title: How to improve the management of chest pain: hospitalists and use of prediction rules.
abstract: BACKGROUND
  Three percent of patients with acute myocardial infarction are still missed despite the excess number of admissions with chest pain. The purpose of this study was to review the characteristics of patients who were admitted with chest pain, to evaluate the appropriateness of admissions and the outcomes. We also discuss whether use of a prediction rule could have made a difference in the management of these cases.
  
  
  METHODS
  We performed retrospective chart review on all patients admitted to the hospitalist service with a diagnosis of chest pain. Each patient was risk stratified using Diamond and Forrester algorithm for probability of coronary artery disease (CAD), retrospectively. Results were analyzed using chi2 test or exact test and Student's t test.
  
  
  RESULTS
  Of 260 patients admitted with chest pain to the hospitalist service, only 24 (9.2%) received the final diagnosis of acute coronary syndrome (ACS). The patients in the ACS group were older and more likely to be male and to have a history of hyperlipidemia, CAD, peripheral vascular disease, cerebrovascular disease and percutaneous coronary intervention (PCI). Of 34 patients who underwent cardiac catheterization, 20 (58.8%) had occlusive CAD and 14 of them received PCI. Risk stratification of patients, retrospectively, revealed 28.3% of the total patient population was high risk, while 6.6% of them were low risk. The number of ACS cases was highest in the high risk group, while none was detected in the low risk group.
  
  
  CONCLUSIONS
  Our study demonstrated that using a prediction rule could have prevented about 6% of the chest pain admissions. Therefore, the use of risk stratification methods should be encouraged to decrease cost and improve efficiency of care.
authors.affiliation: Carolinas Medical Center-University, PO Box 560727, Charlotte, NC 28256, USA. bcakir@carolina.rr.com
authors.forename: Beril
authors.initials: B
authors.lastname: Cakir
authors.forename: Kay
authors.initials: K
authors.lastname: Blue
mesh_terms: D000367:Age Factors; D000465:Algorithms; D015906:Angioplasty, Balloon, Coronary; D006328:Cardiac Catheterization; D002561:Cerebrovascular Disorders / Q000150:complications; D002637:Chest Pain / Q000175:diagnosis* / Q000628:therapy; D003324:Coronary Artery Disease / Q000150:complications / Q000175:diagnosis; D003327:Coronary Disease / Q000175:diagnosis; D005260:Female; D005544:Forecasting; D020445:Hospitalists; D006801:Humans; D006949:Hyperlipidemias / Q000150:complications; D008297:Male; D008875:Middle Aged; D009203:Myocardial Infarction / Q000175:diagnosis; D010343:Patient Admission; D016491:Peripheral Vascular Diseases / Q000150:complications; D011336:Probability; D011379:Prognosis; D012189:Retrospective Studies; D018570:Risk Assessment; D012307:Risk Factors; D012737:Sex Factors; D013577:Syndrome