Evaluation of Prostate Cancer Risk Calculators for Shared Decision Making Across Diverse Urology Practices in Michigan.
Identifieur interne : 000A44 ( Main/Exploration ); précédent : 000A43; suivant : 000A45Evaluation of Prostate Cancer Risk Calculators for Shared Decision Making Across Diverse Urology Practices in Michigan.
Auteurs : Gregory B. Auffenberg [États-Unis] ; Selin Merdan [États-Unis] ; David C. Miller [États-Unis] ; Karandeep Singh [États-Unis] ; Benjamin R. Stockton [États-Unis] ; Khurshid R. Ghani [États-Unis] ; Brian T. Denton [États-Unis]Source :
- Urology [ 1527-9995 ] ; 2017.
Descripteurs français
- KwdFr :
- Adulte d'âge moyen (MeSH), Algorithmes (MeSH), Analyse de régression (MeSH), Antigène spécifique de la prostate (sang), Appréciation des risques (méthodes), Biopsie (méthodes), Dépistage précoce du cancer (méthodes), Enregistrements (MeSH), Grading des tumeurs (MeSH), Humains (MeSH), Michigan (MeSH), Mâle (MeSH), Prise de décision (MeSH), Prostate (anatomopathologie), Sujet âgé (MeSH), Toucher rectal (MeSH), Tumeurs de la prostate (diagnostic), Urologie (méthodes), Valeur prédictive des tests (MeSH), Études de cohortes (MeSH).
- MESH :
- anatomopathologie : Prostate.
- diagnostic : Tumeurs de la prostate.
- méthodes : Appréciation des risques, Biopsie, Dépistage précoce du cancer, Urologie.
- sang : Antigène spécifique de la prostate.
- Adulte d'âge moyen, Algorithmes, Analyse de régression, Enregistrements, Grading des tumeurs, Humains, Michigan, Mâle, Prise de décision, Sujet âgé, Toucher rectal, Valeur prédictive des tests, Études de cohortes.
English descriptors
- KwdEn :
- Aged (MeSH), Algorithms (MeSH), Biopsy (methods), Cohort Studies (MeSH), Decision Making (MeSH), Digital Rectal Examination (MeSH), Early Detection of Cancer (methods), Humans (MeSH), Male (MeSH), Michigan (MeSH), Middle Aged (MeSH), Neoplasm Grading (MeSH), Predictive Value of Tests (MeSH), Prostate (pathology), Prostate-Specific Antigen (blood), Prostatic Neoplasms (diagnosis), Registries (MeSH), Regression Analysis (MeSH), Risk Assessment (methods), Urology (methods).
- MESH :
- chemical , blood : Prostate-Specific Antigen.
- diagnosis : Prostatic Neoplasms.
- methods : Biopsy, Early Detection of Cancer, Risk Assessment, Urology.
- pathology : Prostate.
- Aged, Algorithms, Cohort Studies, Decision Making, Digital Rectal Examination, Humans, Male, Michigan, Middle Aged, Neoplasm Grading, Predictive Value of Tests, Registries, Regression Analysis.
Abstract
OBJECTIVE
To compare the predictive performance of a logistic regression model developed with contemporary data from a diverse group of urology practices to that of the Prostate Cancer Prevention Trial (PCPT) Risk Calculator version 2.0.
MATERIALS AND METHODS
With data from all first-time prostate biopsies performed between January 2012 and March 2015 across the Michigan Urological Surgery Improvement Collaborative (MUSIC), we developed a multinomial logistic regression model to predict the likelihood of finding high-grade cancer (Gleason score ≥7), low-grade cancer (Gleason score ≤6), or no cancer on prostate biopsy. The performance of the MUSIC model was evaluated in out-of-sample data using 10-fold cross-validation. Discrimination and calibration statistics were used to compare the performance of the MUSIC model to that of the PCPT risk calculator in the MUSIC cohort.
