title: Risk stratification after myocardial infarction.
abstract: OBJECTIVE
  To review the literature on risk stratification after acute myocardial infarction in the reperfusion era and to propose an algorithm for early and continual risk assessment.
  
  
  METHODS
  A MEDLINE search of the English-language literature on humans was done using the terms myocardial infarction, prospective studies, and prognosis. This search was supplemented by narrowed searches for subheadings (such as cardiogenic shock, thrombolytic therapy, and stress testing) and surveys of references cited in review articles and book chapters.
  
  
  METHODS
  Literature on prognosis and myocardial infarction published from 1981 to 1996 was considered. From the literature on stress testing methods, studies that enrolled patients before 1980, enrolled patients for indications other than myocardial infarction, tested patients more than 6 weeks after infarction, were missing outcome data, or had inadequate follow-up were excluded.
  
  
  METHODS
  Because too few randomized trials were available to allow the cross-comparison of risk-stratification methods, the available observational data were synthesized and supplemented with clinical judgments to produce recommendations.
  
  
  RESULTS
  Risk stratification must begin when acute myocardial infarction is diagnosed. High-risk patients (such as those with cardiogenic shock) and candidates for reperfusion therapy must be identified quickly if ideal emergency care is to be given. At specific points during hospitalization, specialized tests may be useful if they add incremental information to the results of clinical evaluations. High-risk patients who have complications after infarction or significant left ventricular dysfunction probably benefit from early angiography; patients without these conditions are at low risk for recurrent events and should have noninvasive stress testing for further risk stratification.
  
  
  CONCLUSIONS
  Physicians should continually reappraise risk throughout hospitalization to optimize both patient outcomes and cost containment.
authors.affiliation: Duke University Medical Center, Durham, North Carolina, USA.
authors.forename: E D
authors.initials: ED
authors.lastname: Peterson
authors.forename: L J
authors.initials: LJ
authors.lastname: Shaw
authors.forename: R M
authors.initials: RM
authors.lastname: Califf
mesh_terms: D000465:Algorithms; D003363:Cost Control; D019317:Evidence-Based Medicine; D006760:Hospitalization / Q000191:economics; D006801:Humans; D009203:Myocardial Infarction / Q000150:complications / Q000175:diagnosis* / Q000191:economics / Q000628:therapy*; D017063:Outcome Assessment, Health Care; D010351:Patient Discharge; D012307:Risk Factors