title: Utility of a clinical support tool for outpatient evaluation of pediatric chest pain.
abstract: This study evaluates a clinical pathway currently being employed at a large single-center pediatric cardiology practice. The dataset includes 1,997 pediatric patients with the primary complaint of chest pain. A logistic regression model was developed to predict cardiac disease and identify strong indicators of cardiac pathology. The area under the ROC curve was 0.73 and the Matthews correlation coefficient was 0.23. Given the low incidence of pathology disease, this study was unable to identify strong predictors of major cardiac pathology. The analysis did support syncope, palpitations and the onset of chest pain in the past 2-7 days as predictors of minor cardiac disease. However, the model indicated exertional chest pain is negatively associated with cardiac disease. This data should be evaluated with caution as some of the results are contrary to most clinical cardiologists' views. The majority of the results support the cardiac disease predictors in the clinical pathway.
authors.affiliation: Georgia Institute of Technology, Atlanta, GA, USA.
authors.forename: Thomas
authors.initials: T
authors.lastname: Perry
authors.forename: Hongyuan
authors.initials: H
authors.lastname: Zha
authors.forename: Matthew E
authors.initials: ME
authors.lastname: Oster
authors.forename: Patricio A
authors.initials: PA
authors.lastname: Frias
authors.forename: Mark
authors.initials: M
authors.lastname: Braunstein
mesh_terms: D000293:Adolescent; D000704:Analysis of Variance; D002309:Cardiology / Q000379:methods; D002637:Chest Pain / Q000209:etiology*; D002648:Child; D019091:Critical Pathways; D003661:Decision Support Techniques; D057286:Electronic Health Records; D006331:Heart Diseases / Q000150:complications / Q000175:diagnosis*; D006801:Humans; D016015:Logistic Models; D010372:Pediatrics / Q000379:methods; D012372:ROC Curve; D011795:Surveys and Questionnaires