RESULTS
Of the 11,809 biopsies included, 4289 (36.3%) revealed high-grade cancer; 2027 (17.2%) revealed low-grade cancer; and the remaining 5493 (46.5%) were negative. In the MUSIC model, prostate-specific antigen level, rectal examination findings, age, race, and family history of prostate cancer were significant predictors of finding high-grade cancer on biopsy. The 2 models, based on similar predictors, had comparable discrimination (multiclass area under the curve = 0.63 for the MUSIC model and 0.62 for the PCPT calculator). Calibration analyses demonstrated that the MUSIC model more accurately predicted observed outcomes, whereas the PCPT risk calculator substantively overestimated the likelihood of finding no cancer while underestimating the risk of high-grade cancer in this population.
CONCLUSION
The PCPT risk calculator may not be a good predictor of individual biopsy outcomes for patients seen in contemporary urology practices.
DOI: 10.1016/j.urology.2017.01.039
PubMed: 28237530
Affiliations:
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Stockton</name><affiliation wicri:level="2"><nlm:affiliation>Lakeside Urology PC, St. Joseph, MI.</nlm:affiliation><country xml:lang="fr">États-Unis</country><placeName><region type="state">Michigan</region></placeName><wicri:cityArea>Lakeside Urology PC, St. Joseph</wicri:cityArea></affiliation></author><author><name sortKey="Ghani, Khurshid R" sort="Ghani, Khurshid R" uniqKey="Ghani K" first="Khurshid R" last="Ghani">Khurshid R. Ghani</name><affiliation wicri:level="2"><nlm:affiliation>Department of Urology, University of Michigan, Ann Arbor, MI.</nlm:affiliation><country xml:lang="fr">États-Unis</country><placeName><region type="state">Michigan</region></placeName><wicri:cityArea>Department of Urology, University of Michigan, Ann Arbor</wicri:cityArea></affiliation></author><author><name sortKey="Denton, Brian T" sort="Denton, Brian T" uniqKey="Denton B" first="Brian T" last="Denton">Brian T. Denton</name><affiliation wicri:level="2"><nlm:affiliation>Department of Urology, University of Michigan, Ann Arbor, MI; Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI.</nlm:affiliation><country xml:lang="fr">États-Unis</country><placeName><region type="state">Michigan</region></placeName><wicri:cityArea>Department of Urology, University of Michigan, Ann Arbor, MI; Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor</wicri:cityArea></affiliation></author></analytic><series><title level="j">Urology</title><idno type="eISSN">1527-9995</idno><imprint><date when="2017" type="published">2017</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Aged (MeSH)</term><term>Algorithms (MeSH)</term><term>Biopsy (methods)</term><term>Cohort Studies (MeSH)</term><term>Decision Making (MeSH)</term><term>Digital Rectal Examination (MeSH)</term><term>Early Detection of Cancer (methods)</term><term>Humans (MeSH)</term><term>Male (MeSH)</term><term>Michigan (MeSH)</term><term>Middle Aged (MeSH)</term><term>Neoplasm Grading (MeSH)</term><term>Predictive Value of Tests (MeSH)</term><term>Prostate (pathology)</term><term>Prostate-Specific Antigen (blood)</term><term>Prostatic Neoplasms (diagnosis)</term><term>Registries (MeSH)</term><term>Regression Analysis (MeSH)</term><term>Risk Assessment (methods)</term><term>Urology (methods)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Adulte d'âge moyen (MeSH)</term><term>Algorithmes (MeSH)</term><term>Analyse de régression (MeSH)</term><term>Antigène spécifique de la prostate (sang)</term><term>Appréciation des risques (méthodes)</term><term>Biopsie (méthodes)</term><term>Dépistage précoce du cancer (méthodes)</term><term>Enregistrements (MeSH)</term><term>Grading des tumeurs (MeSH)</term><term>Humains (MeSH)</term><term>Michigan (MeSH)</term><term>Mâle (MeSH)</term><term>Prise de décision (MeSH)</term><term>Prostate (anatomopathologie)</term><term>Sujet âgé (MeSH)</term><term>Toucher rectal (MeSH)</term><term>Tumeurs de la prostate (diagnostic)</term><term>Urologie (méthodes)</term><term>Valeur prédictive des tests (MeSH)</term><term>Études de cohortes (MeSH)</term></keywords><keywords scheme="MESH" type="chemical" qualifier="blood" xml:lang="en"><term>Prostate-Specific Antigen</term></keywords><keywords