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Variable performance of models for predicting methicillin-resistant Staphylococcus aureus carriage in European surgical wards

Identifieur interne : 000C48 ( Pmc/Checkpoint ); précédent : 000C47; suivant : 000C49

Variable performance of models for predicting methicillin-resistant Staphylococcus aureus carriage in European surgical wards

Auteurs : Andie S. Lee [Suisse, Australie] ; Angelo Pan [Italie] ; Stephan Harbarth [Suisse] ; Andrea Patroni [Italie] ; Annie Chalfine [France] ; George L. Daikos [Grèce] ; Silvia Garilli [Italie] ; José Antonio Martínez [Espagne] ; Ben S. Cooper [Thaïlande, Royaume-Uni]

Source :

RBID : PMC:4347652

Abstract

Background

Predictive models to identify unknown methicillin-resistant Staphylococcus aureus (MRSA) carriage on admission may optimise targeted MRSA screening and efficient use of resources. However, common approaches to model selection can result in overconfident estimates and poor predictive performance. We aimed to compare the performance of various models to predict previously unknown MRSA carriage on admission to surgical wards.

Methods

The study analysed data collected during a prospective cohort study which enrolled consecutive adult patients admitted to 13 surgical wards in 4 European hospitals. The participating hospitals were located in Athens (Greece), Barcelona (Spain), Cremona (Italy) and Paris (France). Universal admission MRSA screening was performed in the surgical wards. Data regarding demographic characteristics and potential risk factors for MRSA carriage were prospectively collected during the study period. Four logistic regression models were used to predict probabilities of unknown MRSA carriage using risk factor data: “Stepwise” (variables selected by backward elimination); “Best BMA” (model with highest posterior probability using Bayesian model averaging which accounts for uncertainty in model choice); “BMA” (average of all models selected with BMA); and “Simple” (model including variables selected >50% of the time by both Stepwise and BMA approaches applied to repeated random sub-samples of 50% of the data). To assess model performance, cross-validation against data not used for model fitting was conducted and net reclassification improvement (NRI) was calculated.

Results

Of 2,901 patients enrolled, 111 (3.8%) were newly identified MRSA carriers. Recent hospitalisation and presence of a wound/ulcer were significantly associated with MRSA carriage in all models. While all models demonstrated limited predictive ability (mean c-statistics <0.7) the Simple model consistently detected more MRSA-positive individuals despite screening fewer patients than the Stepwise model. Moreover, the Simple model improved reclassification of patients into appropriate risk strata compared with the Stepwise model (NRI 6.6%, P = .07).

Conclusions

Though commonly used, models developed using stepwise variable selection can have relatively poor predictive value. When developing MRSA risk indices, simpler models, which account for uncertainty in model selection, may better stratify patients’ risk of unknown MRSA carriage.


Url:
DOI: 10.1186/s12879-015-0834-y
PubMed: 25880328
PubMed Central: 4347652


Affiliations:


