Multivariate model for predicting semen cryopreservation outcomes in a human sperm bank

Semen cryopreservation is widely used in assisted reproductive technologies, but it reduces sperm quality dramatically. The aim of this study was to develop a model using basal semen quality to predict the outcome of postthaw semen parameters and improve the efficiency of cryopreservation in a human sperm bank. Basal semen parameters of 180 samples were evaluated in the first stage, and a multiple logistic regression analysis involving a backward elimination selection procedure was applied to select independent predictors. After a comprehensive analysis of all results, we developed a new model to assess the freezability of sperm. Progressive motility (PR), straight-line velocity (VSL) and average path velocity (VAP) were included in our model. A greater area under the receiver operating characteristic curve was obtained in our model when compared with other indicators. In the second stage of our study, samples that satisfied the new model were selected to undergo freeze-thawing. Compared with the first stage, the rate of good freezability was increased significantly (94% vs 67%, P = 0.003). By determining basal semen quality, we have developed a new model to improve the efficiency of cryopreservation in a human sperm bank.