title: Evaluating patients with chest pain using classification and regression trees.
abstract: We collected data on 320 patients complaining to their general practitioner of a new episode of chest pain, discomfort or oppression. Relationships were examined between initial signs and symptoms and a follow-up diagnosis after a period of 2 weeks to 2 months. The data were analysed with CART, a statistical decision theory software package. In our first run, the number of misclassifications by CART was 56%. After regrouping of the data and diagnostic categories, there were 37% misclassifications. The most discriminating variable turned out to be pain on palpation. When comparing each of five diagnostic groups to all others, we found a positive predictive value of 27% for gastrointestinal diseases, 72% for cardiovascular disorders, 69% for respiratory diseases, 58% for psychopathology and 73% for chest wall pathology. The CART methodology needs further investigation and testing before any clinical application will be possible in general practice.
authors.affiliation: Flemish Institute of General Practice, State University of Limburg, Maastricht, Netherlands.
authors.forename: F
authors.initials: F
authors.lastname: Buntinx
authors.forename: J
authors.initials: J
authors.lastname: Truyen
authors.forename: P
authors.initials: P
authors.lastname: Embrechts
authors.forename: G
authors.initials: G
authors.lastname: Moreel
authors.forename: R
authors.initials: R
authors.lastname: Peeters
mesh_terms: D002637:Chest Pain / Q000145:classification* / Q000175:diagnosis / Q000209:etiology; D003661:Decision Support Techniques; D003663:Decision Trees; D005194:Family Practice / Q000379:methods* / Q000592:standards; D006801:Humans; D011237:Predictive Value of Tests; D011446:Prospective Studies; D012044:Regression Analysis