title: Simple point-of-care risk stratification in acute coronary syndromes: the AMIS model.
abstract: BACKGROUND
  Early risk stratification is important in the management of patients with acute coronary syndromes (ACS).
  
  
  OBJECTIVE
  To develop a rapidly available risk stratification tool for use in all ACS.
  
  
  METHODS
  Application of modern data mining and machine learning algorithms to a derivation cohort of 7520 ACS patients included in the AMIS (Acute Myocardial Infarction in Switzerland)-Plus registry between 2001 and 2005; prospective model testing in two validation cohorts.
  
  
  RESULTS
  The most accurate prediction of in-hospital mortality was achieved with the "Averaged One-Dependence Estimators" (AODE) algorithm, with input of seven variables available at first patient contact: age, Killip class, systolic blood pressure, heart rate, pre-hospital cardiopulmonary resuscitation, history of heart failure, history of cerebrovascular disease. The c-statistic for the derivation cohort (0.875) was essentially maintained in important subgroups, and calibration over five risk categories, ranging from <1% to >30% predicted mortality, was accurate. Results were validated prospectively against an independent AMIS-Plus cohort (n = 2854, c-statistic 0.868) and the Krakow-Region ACS Registry (n = 2635, c-statistic 0.842). The AMIS model significantly outperformed established "point-of-care" risk-prediction tools in both validation cohorts. In comparison to a logistic regression-based model, the AODE-based model proved to be more robust when tested on the Krakow validation cohort (c-statistic 0.842 vs 0.746). Accuracy of the AMIS model prediction was maintained at 12-month follow-up in an independent cohort (n = 1972, c-statistic 0.877).
  
  
  CONCLUSIONS
  The AMIS model is a reproducibly accurate point-of-care risk stratification tool for the complete range of ACS, based on variables available at first patient contact.
authors.affiliation: Department of Internal Medicine, Division of Cardiology, Triemli Hospital, Birmensdorferstrasse 497, CH-8063 Zurich, Switzerland. david.kurz@triemli.stzh.ch
authors.forename: D J
authors.initials: DJ
authors.lastname: Kurz
authors.forename: A
authors.initials: A
authors.lastname: Bernstein
authors.forename: K
authors.initials: K
authors.lastname: Hunt
authors.forename: D
authors.initials: D
authors.lastname: Radovanovic
authors.forename: P
authors.initials: P
authors.lastname: Erne
authors.forename: Z
authors.initials: Z
authors.lastname: Siudak
authors.forename: O
authors.initials: O
authors.lastname: Bertel
mesh_terms: D054058:Acute Coronary Syndrome / Q000175:diagnosis* / Q000503:physiopathology; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001185:Artificial Intelligence; D003661:Decision Support Techniques; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D004812:Epidemiologic Methods; D005189:False Positive Reactions; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D019095:Point-of-Care Systems; D011379:Prognosis; D018570:Risk Assessment / Q000379:methods