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dc.contributor.authorSchierenberg, Alwin
dc.contributor.authorMinnaard, Margaretha C.
dc.contributor.authorHopstaken, Rogier M.
dc.contributor.authorVan De Pol, Alma C.
dc.contributor.authorBroekhuizen, Berna D.L.
dc.contributor.authorDe Wit, Niek J.
dc.contributor.authorReitsma, Johannes B.
dc.contributor.authorVan Vugt, Saskia F.
dc.contributor.authorGraffelman, Aleida W.
dc.contributor.authorMelbye, Hasse
dc.contributor.authorRainer, Timothy H.
dc.contributor.authorSteurer, Johann
dc.contributor.authorHolm, Anette
dc.contributor.authorGonzales, Ralph
dc.contributor.authorDinant, Geert-Jan
dc.contributor.authorDe Groot, Joris A.H.
dc.contributor.authorVerheij, Theo J.M.
dc.date.accessioned2017-02-22T14:40:49Z
dc.date.available2017-02-22T14:40:49Z
dc.date.issued2016-02-26
dc.description.abstractBackground:<br> Pneumonia remains difficult to diagnose in primary care. Prediction models based on signs and symptoms (S&S) serve to minimize the diagnostic uncertainty. External validation of these models is essential before implementation into routine practice. In this study all published S&S models for prediction of pneumonia in primary care were externally validated in the individual patient data (IPD) of previously performed diagnostic studies.<br> Methods and Findings:<br> S&S models for diagnosing pneumonia in adults presenting to primary care with lower respiratory tract infection and IPD for validation were identified through a systematical search. Six prediction models and IPD of eight diagnostic studies (N total = 5308, prevalence pneumonia 12%) were included. Models were assessed on discrimination and calibration. Discrimination was measured using the pooled Area Under the Curve (AUC) and delta AUC, representing the performance of an individual model relative to the average dataset performance. Prediction models by van Vugt et al. and Heckerling et al. demonstrated the highest pooled AUC of 0.79 (95% CI 0.74–0.85) and 0.72 (0.68–0.76), respectively. Other models by Diehr et al., Singal et al., Melbye et al., and Hopstaken et al. demonstrated pooled AUCs of 0.65 (0.61–0.68), 0.64 (0.61–0.67), 0.56 (0.49–0.63) and 0.53 (0.5–0.56), respectively. A similar ranking was present based on the delta AUCs of the models. Calibration demonstrated close agreement of observed and predicted probabilities in the models by van Vugt et al. and Singal et al., other models lacked such correspondence. The absence of predictors in the IPD on dataset level hampered a systematical comparison of model performance and could be a limitation to the study.<br> Conclusions:<br> The model by van Vugt et al. demonstrated the highest discriminative accuracy coupled with reasonable to good calibration across the IPD of different study populations. This model is therefore the main candidate for primary care use.en_US
dc.descriptionSource: <a href=http://dx.doi.org/10.1371/journal.pone.0149895>doi: 10.1371/journal.pone.0149895</a>en_US
dc.identifier.citationSchierenberg A, Minnaard MC, Hopstaken RM, van de Pol AC, Broekhuizen BDL, de Wit NJ, et al. (2016) External Validation of Prediction Models for Pneumonia in Primary Care Patients with Lower Respiratory Tract Infection: An Individual Patient Data Meta-Analysis. PLoS ONE 11(2): e0149895. doi:10.1371/journal.pone.0149895en_US
dc.identifier.cristinIDFRIDAID 1418820
dc.identifier.doi10.1371/journal.pone.0149895
dc.identifier.issn1932-6203
dc.identifier.urihttps://hdl.handle.net/10037/10343
dc.language.isoengen_US
dc.publisherPublic Library of Scienceen_US
dc.relation.journalPLoS ONE
dc.rights.accessRightsopenAccessen_US
dc.subjectVDP::Medisinske Fag: 700::Helsefag: 800::Samfunnsmedisin, sosialmedisin: 801en_US
dc.subjectVDP::Medical disciplines: 700::Health sciences: 800::Community medicine, Social medicine: 801en_US
dc.titleExternal validation of prediction models for pneumonia in primary care patients with lower respiratory tract infection: An individual patient data meta-analysisen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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