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Pooled individual p...
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Aerts, MarcInteruniversity Institute for Biostatistics and Statistical Bioinformatics
(författare)
Pooled individual patient data from five countries were used to derive a clinical prediction rule for coronary artery disease in primary care.
- Artikel/kapitelEngelska2017
Förlag, utgivningsår, omfång ...
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Elsevier,2017
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electronicrdacarrier
Nummerbeteckningar
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LIBRIS-ID:oai:DiVA.org:liu-135309
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https://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-135309URI
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https://doi.org/10.1016/j.jclinepi.2016.09.011DOI
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Språk:engelska
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Sammanfattning på:engelska
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Ämneskategori:art swepub-publicationtype
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Funding agencies: Federal Ministry of Education and Research, Germany (BMBF) [FKZ 01GK0920]
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OBJECTIVE: To construct a clinical prediction rule for coronary artery disease (CAD) presenting with chest pain in primary care.STUDY DESIGN AND SETTING: Meta-Analysis using 3,099 patients from five studies. To identify candidate predictors, we used random forest trees, multiple imputation of missing values, and logistic regression within individual studies. To generate a prediction rule on the pooled data, we applied a regression model that took account of the differing standard data sets collected by the five studies.RESULTS: The most parsimonious rule included six equally weighted predictors: age ≥55 (males) or ≥65 (females) (+1); attending physician suspected a serious diagnosis (+1); history of CAD (+1); pain brought on by exertion (+1); pain feels like "pressure" (+1); pain reproducible by palpation (-1). CAD was considered absent if the prediction score is <2. The area under the ROC curve was 0.84. We applied this rule to a study setting with a CAD prevalence of 13.2% using a prediction score cutoff of <2 (i.e., -1, 0, or +1). When the score was <2, the probability of CAD was 2.1% (95% CI: 1.1-3.9%); when the score was ≥ 2, it was 43.0% (95% CI: 35.8-50.4%).CONCLUSIONS: Clinical prediction rules are a key strategy for individualizing care. Large data sets based on electronic health records from diverse sites create opportunities for improving their internal and external validity. Our patient-level meta-analysis from five primary care sites should improve external validity. Our strategy for addressing site-to-site systematic variation in missing data should improve internal validity. Using principles derived from decision theory, we also discuss the problem of setting the cutoff prediction score for taking action.
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Biuppslag (personer, institutioner, konferenser, titlar ...)
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Minalu, GirmaInteruniversity Institute for Biostatistics and Statistical Bioinformatics
(författare)
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Bösner, StefanDepartment of General Practice and Family Medicine, Philipps University Marburg, Germany.
(författare)
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Buntinx, FrankDepartment of Public Health and Primary Care, KU Leuven, Belgium; Department of General Practice, Maastricht University, The Netherlands.
(författare)
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Burnand, BernardInstitute of Social and Preventive Medicine, Lausanne University Hospital, Switzerland.
(författare)
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Haasenritter, JörgDepartment of General Practice and Family Medicine, Philipps University Marburg, Germany.
(författare)
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Herzig, LilliInstitute of Family Medicine, University of Lausanne, Switzerland.
(författare)
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Knottnerus, J AndréDepartment of General Practice, Maastricht University, The Netherlands.
(författare)
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Nilsson, StaffanLinköpings universitet,Avdelningen för samhällsmedicin,Medicinska fakulteten,Region Östergötland, Vårdcentralen Vikbolandet(Swepub:liu)stani72
(författare)
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Renier, WalterDepartment of Public Health and Primary Care, KU Leuven, Belgium
(författare)
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Sox, CarolDepartment of Community and Family Medicine, Geisel School of Medicine at Dartmouth, USA.
(författare)
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Sox, HaroldDepartment of Community and Family Medicine, Geisel School of Medicine at Dartmouth, Hanover, NH , USA; Patient-Centered Outcomes Research Institute, Washington, USA.
(författare)
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Donner-Banzhoff, NorbertDepartment of General Practice and Family Medicine, Philipps University Marburg, Germany.
(författare)
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Interuniversity Institute for Biostatistics and Statistical BioinformaticsDepartment of General Practice and Family Medicine, Philipps University Marburg, Germany.
(creator_code:org_t)
Sammanhörande titlar
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Ingår i:Journal of Clinical Epidemiology: Elsevier81, s. 120-1280895-43561878-5921
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Aerts, Marc
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Minalu, Girma
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Bösner, Stefan
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Buntinx, Frank
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Burnand, Bernard
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Haasenritter, Jö ...
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visa fler...
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Herzig, Lilli
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Knottnerus, J An ...
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Nilsson, Staffan
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Renier, Walter
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Sox, Carol
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Sox, Harold
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Donner-Banzhoff, ...
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