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PMC:4347652

Le document en format XML

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<wicri:regionArea>Centre for Clinical Vaccinology and Tropical Medicine, Nuffield Department of Clinical Medicine, University of Oxford, Oxford</wicri:regionArea>
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<sec>
<title>Background</title>
<p>Predictive models to identify unknown methicillin-resistant
<italic>Staphylococcus aureus</italic>
(MRSA) carriage on admission may optimise targeted MRSA screening and efficient use of resources. However, common approaches to model selection can result in overconfident estimates and poor predictive performance. We aimed to compare the performance of various models to predict previously unknown MRSA carriage on admission to surgical wards.</p>
</sec>
<sec>
<title>Methods</title>
<p>The study analysed data collected during a prospective cohort study which enrolled consecutive adult patients admitted to 13 surgical wards in 4 European hospitals. The participating hospitals were located in Athens (Greece), Barcelona (Spain), Cremona (Italy) and Paris (France). Universal admission MRSA screening was performed in the surgical wards. Data regarding demographic characteristics and potential risk factors for MRSA carriage were prospectively collected during the study period. Four logistic regression models were used to predict probabilities of unknown MRSA carriage using risk factor data: “Stepwise” (variables selected by backward elimination); “Best BMA” (model with highest posterior probability using Bayesian model averaging which accounts for uncertainty in model choice); “BMA” (average of all models selected with BMA); and “Simple” (model including variables selected >50% of the time by both Stepwise and BMA approaches applied to repeated random sub-samples of 50% of the data). To assess model performance, cross-validation against data not used for model fitting was conducted and net reclassification improvement (NRI) was calculated.</p>
</sec>
<sec>
<title>Results</title>
<p>Of 2,901 patients enrolled, 111 (3.8%) were newly identified MRSA carriers. Recent hospitalisation and presence of a wound/ulcer were significantly associated with MRSA carriage in all models. While all models demonstrated limited predictive ability (mean
<italic>c</italic>
-statistics <0.7) the Simple model consistently detected more MRSA-positive individuals despite screening fewer patients than the Stepwise model. Moreover, the Simple model improved reclassification of patients into appropriate risk strata compared with the Stepwise model (NRI 6.6%,
<italic>P</italic>
 = .07).</p>
</sec>
<sec>
<title>Conclusions</title>
<p>Though commonly used, models developed using stepwise variable selection can have relatively poor predictive value. When developing MRSA risk indices, simpler models, which account for uncertainty in model selection, may better stratify patients’ risk of unknown MRSA carriage.</p>
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<pmc article-type="research-article">
<pmc-dir>properties open_access</pmc-dir>
<front>
<journal-meta>
<journal-id journal-id-type="nlm-ta">BMC Infect Dis</journal-id>
<journal-id journal-id-type="iso-abbrev">BMC Infect. Dis</journal-id>
<journal-title-group>
<journal-title>BMC Infectious Diseases</journal-title>
</journal-title-group>
<issn pub-type="epub">1471-2334</issn>
<publisher>
<publisher-name>BioMed Central</publisher-name>
<publisher-loc>London</publisher-loc>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="pmid">25880328</article-id>
<article-id pub-id-type="pmc">4347652</article-id>
<article-id pub-id-type="publisher-id">834</article-id>
<article-id pub-id-type="doi">10.1186/s12879-015-0834-y</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Research Article</subject>
</subj-group>
</article-categories>
<title-group>
<article-title>Variable performance of models for predicting methicillin-resistant
<italic>Staphylococcus aureus</italic>
carriage in European surgical wards</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>Lee</surname>
<given-names>Andie S</given-names>
</name>
<address>
<email>andie.lee@live.com.au</email>
</address>
<xref ref-type="aff" rid="Aff1"></xref>
<xref ref-type="aff" rid="Aff2"></xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Pan</surname>
<given-names>Angelo</given-names>
</name>
<address>
<email>angelo.pan1@tin.it</email>
</address>
<xref ref-type="aff" rid="Aff3"></xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Harbarth</surname>
<given-names>Stephan</given-names>
</name>
<address>
