APPLICATION OF MULTIVARIATE CLUSTER, DISCRIMINATE FUNCTION, AND STEPWISE REGRESSION-ANALYSES TO VARIABLE SELECTION AND PREDICTIVE MODELING OF SPERM CRYOSURVIVAL

Objective: To develop a mathematical model that predicts sperm cryodam age based on the kinematic characteristics of seminal sperm as detecte d by computer-aided sperm analysis (CASA). Design: Computer-aided sper m analysis was performed on donor semen before and after freezing. An iterative multivariate statistical analysis technique was developed to identify sperm subpopulations and to select the best variables for mo deling. Stepwise, multivariate regression was performed on the selecte d subpopulations to predict the post-thaw percentage of motile sperm f rom prefreeze kinematic values. Setting: Andrology laboratories, IVF l aboratories, and sperm cryobanks. Participants: Semen donors in an aca demic research environment. Main Outcome Measures: Identification of p redictive kinematic variables; number of sperm subpopulations per samp le; number of kinematic variables per subpopulation; prediction error for subpopulation membership; and an equation for prediction of post-t haw percentage of motile sperm from prefreeze CASA variables. Results: The number of subpopulations for each specimen was predicted by 3 to 5 kinematic variables. Straight-line velocity (VSL) and linearity were the most commonly predictive primary variables, whereas curvilinear v elocity and amplitude of lateral head displacement were the most commo nly predictive secondary variables. The best linear model predicted th e post-thaw percentage of motile sperm from the difference in VSL betw een the subpopulation with the highest value and the subpopulation wit h the lowest value in each prefreeze specimen. Conclusions: A small nu mber of consistent kinematic variables accurately described physiologi c subpopulations of sperm in prefreeze and post-thaw specimens from di fferent men. An equation based on the characteristics of these subpopu lations predicts the post-thaw percentage of motile sperm (i.e., sperm recovery) from simple prefreeze kinematic variables. This equation co uld improve specimen screening by eliminating the requirements for fre ezing and thawing in order to identify a specimen's vulnerability to c ryodamage.