title: Validation of a prediction score model to distinguish acute coronary syndromes from other conditions causing raised cardiac troponin T levels.
abstract: OBJECTIVE
  Serum cardiac troponins can be elevated in acute coronary syndromes (ACS) and other non-ACS conditions. We investigated the usefulness of a prediction score model comprising clinical variables to distinguish patients with ACS from other non-ACS conditions.
  
  
  METHODS
  Two independent, non-randomized observational cohorts (groups 1 and 2) were examined, comprising consecutive patients who were admitted to a university teaching hospital and found to have a raised serum troponin T level (>or=0.01 microg/l). The international definition was used to confirm acute myocardial infarction. Multivariate logistic regression identified clinical variables in the first cohort, which were used to construct a score model for distinguishing between ACS and non-ACS, and this score was re-evaluated in the second cohort.
  
  
  RESULTS
  Of the 313 patients in group 1, a score model was formulated using logarithm troponin T, ischaemic chest pain, ST depression and atrial fibrillation or flutter. Using a score of more than or equal to 1.5, sensitivity and specificity for predicting non-ACS were 0.81 and 0.84. The area under the curve was 0.900 (95% confidence interval 0.867-0.934). Sensitivity and specificity for predicting non-ACS among the 341 patients in group 2 using the same model and a score of more than or equal to 1.5 were 0.76 and 0.89, respectively, and the area under the curve was 0.918 (confidence interval 0.887-0.945).
  
  
  CONCLUSIONS
  A prediction score model using simple clinical variables has been validated, and this can help clinicians in distinguishing patients with ACS from other non-ACS conditions.
authors.affiliation: Aintree Cardiac Centre, University Hospital Aintree, Liverpool, UK. peter.wong@aintree.nhs.uk
authors.forename: Peter S C
authors.initials: PS
authors.lastname: Wong
authors.forename: Gopala K
authors.initials: GK
authors.lastname: Rao
authors.forename: Antony L
authors.initials: AL
authors.lastname: Innasimuthu
authors.forename: Yawer
authors.initials: Y
authors.lastname: Saeed
authors.forename: Charles
authors.initials: C
authors.lastname: van Heyningen
authors.forename: Derek R
authors.initials: DR
authors.lastname: Robinson
mesh_terms: D054058:Acute Coronary Syndrome / Q000097:blood / Q000150:complications / Q000175:diagnosis* / Q000401:mortality; D000368:Aged; D000369:Aged, 80 and over; D000787:Angina Pectoris / Q000209:etiology; D019540:Area Under Curve; D001281:Atrial Fibrillation / Q000453:epidemiology; D001282:Atrial Flutter / Q000453:epidemiology; D015415:Biomarkers / Q000097:blood; D016009:Chi-Square Distribution; D015897:Comorbidity; D003937:Diagnosis, Differential; D004562:Electrocardiography; D004739:England; D005260:Female; D017052:Hospital Mortality; D006785:Hospitals, University; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D016017:Odds Ratio; D011237:Predictive Value of Tests; D011379:Prognosis; D011446:Prospective Studies; D015203:Reproducibility of Results; D018570:Risk Assessment; D012307:Risk Factors; D012680:Sensitivity and Specificity; D013997:Time Factors; D020107:Troponin T / Q000097:blood*; D015854:Up-Regulation
chemical_list: D015415:Biomarkers; D020107:Troponin T