scheme="MESH" qualifier="anatomopathologie" xml:lang="fr"><term>Prostate</term></keywords><keywords scheme="MESH" qualifier="diagnosis" xml:lang="en"><term>Prostatic Neoplasms</term></keywords><keywords scheme="MESH" qualifier="diagnostic" xml:lang="fr"><term>Tumeurs de la prostate</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Biopsy</term><term>Early Detection of Cancer</term><term>Risk Assessment</term><term>Urology</term></keywords><keywords scheme="MESH" qualifier="méthodes" xml:lang="fr"><term>Appréciation des risques</term><term>Biopsie</term><term>Dépistage précoce du cancer</term><term>Urologie</term></keywords><keywords scheme="MESH" qualifier="pathology" xml:lang="en"><term>Prostate</term></keywords><keywords scheme="MESH" qualifier="sang" xml:lang="fr"><term>Antigène spécifique de la prostate</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Aged</term><term>Algorithms</term><term>Cohort Studies</term><term>Decision Making</term><term>Digital Rectal Examination</term><term>Humans</term><term>Male</term><term>Michigan</term><term>Middle Aged</term><term>Neoplasm Grading</term><term>Predictive Value of Tests</term><term>Registries</term><term>Regression Analysis</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Adulte d'âge moyen</term><term>Algorithmes</term><term>Analyse de régression</term><term>Enregistrements</term><term>Grading des tumeurs</term><term>Humains</term><term>Michigan</term><term>Mâle</term><term>Prise de décision</term><term>Sujet âgé</term><term>Toucher rectal</term><term>Valeur prédictive des tests</term><term>Études de cohortes</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p><b>OBJECTIVE</b></p><p>To compare the predictive performance of a logistic regression model developed with contemporary data from a diverse group of urology practices to that of the Prostate Cancer Prevention Trial (PCPT) Risk Calculator version 2.0.</p></div><div type="abstract" xml:lang="en"><p><b>MATERIALS AND METHODS</b></p><p>With data from all first-time prostate biopsies performed between January 2012 and March 2015 across the Michigan Urological Surgery Improvement Collaborative (MUSIC), we developed a multinomial logistic regression model to predict the likelihood of finding high-grade cancer (Gleason score ≥7), low-grade cancer (Gleason score ≤6), or no cancer on prostate biopsy. The performance of the MUSIC model was evaluated in out-of-sample data using 10-fold cross-validation. Discrimination and calibration statistics were used to compare the performance of the MUSIC model to that of the PCPT risk calculator in the MUSIC cohort.</p></div><div type="abstract" xml:lang="en"><p><b>RESULTS</b></p><p>Of the 11,809 biopsies included, 4289 (36.3%) revealed high-grade cancer; 2027 (17.2%) revealed low-grade cancer; and the remaining 5493 (46.5%) were negative. In the MUSIC model, prostate-specific antigen level, rectal examination findings, age, race, and family history of prostate cancer were significant predictors of finding high-grade cancer on biopsy. The 2 models, based on similar predictors, had comparable discrimination (multiclass area under the curve = 0.63 for the MUSIC model and 0.62 for the PCPT calculator). Calibration analyses demonstrated that the MUSIC model more accurately predicted observed outcomes, whereas the PCPT risk calculator substantively overestimated the likelihood of finding no cancer while underestimating the risk of high-grade cancer in this population.</p></div><div type="abstract" xml:lang="en"><p><b>CONCLUSION</b></p><p>The PCPT risk calculator may not be a good predictor of individual biopsy outcomes for patients seen in contemporary urology practices.</p></div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">28237530</PMID><DateCompleted><Year>2018</Year><Month>06</Month><Day>18</Day></DateCompleted><DateRevised><Year>2018</Year><Month>06</Month><Day>18</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1527-9995</ISSN><JournalIssue CitedMedium="Internet"><Volume>104</Volume><PubDate><Year>2017</Year><Month>06</Month></PubDate></JournalIssue><Title>Urology</Title><ISOAbbreviation>Urology</ISOAbbreviation></Journal><ArticleTitle>Evaluation of Prostate Cancer Risk Calculators for Shared Decision Making Across Diverse Urology Practices in Michigan.