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 cryodamage based on the kinematic characteristics of seminal sperm as detected by computer-aided sperm analysis (CASA).


METHODS
Computer-aided sperm 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 modeling. Stepwise, multivariate regression was performed on the selected subpopulations to predict the post-thaw percentage of motile sperm from prefreeze kinematic values.


METHODS
Andrology laboratories, IVF laboratories, and sperm cryobanks.


METHODS
Semen donors in an academic research environment.


METHODS
Identification of predictive kinematic variables; number of sperm subpopulations per sample; number of kinematic variables per subpopulation; prediction error for subpopulation membership; and an equation for prediction of post-thaw 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 velocity and amplitude of lateral head displacement were the most commonly predictive secondary variables. The best linear model predicted the post-thaw percentage of motile sperm from the difference in VSL between the subpopulation with the highest value and the subpopulation with the lowest value in each prefreeze specimen.


CONCLUSIONS
A small number of consistent kinematic variables accurately described physiologic subpopulations of sperm in prefreeze and post-thaw specimens from different men. An equation based on the characteristics of these subpopulations predicts the post-thaw percentage of motile sperm (i.e., sperm recovery) from simple prefreeze kinematic variables. This equation could improve specimen screening by eliminating the requirements for freezing and thawing in order to identify a specimen's vulnerability to cryodamage.
Davis
R O
RO
School of Medicine, Department of Obstetrics and Gynecology, University of California, Davis 95616, USA.
Drobnis
E Z
EZ
Overstreet
J W
JW
D002470:Cell Survival; D015925:Cryopreservation; D016002:Discriminant Analysis; D006801:Humans; D008297:Male; D008433:Mathematics; D008954:Models, Biological; D015999:Multivariate Analysis; D012044:Regression Analysis; D013081:Sperm Motility; D013094:Spermatozoa / Q000502:physiology*