Application of multivariate cluster, discriminate function, and stepwise regression analyses to variable selection and predictive modeling of sperm cryosurvival

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). Computer-aided sperm analysis was performed on donor semen before and after freezing. An iterative multivariate statistical anal...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Fertility and sterility 1995-05, Vol.63 (5), p.1051-1057
Hauptverfasser: Davis, Russell O., Drobnis, Erma Z., Overstreet, James W.
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 1057
container_issue 5
container_start_page 1051
container_title Fertility and sterility
container_volume 63
creator Davis, Russell O.
Drobnis, Erma Z.
Overstreet, James W.
description 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). 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. Andrology laboratories, IVF laboratories, and sperm cryobanks. Semen donors in an academic research environment. 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. 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. 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.
doi_str_mv 10.1016/S0015-0282(16)57547-5
format Article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_77225042</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0015028216575475</els_id><sourcerecordid>77225042</sourcerecordid><originalsourceid>FETCH-LOGICAL-c389t-3b94e0a533fad5e010e8833162db8d90c5acef8eec09d588e94fc84a4247bf533</originalsourceid><addsrcrecordid>eNqFkc9u1DAQxi0EKtvCI1TyASEqNWAnceKcqqrin1SJA3C2HHtcGTlO8CSL9m14VJzsaq-cLHt-38z4-wi55uw9Z7z58J0xLgpWyvIdb25EK-q2EM_IjgvRFKIR1XOyOyMvySXiL8ZYw9vygly0bck63u7I3_tpCt7o2Y-Rjo4OS5j9XievZ6AmLDhDuqXWo0l-8HF9dUs0K35LdbQ0A9Mfj0ATPCVAXPvoqMMBAek80q1XH4AiBNh0m2xKYH2-7oEOo4Xg49M6HidIAzXpMOKS9nmR8Iq8cDogvD6dV-Tnp48_Hr4Uj98-f324fyxMJbu5qPquBqZFVTltBTDOQMqq4k1pe2k7ZoQ24CSAYZ0VUkJXOyNrXZd127ssuyJvj32nNP5eAGc15E9DCDrCuKDKjpWC1WUGxRE0aURM4NSUrdHpoDhTazJqS0attqt825JRIuuuTwOWfgB7Vp2iyPU3p7pGo4NLOhqPZ6wSvGxlnbG7IwbZjL2HpNB4iCbbmbK_yo7-P4v8A1Q4r68</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>77225042</pqid></control><display><type>article</type><title>Application of multivariate cluster, discriminate function, and stepwise regression analyses to variable selection and predictive modeling of sperm cryosurvival</title><source>MEDLINE</source><source>Elsevier ScienceDirect Journals</source><source>EZB-FREE-00999 freely available EZB journals</source><source>Alma/SFX Local Collection</source><creator>Davis, Russell O. ; Drobnis, Erma Z. ; Overstreet, James W.</creator><creatorcontrib>Davis, Russell O. ; Drobnis, Erma Z. ; Overstreet, James W.</creatorcontrib><description>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). 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. Andrology laboratories, IVF laboratories, and sperm cryobanks. Semen donors in an academic research environment. 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. 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. 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.</description><identifier>ISSN: 0015-0282</identifier><identifier>EISSN: 1556-5653</identifier><identifier>DOI: 10.1016/S0015-0282(16)57547-5</identifier><identifier>PMID: 7720917</identifier><identifier>CODEN: FESTAS</identifier><language>eng</language><publisher>New York, NY: Elsevier Inc</publisher><subject>Biological and medical sciences ; Birth control ; CASA ; Cell Survival ; cluster analysis ; cryodamage ; Cryopreservation ; Discriminant Analysis ; Gynecology. Andrology. Obstetrics ; Humans ; Male ; mathematical modeling ; Mathematics ; Medical sciences ; Models, Biological ; Multivariate Analysis ; Regression Analysis ; Sperm ; Sperm Motility ; Spermatozoa - physiology ; Sterility. Assisted procreation</subject><ispartof>Fertility and sterility, 1995-05, Vol.63 (5), p.1051-1057</ispartof><rights>1995 American Society for Reproductive Medicine</rights><rights>1995 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c389t-3b94e0a533fad5e010e8833162db8d90c5acef8eec09d588e94fc84a4247bf533</citedby><cites>FETCH-LOGICAL-c389t-3b94e0a533fad5e010e8833162db8d90c5acef8eec09d588e94fc84a4247bf533</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0015028216575475$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3536,27903,27904,65309</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&amp;idt=3512784$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/7720917$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Davis, Russell O.</creatorcontrib><creatorcontrib>Drobnis, Erma Z.</creatorcontrib><creatorcontrib>Overstreet, James W.</creatorcontrib><title>Application of multivariate cluster, discriminate function, and stepwise regression analyses to variable selection and predictive modeling of sperm cryosurvival</title><title>Fertility and sterility</title><addtitle>Fertil Steril</addtitle><description>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). 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. Andrology laboratories, IVF laboratories, and sperm cryobanks. Semen donors in an academic research environment. 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. 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. 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.</description><subject>Biological and medical sciences</subject><subject>Birth control</subject><subject>CASA</subject><subject>Cell Survival</subject><subject>cluster analysis</subject><subject>cryodamage</subject><subject>Cryopreservation</subject><subject>Discriminant Analysis</subject><subject>Gynecology. Andrology. Obstetrics</subject><subject>Humans</subject><subject>Male</subject><subject>mathematical modeling</subject><subject>Mathematics</subject><subject>Medical sciences</subject><subject>Models, Biological</subject><subject>Multivariate Analysis</subject><subject>Regression Analysis</subject><subject>Sperm</subject><subject>Sperm Motility</subject><subject>Spermatozoa - physiology</subject><subject>Sterility. Assisted procreation</subject><issn>0015-0282</issn><issn>1556-5653</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1995</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqFkc9u1DAQxi0EKtvCI1TyASEqNWAnceKcqqrin1SJA3C2HHtcGTlO8CSL9m14VJzsaq-cLHt-38z4-wi55uw9Z7z58J0xLgpWyvIdb25EK-q2EM_IjgvRFKIR1XOyOyMvySXiL8ZYw9vygly0bck63u7I3_tpCt7o2Y-Rjo4OS5j9XievZ6AmLDhDuqXWo0l-8HF9dUs0K35LdbQ0A9Mfj0ATPCVAXPvoqMMBAek80q1XH4AiBNh0m2xKYH2-7oEOo4Xg49M6HidIAzXpMOKS9nmR8Iq8cDogvD6dV-Tnp48_Hr4Uj98-f324fyxMJbu5qPquBqZFVTltBTDOQMqq4k1pe2k7ZoQ24CSAYZ0VUkJXOyNrXZd127ssuyJvj32nNP5eAGc15E9DCDrCuKDKjpWC1WUGxRE0aURM4NSUrdHpoDhTazJqS0attqt825JRIuuuTwOWfgB7Vp2iyPU3p7pGo4NLOhqPZ6wSvGxlnbG7IwbZjL2HpNB4iCbbmbK_yo7-P4v8A1Q4r68</recordid><startdate>19950501</startdate><enddate>19950501</enddate><creator>Davis, Russell O.</creator><creator>Drobnis, Erma Z.</creator><creator>Overstreet, James W.</creator><general>Elsevier Inc</general><general>Elsevier Science</general><scope>IQODW</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope></search><sort><creationdate>19950501</creationdate><title>Application of multivariate cluster, discriminate function, and stepwise regression analyses to variable selection and predictive modeling of sperm cryosurvival</title><author>Davis, Russell O. ; Drobnis, Erma Z. ; Overstreet, James W.