8-cell embryos on Day 3.It also confirmed the results described by [40] and the division model published by Roux [41] that theoretically, 6-cell stage embryos should have unequally sized blastomeres and higher COD.Fragmentation on Day 2 and Day 3 were indicated as independent significant predictors of implantation in the multivariate models by Van Loendersloot [24] and Holte [37], respectively, and this was confirmed in both the LR and MARS model.Total cytoplasmic volume on Day1, Day2 and Day 3 were all included in the final MARS model while only TCV on Day 2 was included in the LR model.An earlier study [42] identified a significant association between TCV and implantation potential on both Day 2 and Day 3.

This could be explained by the fact that volume regulation is an essential process in the embryo development, as a failure in volume regulation can result in blocked embryos [43].Hnida [44, 45] reported a significant decrease in the mean blastomere volume with an increasing degree of fragmentation for all analyzed embryo stages.Our current data analysis shows a significant (p < 0.01) negative correlation between the TCV and the degree of fragmentation (Fig.3), confirming the conclusion published by Hnida [44, 45].In addition, in the VIF analysis, a retained valued of 92 indicated a moderate to strong (30 ~ 100) collinearity between TCV and fragmentation on Day 3.The collinearity did not affect the final LR model since both predictors were excluded by stepwise logit regression.Nevertheless, the final MARS model, including both TCV and fragmentation fitted the data significantly better than the model including only one of the two parameters.Several other embryo evaluation models have been published in the past several years.

However, some studies suffered from inadequate data sets and concluded their input information was not sufficient to classify the outcome [8, 10, 46].Some other studies managed to achieve a model with high accuracy, without validation in an external data set, which is an essential process for a prediction model [10, 47].The model by Van Loendersloot et al.[24] was based on the investigation of a large number of patients and cycles, and acquired confirmation on external validation data.However, when integrating number of blastomeres, degree of fragmentation, and size difference between blastomeres, the scoring method introduced in this study has limited prediction value for embryos with the same score but different fragmentation and COD status (e.g., an embryo with no fragmentation but unevenly sized blastomeres and an embryo with fragmentation but evenly sized blastomeres).The correlation of fragmentation and total cytoplasmic volume.Total embryo volume was significantly negatively correlated to the degree of fragmentation Several other morphometric characteristics have been considered as predictor for developmental competence or implantation, such as zygote size, nuclear size, embryo area and perimeter, equivalent circle radius of the embryo, embryonic roundness, and zona pellucida thickness [48–50].

Morphometric measurements may minimize the variability among different embryologists and clinics.Although very little relevant literature published on the predictive ability of morphometric parameters, further studies are worth to go.It may improve the understanding of basic biology controlling early embryonic development and how this is affected by clinical parameters.According to our data, the type of female pathology and male pathology have limited influence on the success of IFV/ICSI treatment within the first cycle in univariate analysis.Notably, regardless of no significant difference on clinical pregnancy rate with the presence of endometriosis success rate in patient with severe endometriosis decreased remarkably in our research cohort.However, with the small population (49 in 871 cases), this factor did not contribute to the final model.Ages of male and female were interesting prognostic factors for implantation according to our models.Increasing age of both women and men has been reported to be associated with declining possibility of successful pregnancy chance [51–54].

As one of the most important factors for success with IVF, age of female is commonly included in previous prediction models.Male age, for the first time, is recruited in the prediction model in our study.Univariate logistic regression analysis revealed no significant influence of type of infertility and duration of infertility on clinical pregnancy, which is in line with Bancsi’ s [55] study.The best predictive capacity of our cohort was 0.71 on training set and 0.69 on validation set.Compared with the previous prediction models, most of which could hardly return an accuracy rate of 0.67, this model presented a more confident prediction.Data trained for the model was collected during a period of 6 years, while the validation was performed on a separate and more recent data set.Regardless of different period, this model showed a similar discrimination efficiency on validation set.It should be noteworthy that all the data used in this study was collected from a single source, it is important to evaluate the generalizability of the model to other clinics.

The method needs to be confirmed by replicating experiments on different IVF data sets.It also should be noted that only single embryo transfers were taken into account when we developing the model.This approach has the advantage that the embryo data can be directly linked to the outcome.The major weakness of the study is the fact that the transferred embryos were selected using the SSS and the CASS analysis was performed retrospectively.In clinical single embryo transfer practice, embryologist are often confronted with situations that more than one embryo are available.In these cases, the CASS based scoring method may provide better understanding of all the characteristics of the embryo and assist the embryologist to select the embryo with highest implantation potential.The comparison research carried out on the 104 patients illustrated that in the majority of cases, this model assisted scoring method retained to a different decision, indicating that an evaluation of the this prediction model on future clinical randomized trial would be necessary.