Are sperm parameters able to predict the success of assisted reproductive technology? A retrospective analysis of over 22,000 assisted reproductive technology cycles

精子参数能否预测辅助生殖技术的成功率?一项对超过22000个辅助生殖技术周期的回顾性分析

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Abstract

BACKGROUND: An explosive increase in couples attending assisted reproductive technology has been recently observed, despite an overall success rate of about 20%-30%. Considering the assisted reproductive technology-related economic and psycho-social costs, the improvement of these percentages is extremely relevant. However, in the identification of predictive markers of assisted reproductive technology success, male parameters are largely underestimated so far. STUDY DESIGN: Retrospective, observational study. OBJECTIVES: To evaluate whether conventional semen parameters could predict assisted reproductive technology success. MATERIALS AND METHODS: All couples attending a single third-level fertility center from 1992 to 2020 were retrospectively enrolled, collecting all semen and assisted reproductive technology parameters of fresh cycles. Fertilization rate was the primary end-point, representing a parameter immediately dependent on male contribution. Pregnancy and live birth rates were considered in relation to semen variables. Statistical analyses were performed using the parameters obtained according to the World Health Organization manual editions used for semen analysis. RESULTS: Note that, 22,013 in vitro fertilization and intracytoplasmic sperm injection cycles were considered. Overall, fertilization rate was significantly lower in patients with abnormal semen parameters compared to normozoospermic men, irrespective of the World Health Organization manual edition. In the in vitro fertilization setting, both progressive motility (p = 0.012) and motility after capacitation (p = 0.002) significantly predicted the fertilization rate (statistical accuracy = 71.1%). Sperm motilities also predicted pregnancy (p < 0.001) and live birth (p = 0.001) rates. In intracytoplasmic sperm injection cycles, sperm morphology predicted fertilization rate (p = 0.001, statistical accuracy = 90.3%). Sperm morphology significantly predicted both pregnancy (p < 0.001) and live birth (p < 0.001) rates and a cut-off of 5.5% was identified as a threshold to predict clinical pregnancy (area under the curve = 0.811, p < 0.001). DISCUSSION: Interestingly, sperm motility plays a role in predicting in vitro fertilization success, while sperm morphology is the relevant parameter in intracytoplasmic sperm injection cycles. These parameters may be considered reliable tools to measure the male role on ART outcomes, potentially impacting the clinical management of infertile couples.

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