Variable performance of models for predicting methicillin-resistant Staphylococcus aureus carriage in European surgical wards
Identifieur interne : 002657 ( Main/Exploration ); précédent : 002656; suivant : 002658Variable 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 :
- BMC Infectious Diseases [ 1471-2334 ] ; 2015.
Descripteurs français
- KwdFr :
- Adulte, Adulte d'âge moyen, Dépistage systématique, Espagne (épidémiologie), Facteurs de l'âge, Facteurs de risque, Femelle, Grèce (épidémiologie), Hospitalisation (), Humains, Hôpitaux, Infections à staphylocoques (), Italie (épidémiologie), Muqueuse nasale (microbiologie), Mâle, Paris (épidémiologie), Porteur sain (diagnostic), Porteur sain (épidémiologie), Prévalence, Périnée (microbiologie), Résistance à la méticilline, Staphylococcus aureus résistant à la méticilline (isolement et purification), Statistiques comme sujet, Sujet âgé, Sujet âgé de 80 ans ou plus, Techniques d'aide à la décision, Unités hospitalières, Études de cohortes, Études prospectives.
- MESH :
- diagnostic : Porteur sain.
- isolement et purification : Staphylococcus aureus résistant à la méticilline.
- microbiologie : Muqueuse nasale, Périnée.
- épidémiologie : Espagne, Grèce, Italie, Paris, Porteur sain.
- Adulte, Adulte d'âge moyen, Dépistage systématique, Facteurs de l'âge, Facteurs de risque, Femelle, Hospitalisation, Humains, Hôpitaux, Infections à staphylocoques, Mâle, Prévalence, Résistance à la méticilline, Statistiques comme sujet, Sujet âgé, Sujet âgé de 80 ans ou plus, Techniques d'aide à la décision, Unités hospitalières, Études de cohortes, Études prospectives.
- Wicri :
English descriptors
- KwdEn :
- Adult, Age Factors, Aged, Aged, 80 and over, Carrier State (diagnosis), Carrier State (epidemiology), Cohort Studies, Decision Support Techniques, Female, Greece (epidemiology), Hospital Units, Hospitalization (statistics & numerical data), Hospitals, Humans, Italy (epidemiology), Male, Mass Screening, Methicillin Resistance, Methicillin-Resistant Staphylococcus aureus (isolation & purification), Middle Aged, Nasal Mucosa (microbiology), Paris (epidemiology), Perineum (microbiology), Prevalence, Prospective Studies, Risk Factors, Spain (epidemiology), Staphylococcal Infections (prevention & control), Statistics as Topic.
- MESH :
- geographic , epidemiology : Greece, Italy, Paris, Spain.
- diagnosis : Carrier State.
- epidemiology : Carrier State.
- isolation & purification : Methicillin-Resistant Staphylococcus aureus.
- microbiology : Nasal Mucosa, Perineum.
- prevention & control : Staphylococcal Infections.
- statistics & numerical data : Hospitalization.
- Adult, Age Factors, Aged, Aged, 80 and over, Cohort Studies, Decision Support Techniques, Female, Hospital Units, Hospitals, Humans, Male, Mass Screening, Methicillin Resistance, Middle Aged, Prevalence, Prospective Studies, Risk Factors, Statistics as Topic.
Abstract
Predictive models to identify unknown methicillin-resistant
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.
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
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:
- Australie, Espagne, France, Grèce, Italie, Royaume-Uni, Suisse, Thaïlande
- Angleterre, Attique (région), Catalogne, Nouvelle-Galles du Sud, Oxfordshire, Île-de-France
- Athènes, Barcelone, Oxford, Paris, Sydney
- Université d'Oxford
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Le document en format XML
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<term>Périnée</term>
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<term>Porteur sain</term>
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<term>Age Factors</term>
<term>Aged</term>
<term>Aged, 80 and over</term>
<term>Cohort Studies</term>
<term>Decision Support Techniques</term>
<term>Female</term>
<term>Hospital Units</term>
<term>Hospitals</term>
<term>Humans</term>
<term>Male</term>
<term>Mass Screening</term>
<term>Methicillin Resistance</term>
<term>Middle Aged</term>
<term>Prevalence</term>
<term>Prospective Studies</term>
<term>Risk Factors</term>
<term>Statistics as Topic</term>
</keywords>
<keywords scheme="MESH" xml:lang="fr"><term>Adulte</term>
<term>Adulte d'âge moyen</term>
<term>Dépistage systématique</term>
<term>Facteurs de l'âge</term>
<term>Facteurs de risque</term>
<term>Femelle</term>
<term>Hospitalisation</term>
<term>Humains</term>
<term>Hôpitaux</term>
<term>Infections à staphylocoques</term>
<term>Mâle</term>
<term>Prévalence</term>
<term>Résistance à la méticilline</term>
<term>Statistiques comme sujet</term>
<term>Sujet âgé</term>
<term>Sujet âgé de 80 ans ou plus</term>
<term>Techniques d'aide à la décision</term>
<term>Unités hospitalières</term>
<term>Études de cohortes</term>
<term>Études prospectives</term>
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<keywords scheme="Wicri" type="geographic" xml:lang="fr"><term>Grèce</term>
<term>Italie</term>
<term>Espagne</term>
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<front><div type="abstract" xml:lang="en"><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>
</div>
</front>
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</div1>
</back>
</TEI>
<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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