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LIBRIS Formathandbok  (Information om MARC21)
00005346naa a22007933a 4500
008180504s2017 sw |||| o |||| ||eng c
024a http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-1353092 uri
024a urn:nbn:se:liu:diva-1353092 urn
024a 10.1016/j.jclinepi.2016.09.0112 doi
040 a S
041a eng
042 9 EPLK
100a Aerts, Marc4 aut
2451 0a Pooled individual patient data from five countries were used to derive a clinical prediction rule for coronary artery disease in primary care.h [Elektronisk resurs]
260 b Elsevierc 2017
500 a <p>Funding agencies: Federal Ministry of Education and Research, Germany (BMBF) [FKZ 01GK0920]</p>
500 a Published
506a gratis
520 a 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 &lt;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 &lt;2 (i.e., -1, 0, or +1). When the score was &lt;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.
650 7a Medical and Health Sciences2 hsv
650 7a Health Sciences2 hsv
650 7a Public Health, Global Health, Social Medicine and Epidemiology2 hsv
650 7a Medicin och hälsovetenskap2 hsv
650 7a Hälsovetenskaper2 hsv
650 7a Folkhälsovetenskap, global hälsa, socialmedicin och epidemiologi2 hsv
650 7a Clinical Medicine2 hsv
650 7a Cardiac and Cardiovascular Systems2 hsv
650 7a Klinisk medicin2 hsv
650 7a Kardiologi2 hsv
653 0a Chest pain
653 0a Individual patient data meta-analysis
653 0a Medical history taking
653 0a Myocardial ischemia
653 0a Primary health care
653 0a Sensitivity and specificity
653 0a Symptom assessment
700a Minalu, Girma4 aut
700a Bösner, Stefan4 aut
700a Buntinx, Frank4 aut
700a Burnand, Bernard4 aut
700a Haasenritter, Jörg4 aut
700a Herzig, Lilli4 aut
700a Knottnerus, J André4 aut0 137212
700a Nilsson, Staffan4 aut
700a Renier, Walter4 aut
700a Sox, Carol4 aut
700a Sox, Harold4 aut
700a Donner-Banzhoff, Norbert4 aut
7101 2a Linköpings universitetb Institutionen för medicin och hälsa4 pbl0 308697
7101 2a Linköpings universitetb Medicinska fakulteten4 pbl
7101 2a Region Östergötlandb Primärvårdscentrum4 pbl
7721 8i channel recordw 18478042
7730 8i Värdpublikationt Journal of Clinical Epidemiologyg 81, 120-128x 0895-4356
8564 0u http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-135309
8564 0u http://dx.doi.org/10.1016/j.jclinepi.2016.09.011
8564 0u http://liu.diva-portal.org/smash/get/diva2:1080496/FULLTEXT01
9102 s6 710a IMHu Linköpings universitet.b Institutionen för medicin och hälsa
9102 s6 710i Engelska:a Linköping Universty.b Department of Medical and Health Sciencesw iu Linköpings universitet.b Institutionen för medicin och hälsa
9102 s6 710a Linköping Universty.b Department of Medicine and Health Sciencesu Linköpings universitet.b Institutionen för medicin och hälsa
9102 k6 710a Linköpings universitet.b Institutionen för hälsa och samhälleu Linköpings universitet.b Institutionen för medicin och hälsa
9102 k6 710a Linköpings universitet.b Institutionen för medicin och vårdu Linköpings universitet.b Institutionen för medicin och hälsa
841 5 APISa x ab 180504||0000|||||001||||||000000e 1
0245 APISa urn:nbn:se:liu:diva-1353092 urn
852 5 APISb APIS
8564 05 APISu http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-135309

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