</ArticleTitle><Pagination><MedlinePgn>137-142</MedlinePgn></Pagination><ELocationID EIdType="pii" ValidYN="Y">S0090-4295(17)30165-6</ELocationID><ELocationID EIdType="doi" ValidYN="Y">10.1016/j.urology.2017.01.039</ELocationID><Abstract><AbstractText Label="OBJECTIVE">To compare the predictive performance of a logistic regression model developed with contemporary data from a diverse group of urology practices to that of the Prostate Cancer Prevention Trial (PCPT) Risk Calculator version 2.0.</AbstractText><AbstractText Label="MATERIALS AND METHODS">With data from all first-time prostate biopsies performed between January 2012 and March 2015 across the Michigan Urological Surgery Improvement Collaborative (MUSIC), we developed a multinomial logistic regression model to predict the likelihood of finding high-grade cancer (Gleason score ≥7), low-grade cancer (Gleason score ≤6), or no cancer on prostate biopsy. The performance of the MUSIC model was evaluated in out-of-sample data using 10-fold cross-validation. Discrimination and calibration statistics were used to compare the performance of the MUSIC model to that of the PCPT risk calculator in the MUSIC cohort.</AbstractText><AbstractText Label="RESULTS">Of the 11,809 biopsies included, 4289 (36.3%) revealed high-grade cancer; 2027 (17.2%) revealed low-grade cancer; and the remaining 5493 (46.5%) were negative. In the MUSIC model, prostate-specific antigen level, rectal examination findings, age, race, and family history of prostate cancer were significant predictors of finding high-grade cancer on biopsy. The 2 models, based on similar predictors, had comparable discrimination (multiclass area under the curve = 0.63 for the MUSIC model and 0.62 for the PCPT calculator). Calibration analyses demonstrated that the MUSIC model more accurately predicted observed outcomes, whereas the PCPT risk calculator substantively overestimated the likelihood of finding no cancer while underestimating the risk of high-grade cancer in this population.</AbstractText><AbstractText Label="CONCLUSION">The PCPT risk calculator may not be a good predictor of individual biopsy outcomes for patients seen in contemporary urology practices.</AbstractText><CopyrightInformation>Copyright © 2017 Elsevier Inc. All rights reserved.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Auffenberg</LastName><ForeName>Gregory B</ForeName><Initials>GB</Initials><AffiliationInfo><Affiliation>Department of Urology, University of Michigan, Ann Arbor, MI. Electronic address: gauffen@med.umich.edu.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Merdan</LastName><ForeName>Selin</ForeName><Initials>S</Initials><AffiliationInfo><Affiliation>Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Miller</LastName><ForeName>David C</ForeName><Initials>DC</Initials><AffiliationInfo><Affiliation>Department of Urology, University of Michigan, Ann Arbor, MI.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Singh</LastName><ForeName>Karandeep</ForeName><Initials>K</Initials><AffiliationInfo><Affiliation>Department of Learning Health Sciences, University of Michigan, Ann Arbor, MI; Division of Nephrology, Department of Medicine, University of Michigan, Ann Arbor, MI.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Stockton</LastName><ForeName>Benjamin R</ForeName><Initials>BR</Initials><AffiliationInfo><Affiliation>Lakeside Urology PC, St. Joseph, MI.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Ghani</LastName><ForeName>Khurshid R</ForeName><Initials>KR</Initials><AffiliationInfo><Affiliation>Department of Urology, University of Michigan, Ann Arbor, MI.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Denton</LastName><ForeName>Brian T</ForeName><Initials>BT</Initials><AffiliationInfo><Affiliation>Department of Urology, University of Michigan, Ann Arbor, MI; Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2017</Year><Month>02</Month><Day>22</Day></ArticleDate></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>Urology</MedlineTA><NlmUniqueID>0366151</NlmUniqueID><ISSNLinking>0090-4295</ISSNLinking></MedlineJournalInfo><ChemicalList><Chemical><RegistryNumber>EC 3.4.21.77</RegistryNumber><NameOfSubstance UI="D017430">Prostate-Specific Antigen</NameOfSubstance></Chemical></ChemicalList><CitationSubset>IM</CitationSubset><CommentsCorrectionsList><CommentsCorrections RefType="CommentIn"><RefSource>Urology. 