title: Emergency department visit classification using the NYU algorithm.
abstract: OBJECTIVE
  Reliable measures of emergency department (ED) use are important for studying ED utilization and access to care. We assessed the association of emergent classification of an ED visit based on the New York University ED Algorithm (EDA) with hospital mortality and hospital admission.
  
  
  METHODS
  Using diagnosis codes, we applied the EDA to classify ED visits into emergent, intermediate, and nonemergent categories and studied associations of emergent status with hospital mortality and hospital admissions.
  
  
  METHODS
  We used a nationally representative sample of patients with visits to hospital-based EDs from repeated cross sections of the National Hospital Ambulatory Medical Care Survey from 2006 to 2009. We performed survey-weighted logistic regression analyses, adjusting for year and patient demographic and socioeconomic characteristics, to estimate the association of emergent ED visits with the probability of hospital mortality or hospital admission.
  
  
  RESULTS
  The EDA measure of emergent visits was significantly and positively associated with mortality (odds ratio [OR]: 3.79, 95% confidence interval [CI]: 2.50-5.75) and hospital admission (OR: 5.28, 95% CI, 4.93-5.66).
  
  
  CONCLUSIONS
  This analysis assessed the NYU algorithm in measuring emergent and nonemergent ED use in the general population. Emergent classification based on the algorithm was strongly and significantly positively associated with hospitalization and death in a nationally representative population. The algorithm can be useful in studying ED utilization and evaluating policies that aim to change it.
authors.affiliation: RTI International, 701 13th St NW, Ste 750, Washington, DC 20005-3967. E-mail: sgandhi@rti.org.
authors.forename: Sabina Ohri
authors.initials: SO
authors.lastname: Gandhi
authors.forename: Lindsay
authors.initials: L
authors.lastname: Sabik
mesh_terms: D000328:Adult; D000367:Age Factors; D000368:Aged; D000465:Algorithms; D004636:Emergency Service, Hospital / Q000706:statistics & numerical data*; D005260:Female; D019538:Health Care Surveys; D006297:Health Services Accessibility / Q000706:statistics & numerical data*; D017052:Hospital Mortality; D006785:Hospitals, University; D006801:Humans; D015994:Incidence; D038801:International Classification of Diseases / Q000145:classification*; D008297:Male; D008875:Middle Aged; D016017:Odds Ratio; D017063:Outcome Assessment, Health Care; D010343:Patient Admission / Q000706:statistics & numerical data; D018570:Risk Assessment; D012737:Sex Factors; D012959:Socioeconomic Factors; D014481:United States