Abstract: P-110

Clinical Value of the Newly Developed Nomogram Predicting the Fertilization Outcome in Assisted Reproductive Technology (ART).

Presenter: Rie Yokota


Abstract


OBJECTIVE: To develop the clinically useful nomogram which can predict the fertilization outcome of in-vitro fertilization (IVF) and intracytoplasmic sperm injection (ICSI) based on semen parameters and women factors.
DESIGN: Retrospective and prospective clinical study. view more

OBJECTIVE: To develop the clinically useful nomogram which can predict the fertilization outcome of in-vitro fertilization (IVF) and intracytoplasmic sperm injection (ICSI) based on semen parameters and women factors.
DESIGN: Retrospective and prospective clinical study.
MATERIALS AND METHODS: To identify the predictive factors of successful fertilization, IVF and ICSI cycles in our clinic between June and October 2016 were analyzed. All semen was analyzed by new computer aided sperm analysis system named sperm motility analysis system (SMAS) before and after sperm separation using Percoll solution. Women factors including age, serum Anti-Müllerian hormone (AMH) levels and the number of eggs retrieved were also included in the analysis. After identification of predictive factors by statistical analysis, nomogram predicting fertilization outcome was developed using multiple linear regression analysis. Another IVF, ICSI cycles between October and December 2016 were prospectively analyzed to verify the collectivity of fertilization nomogram.
RESULTS: A total of 898 IVF cycles were evaluated to identify predictive factors, some factors were converted in a logarithmic conversion to enhance accuracy of analysis. Logistic regression analysis revealed sperm motility value (SMV), curvilinear velocity (VCL), frequency of lateral head displacement (FLH) before sperm separation and the number of eggs retrieved as independent predictive factors for IVF fertilization outcome. Based on this result, multiple linear regression analysis was conducted and IVF fertilization nomogram was determined as 44.65 - 15.56*log(eggs) + logSMV + 4.34*VCL + 5.0*FLH. Then we divided the cases of prospective analysis of 332 IVF cycles into four groups based on the predicted value calculated by nomogram. Spearman’s rank correlation analysis revealed that IVF fertilization rate were significantly related to the predictive value (p<.05). Similar analysis was adapted to 533 ICSI outcome, however, there was no independent predictive factor for the fertility outcome of ICSI.
CONCLUSIONS: IVF fertilization nomogram determined by semen analysis parameters and number of eggs retrieved was able to predict the fertilization in IVF. Conversely, there was no independent predictive factor predicting fertilization outcome in ICSI, indicating couples with poor predictive value in IVF can benefit from ICSI.

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