<email>stephan.harbarth@hcuge.ch</email>
</address>
<xref ref-type="aff" rid="Aff1"></xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Patroni</surname>
<given-names>Andrea</given-names>
</name>
<address>
<email>andreapatroni@hotmail.com</email>
</address>
<xref ref-type="aff" rid="Aff4"></xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Chalfine</surname>
<given-names>Annie</given-names>
</name>
<address>
<email>achalfine@hpsj.fr</email>
</address>
<xref ref-type="aff" rid="Aff5"></xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Daikos</surname>
<given-names>George L</given-names>
</name>
<address>
<email>gdaikos@med.uoa.gr</email>
</address>
<xref ref-type="aff" rid="Aff6"></xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Garilli</surname>
<given-names>Silvia</given-names>
</name>
<address>
<email>garillisilvia@libero.it</email>
</address>
<xref ref-type="aff" rid="Aff3"></xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Martínez</surname>
<given-names>José Antonio</given-names>
</name>
<address>
<email>jamarti@clinic.ub.es</email>
</address>
<xref ref-type="aff" rid="Aff7"></xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Cooper</surname>
<given-names>Ben S</given-names>
</name>
<address>
<email>ben.s.cooper@gmail.com</email>
</address>
<xref ref-type="aff" rid="Aff8"></xref>
<xref ref-type="aff" rid="Aff9"></xref>
</contrib>
<contrib contrib-type="author">
<collab>in collaboration with the MOSAR-04 Study Team</collab>
</contrib>
<aff id="Aff1">
<label></label>
Infection Control Program, University of Geneva Hospitals and Faculty of Medicine, Geneva, Switzerland</aff>
<aff id="Aff2">
<label></label>
Departments of Infectious Diseases and Microbiology, Royal Prince Alfred Hospital, Sydney, Australia</aff>
<aff id="Aff3">
<label></label>
Infectious and Tropical Diseases Unit, Istituti Ospitalieri di Cremona, Cremona, Italy</aff>
<aff id="Aff4">
<label></label>
General Medicine Unit, Ospedale di Esine, Esine, Italy</aff>
<aff id="Aff5">
<label></label>
Infection Control Unit, Groupe Hospitalier Paris Saint-Joseph, Paris, France</aff>
<aff id="Aff6">
<label></label>
First Department of Propaedeutic Medicine, Laiko General Hospital, Athens, Greece</aff>
<aff id="Aff7">
<label></label>
Service of Infectious Diseases, Hospital Clinic de Barcelona, Barcelona, Spain</aff>
<aff id="Aff8">
<label></label>
Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand</aff>
<aff id="Aff9">
<label></label>
Centre for Clinical Vaccinology and Tropical Medicine, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK</aff>
</contrib-group>
<pub-date pub-type="epub">
<day>27</day>
<month>2</month>
<year>2015</year>
</pub-date>
<pub-date pub-type="pmc-release">
<day>27</day>
<month>2</month>
<year>2015</year>
</pub-date>
<pub-date pub-type="collection">
<year>2015</year>
</pub-date>
<volume>15</volume>
<elocation-id>105</elocation-id>
<history>
<date date-type="received">
<day>20</day>
<month>9</month>
<year>2014</year>
</date>
<date date-type="accepted">
<day>12</day>
<month>2</month>
<year>2015</year>
</date>
</history>
<permissions>
<copyright-statement>© Lee et al.; licensee BioMed Central. 2015</copyright-statement>
<license license-type="open-access">
<license-p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (
<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/4.0">http://creativecommons.org/licenses/by/4.0</ext-link>
), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (
<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/publicdomain/zero/1.0/">http://creativecommons.org/publicdomain/zero/1.0/</ext-link>
) applies to the data made available in this article, unless otherwise stated.</license-p>
</license>
</permissions>
<abstract id="Abs1">
<sec>
<title>Background</title>
<p>Predictive models to identify unknown methicillin-resistant
<italic>Staphylococcus aureus</italic>
(MRSA) carriage on admission may optimise targeted MRSA screening and efficient use of resources. However, common approaches to model selection can result in overconfident estimates and poor predictive performance. We aimed to compare the performance of various models to predict previously unknown MRSA carriage on admission to surgical wards.</p>
</sec>
<sec>
<title>Methods</title>
