title: A risk scoring system to identify emergency department patients with heart failure at high risk for serious adverse events.
abstract: OBJECTIVE
  There are no validated guidelines to guide physicians with difficult disposition decisions for emergency department (ED) patients with heart failure (HF). The authors sought to develop a risk scoring system to identify HF patients at high risk for serious adverse events (SAEs).
  
  
  METHODS
  This was a prospective cohort study at six large Canadian EDS that enrolled adult patients who presented with acute decompensated HF. Each patient was assessed for standardized clinical and laboratory variables as well as for SAEs defined as death, intubation, admission to a monitored unit, or relapse requiring admission. Adjusted odds ratios for predictors of SAEs were calculated by stepwise logistic regression.
  
  
  RESULTS
  In 559 visits, 38.1% resulted in patient admission. Of 65 (11.6%) SAE cases, 31 (47.7%) occurred in patients not initially admitted. The multivariate model and resultant Ottawa Heart Failure Risk Scale consists of 10 elements, and the risk of SAEs varied from 2.8% to 89.0%, with good calibration between observed and expected probabilities. Internal validation showed the risk scores to be very accurate across 1,000 replications using the bootstrap method. A threshold of 1, 2, or 3 total scores for admission would be associated with sensitivities of 95.2, 80.6, or 64.5%, respectively, all better than current practice.
  
  
  CONCLUSIONS
  Many HF patients are discharged home from the ED and then suffer SAEs or death. The authors have developed an accurate risk scoring system that could ultimately be used to stratify the risk of poor outcomes and to enable rational and safe disposition decisions.
authors.affiliation: Department of Emergency Medicine, University of Ottawa, Ottawa, Ontario, Canada. istiell@ohri.ca
authors.forename: Ian G
authors.initials: IG
authors.lastname: Stiell
authors.forename: Catherine M
authors.initials: CM
authors.lastname: Clement
authors.forename: Robert J
authors.initials: RJ
authors.lastname: Brison
authors.forename: Brian H
authors.initials: BH
authors.lastname: Rowe
authors.forename: Bjug
authors.initials: B
authors.lastname: Borgundvaag
authors.forename: Shawn D
authors.initials: SD
authors.lastname: Aaron
authors.forename: Eddy
authors.initials: E
authors.lastname: Lang
authors.forename: Lisa A
authors.initials: LA
authors.lastname: Calder
authors.forename: Jeffrey J
authors.initials: JJ
authors.lastname: Perry
authors.forename: Alan J
authors.initials: AJ
authors.lastname: Forster
authors.forename: George A
authors.initials: GA
authors.lastname: Wells
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000704:Analysis of Variance; D002423:Cause of Death; D015331:Cohort Studies; D018450:Disease Progression; D004635:Emergency Medicine / Q000592:standards / Q000639:trends; D004636:Emergency Service, Hospital / Q000706:statistics & numerical data*; D004638:Emergency Treatment / Q000009:adverse effects* / Q000379:methods; D005260:Female; D006333:Heart Failure / Q000175:diagnosis / Q000401:mortality* / Q000628:therapy*; D017052:Hospital Mortality / Q000639:trends; D006801:Humans; D016015:Logistic Models; D008297:Male; D015999:Multivariate Analysis; D016017:Odds Ratio; D010343:Patient Admission / Q000706:statistics & numerical data; D010351:Patient Discharge / Q000706:statistics & numerical data*; D012189:Retrospective Studies; D018570:Risk Assessment; D012720:Severity of Illness Index; D015996:Survival Rate; D013997:Time Factors