2017 Jun;104:142</RefSource><PMID Version="1">28390734</PMID></CommentsCorrections></CommentsCorrectionsList><MeshHeadingList><MeshHeading><DescriptorName UI="D000368" MajorTopicYN="N">Aged</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000465" MajorTopicYN="N">Algorithms</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D001706" MajorTopicYN="N">Biopsy</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="N">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D015331" MajorTopicYN="N">Cohort Studies</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D003657" MajorTopicYN="Y">Decision Making</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D051517" MajorTopicYN="N">Digital Rectal Examination</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D055088" MajorTopicYN="N">Early Detection of Cancer</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008297" MajorTopicYN="N">Male</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008824" MajorTopicYN="N">Michigan</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008875" MajorTopicYN="N">Middle Aged</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D060787" MajorTopicYN="N">Neoplasm Grading</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D011237" MajorTopicYN="N">Predictive Value of Tests</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D011467" MajorTopicYN="N">Prostate</DescriptorName><QualifierName UI="Q000473" MajorTopicYN="N">pathology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D017430" MajorTopicYN="N">Prostate-Specific Antigen</DescriptorName><QualifierName UI="Q000097" MajorTopicYN="N">blood</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D011471" MajorTopicYN="N">Prostatic Neoplasms</DescriptorName><QualifierName UI="Q000175" MajorTopicYN="Y">diagnosis</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D012042" MajorTopicYN="N">Registries</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D012044" MajorTopicYN="N">Regression Analysis</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D018570" MajorTopicYN="N">Risk Assessment</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D014572" MajorTopicYN="N">Urology</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName></MeshHeading></MeshHeadingList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2016</Year><Month>11</Month><Day>29</Day></PubMedPubDate><PubMedPubDate PubStatus="revised"><Year>2016</Year><Month>12</Month><Day>30</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2017</Year><Month>01</Month><Day>18</Day></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2017</Year><Month>2</Month><Day>27</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2018</Year><Month>6</Month><Day>19</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2017</Year><Month>2</Month><Day>27</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">28237530</ArticleId><ArticleId IdType="pii">S0090-4295(17)30165-6</ArticleId><ArticleId IdType="doi">10.1016/j.urology.2017.01.039</ArticleId></ArticleIdList></PubmedData></pubmed><affiliations><list><country><li>États-Unis</li></country><region><li>Michigan</li></region></list><tree><country name="États-Unis"><region name="Michigan"><name sortKey="Auffenberg, Gregory B" sort="Auffenberg, Gregory B" uniqKey="Auffenberg G" first="Gregory B" last="Auffenberg">Gregory B. Auffenberg</name></region><name sortKey="Denton, Brian T" sort="Denton, Brian T" uniqKey="Denton B" first="Brian T" last="Denton">Brian T. Denton</name><name sortKey="Ghani, Khurshid R" sort="Ghani, Khurshid R" uniqKey="Ghani K" first="Khurshid R" last="Ghani">Khurshid R. Ghani</name><name sortKey="Merdan, Selin" sort="Merdan, Selin" uniqKey="Merdan S" first="Selin" last="Merdan">Selin Merdan</name><name sortKey="Miller, David C" sort="Miller, David C" uniqKey="Miller D" first="David C" last="Miller">David C. Miller</name><name sortKey="Singh, Karandeep" sort="Singh, Karandeep" uniqKey="Singh K" first="Karandeep" last="Singh">Karandeep Singh</name><name sortKey="Stockton, Benjamin R" sort="Stockton, Benjamin R" uniqKey="Stockton B" first="Benjamin R" last="Stockton">Benjamin R. Stockton</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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