title: A prediction rule to identify low-risk patients with pulmonary embolism.
abstract: BACKGROUND
  A simple prognostic model could help identify patients with pulmonary embolism who are at low risk of death and are candidates for outpatient treatment.
  
  
  METHODS
  We randomly allocated 15,531 retrospectively identified inpatients who had a discharge diagnosis of pulmonary embolism from 186 Pennsylvania hospitals to derivation (67%) and internal validation (33%) samples. We derived our rule to predict 30-day mortality using classification tree analysis and patient data routinely available at initial examination as potential predictor variables. We used data from a European prospective study to externally validate the rule among 221 inpatients with pulmonary embolism. We determined mortality and nonfatal adverse medical outcomes across derivation and validation samples.
  
  
  RESULTS
  Our final model consisted of 10 patient factors (age > or = 70 years; history of cancer, heart failure, chronic lung disease, chronic renal disease, and cerebrovascular disease; and clinical variables of pulse rate > or = 110 beats/min, systolic blood pressure < 100 mm Hg, altered mental status, and arterial oxygen saturation < 90%). Patients with none of these factors were defined as low risk. The 30-day mortality rates for low-risk patients were 0.6%, 1.5%, and 0% in the derivation, internal validation, and external validation samples, respectively. The rates of nonfatal adverse medical outcomes were less than 1% among low-risk patients across all study samples.
  
  
  CONCLUSIONS
  This simple prediction rule accurately identifies patients with pulmonary embolism who are at low risk of short-term mortality and other adverse medical outcomes. Prospective validation of this rule is important before its implementation as a decision aid for outpatient treatment.
authors.affiliation: Division of General Internal Medicine, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA. aujesky@swissonline.ch
authors.forename: Drahomir
authors.initials: D
authors.lastname: Aujesky
authors.forename: D Scott
authors.initials: DS
authors.lastname: Obrosky
authors.forename: Roslyn A
authors.initials: RA
authors.lastname: Stone
authors.forename: Thomas E
authors.initials: TE
authors.lastname: Auble
authors.forename: Arnaud
authors.initials: A
authors.lastname: Perrier
authors.forename: Jacques
authors.initials: J
authors.lastname: Cornuz
authors.forename: Pierre-Marie
authors.initials: PM
authors.lastname: Roy
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Fine
mesh_terms: D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D015331:Cohort Studies; D003661:Decision Support Techniques; D016002:Discriminant Analysis; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D010414:Pennsylvania / Q000453:epidemiology; D011237:Predictive Value of Tests; D011336:Probability; D011655:Pulmonary Embolism / Q000145:classification* / Q000175:diagnosis / Q000453:epidemiology* / Q000628:therapy; D012042:Registries; D015203:Reproducibility of Results; D012189:Retrospective Studies; D012307:Risk Factors; D012720:Severity of Illness Index; D016019:Survival Analysis