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c389t-3b94e0a533fad5e010e8833162db8d90c5acef8eec09d588e94fc84a4247bf533</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1995</creationdate><topic>Biological and medical sciences</topic><topic>Birth control</topic><topic>CASA</topic><topic>Cell Survival</topic><topic>cluster analysis</topic><topic>cryodamage</topic><topic>Cryopreservation</topic><topic>Discriminant Analysis</topic><topic>Gynecology. Andrology. Obstetrics</topic><topic>Humans</topic><topic>Male</topic><topic>mathematical modeling</topic><topic>Mathematics</topic><topic>Medical sciences</topic><topic>Models, Biological</topic><topic>Multivariate Analysis</topic><topic>Regression Analysis</topic><topic>Sperm</topic><topic>Sperm Motility</topic><topic>Spermatozoa - physiology</topic><topic>Sterility. Assisted procreation</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Davis, Russell O.</creatorcontrib><creatorcontrib>Drobnis, Erma Z.</creatorcontrib><creatorcontrib>Overstreet, James W.</creatorcontrib><collection>Pascal-Francis</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Fertility and sterility</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Davis, Russell O.</au><au>Drobnis, Erma Z.</au><au>Overstreet, James W.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Application of multivariate cluster, discriminate function, and stepwise regression analyses to variable selection and predictive modeling of sperm cryosurvival</atitle><jtitle>Fertility and sterility</jtitle><addtitle>Fertil Steril</addtitle><date>1995-05-01</date><risdate>1995</risdate><volume>63</volume><issue>5</issue><spage>1051</spage><epage>1057</epage><pages>1051-1057</pages><issn>0015-0282</issn><eissn>1556-5653</eissn><coden>FESTAS</coden><abstract>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). 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. Andrology laboratories, IVF laboratories, and sperm cryobanks. Semen donors in an academic research environment. 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. 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. 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.</abstract><cop>New York, NY</cop><pub>Elsevier Inc</pub><pmid>7720917</pmid><doi>10.1016/S0015-0282(16)57547-5</doi><tpages>7</tpages></addata></record>
fulltext fulltext
identifier ISSN: 0015-0282
ispartof Fertility and sterility, 1995-05, Vol.63 (5), p.1051-1057
issn 0015-0282
1556-5653
language eng
recordid cdi_proquest_miscellaneous_77225042
source MEDLINE; Elsevier ScienceDirect Journals; EZB-FREE-00999 freely available EZB journals; Alma/SFX Local Collection
subjects Biological and medical sciences
Birth control
CASA
Cell Survival
cluster analysis
cryodamage
Cryopreservation
Discriminant Analysis
Gynecology. Andrology. Obstetrics
Humans
Male
mathematical modeling
Mathematics
Medical sciences
Models, Biological
Multivariate Analysis
Regression Analysis
Sperm
Sperm Motility
Spermatozoa - physiology
Sterility. Assisted procreation
title Application of multivariate cluster, discriminate function, and stepwise regression analyses to variable selection and predictive modeling of sperm cryosurvival
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-25T00%3A46%3A14IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Application%20of%20multivariate%20cluster,%20discriminate%20function,%20and%20stepwise%20regression%20analyses%20to%20variable%20selection%20and%20predictive%20modeling%20of%20sperm%20cryosurvival&rft.jtitle=Fertility%20and%20sterility&rft.au=Davis,%20Russell%20O.&rft.date=1995-05-01&rft.volume=63&rft.issue=5&rft.spage=1051&rft.epage=1057&rft.pages=1051-1057&rft.issn=0015-0282&rft.eissn=1556-5653&rft.coden=FESTAS&rft_id=info:doi/10.1016/S0015-0282(16)57547-5&rft_dat=%3Cproquest_cross%3E77225042%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=77225042&rft_id=info:pmid/7720917&rft_els_id=S0015028216575475&rfr_iscdi=true