<p>The study analysed data collected during a prospective cohort study which enrolled consecutive adult patients admitted to 13 surgical wards in 4 European hospitals. The participating hospitals were located in Athens (Greece), Barcelona (Spain), Cremona (Italy) and Paris (France). Universal admission MRSA screening was performed in the surgical wards. Data regarding demographic characteristics and potential risk factors for MRSA carriage were prospectively collected during the study period. Four logistic regression models were used to predict probabilities of unknown MRSA carriage using risk factor data: “Stepwise” (variables selected by backward elimination); “Best BMA” (model with highest posterior probability using Bayesian model averaging which accounts for uncertainty in model choice); “BMA” (average of all models selected with BMA); and “Simple” (model including variables selected >50% of the time by both Stepwise and BMA approaches applied to repeated random sub-samples of 50% of the data). To assess model performance, cross-validation against data not used for model fitting was conducted and net reclassification improvement (NRI) was calculated.</p>
</sec>
<sec>
<title>Results</title>
<p>Of 2,901 patients enrolled, 111 (3.8%) were newly identified MRSA carriers. Recent hospitalisation and presence of a wound/ulcer were significantly associated with MRSA carriage in all models. While all models demonstrated limited predictive ability (mean
<italic>c</italic>
-statistics <0.7) the Simple model consistently detected more MRSA-positive individuals despite screening fewer patients than the Stepwise model. Moreover, the Simple model improved reclassification of patients into appropriate risk strata compared with the Stepwise model (NRI 6.6%,
<italic>P</italic>
 = .07).</p>
</sec>
<sec>
<title>Conclusions</title>
<p>Though commonly used, models developed using stepwise variable selection can have relatively poor predictive value. When developing MRSA risk indices, simpler models, which account for uncertainty in model selection, may better stratify patients’ risk of unknown MRSA carriage.</p>
</sec>
</abstract>
<kwd-group xml:lang="en">
<title>Keywords</title>
<kwd>Methicillin-resistant
<italic>Staphylococcus aureus</italic>
</kwd>
<kwd>Screening</kwd>
<kwd>Predictive models</kwd>
</kwd-group>
<custom-meta-group>
<custom-meta>
<meta-name>issue-copyright-statement</meta-name>
<meta-value>© The Author(s) 2015</meta-value>
</custom-meta>
</custom-meta-group>
</article-meta>
</front>
</pmc>
<affiliations>
<list>
<country>
<li>Australie</li>
<li>Espagne</li>
<li>France</li>
<li>Grèce</li>
<li>Italie</li>
<li>Royaume-Uni</li>
<li>Suisse</li>
<li>Thaïlande</li>
</country>
<region>
<li>Angleterre</li>
<li>Attique (région)</li>
<li>Catalogne</li>
<li>Nouvelle-Galles du Sud</li>
<li>Oxfordshire</li>
<li>Île-de-France</li>
</region>
<settlement>
<li>Athènes</li>
<li>Barcelone</li>
<li>Oxford</li>
<li>Paris</li>
<li>Sydney</li>
</settlement>
<orgName>
<li>Université d'Oxford</li>
</orgName>
</list>
<tree>
<country name="Suisse">
<noRegion>
<name sortKey="Lee, Andie S" sort="Lee, Andie S" uniqKey="Lee A" first="Andie S" last="Lee">Andie S. Lee</name>
</noRegion>
<name sortKey="Harbarth, Stephan" sort="Harbarth, Stephan" uniqKey="Harbarth S" first="Stephan" last="Harbarth">Stephan Harbarth</name>
</country>
<country name="Australie">
<region name="Nouvelle-Galles du Sud">
<name sortKey="Lee, Andie S" sort="Lee, Andie S" uniqKey="Lee A" first="Andie S" last="Lee">Andie S. Lee</name>
</region>
</country>
<country name="Italie">
<noRegion>
<name sortKey="Pan, Angelo" sort="Pan, Angelo" uniqKey="Pan A" first="Angelo" last="Pan">Angelo Pan</name>
</noRegion>
<name sortKey="Garilli, Silvia" sort="Garilli, Silvia" uniqKey="Garilli S" first="Silvia" last="Garilli">Silvia Garilli</name>
<name sortKey="Patroni, Andrea" sort="Patroni, Andrea" uniqKey="Patroni A" first="Andrea" last="Patroni">Andrea Patroni</name>
</country>
<country name="France">
<region name="Île-de-France">
<name sortKey="Chalfine, Annie" sort="Chalfine, Annie" uniqKey="Chalfine A" first="Annie" last="Chalfine">Annie Chalfine</name>
</region>
</country>
<country name="Grèce">
<region name="Attique (région)">
<name sortKey="Daikos, George L" sort="Daikos, George L" uniqKey="Daikos G" first="George L" last="Daikos">George L. Daikos</name>
</region>
</country>
<country name="Espagne">
<region name="Catalogne">
<name sortKey="Martinez, Jose Antonio" sort="Martinez, Jose Antonio" uniqKey="Martinez J" first="José Antonio" last="Martínez">José Antonio Martínez</name>
</region>
</country>
<country name="Thaïlande">
<noRegion>
<name sortKey="Cooper, Ben S" sort="Cooper, Ben S" uniqKey="Cooper B" first="Ben S" last="Cooper">Ben S. Cooper</name>
</noRegion>
</country>
<country name="Royaume-Uni">
<region name="Angleterre">
<name sortKey="Cooper, Ben S" sort="Cooper, Ben S" uniqKey="Cooper B" first="Ben S" last="Cooper">Ben S. Cooper</name>
</region>
</country>
</tree>
</affiliations>
</record>

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