title: Predicting death in patients with acute type a aortic dissection.
abstract: BACKGROUND
  Given the high mortality rates in patients with type A aortic dissection, predictive tools to identify patients at increased risk of death are needed to assist clinicians for optimal treatment.
  
  
  RESULTS
  Accordingly, we evaluated 547 patients with this diagnosis enrolled in the International Registry of Acute Aortic Dissection (IRAD) between January 1996 and December 1999. Univariate testing followed by multivariate logistic regression analysis was performed to identify independent predictors of death. In-hospital mortality rate was 32.5% in type A dissection patients. In-hospital complications (neurological deficits, altered mental status, myocardial or mesenteric ischemia, kidney failure, hypotension, cardiac tamponade, and limb ischemia) were increased in patients who died compared with survivors (P<0.05 for all). Logistic regression identified the following presenting variables as predictors of death: age > or =70 years (OR, 1.70; 95% CI, 1.05 to 2.77; P=0.03), abrupt onset of chest pain (OR 2.60; 95% CI, 1.22 to 5.54; P=0.01), hypotension/shock/tamponade (OR, 2.97; 95% CI, 1.83 to 4.81; P<0.0001), kidney failure (OR, 4.77; 95% CI, 1.80 to 12.6; P=0.002), pulse deficit (OR, 2.03; 95% CI, 1.25 to 3.29, P=0.004), and abnormal ECG (OR, 1.77; 95% CI, 1.06 to 2.95; P=0.03) (area under receiver operating curve, 0.74; Hosmer-Lemeshow statistic, P=0.75).
  
  
  CONCLUSIONS
  The in-hospital mortality rate in acute type A aortic dissection is high and can be predicted with the use of a clinical model incorporated in a simple risk prediction tool. This tool can be used to educate patients with dissection about their predicted risk and in clinical research for risk adjustment while comparing outcomes of different therapies.
authors.affiliation: University of Michigan, Ann Arbor 48109, USA.
authors.forename: Rajendra H
authors.initials: RH
authors.lastname: Mehta
authors.forename: Toru
authors.initials: T
authors.lastname: Suzuki
authors.forename: Peter G
authors.initials: PG
authors.lastname: Hagan
authors.forename: Eduardo
authors.initials: E
authors.lastname: Bossone
authors.forename: Dan
authors.initials: D
authors.lastname: Gilon
authors.forename: Alfredo
authors.initials: A
authors.lastname: Llovet
authors.forename: Luis C
authors.initials: LC
authors.lastname: Maroto
authors.forename: Jeanna V
authors.initials: JV
authors.lastname: Cooper
authors.forename: Dean E
authors.initials: DE
authors.lastname: Smith
authors.forename: William F
authors.initials: WF
authors.lastname: Armstrong
authors.forename: Christoph A
authors.initials: CA
authors.lastname: Nienaber
authors.forename: Kim A
authors.initials: KA
authors.lastname: Eagle
mesh_terms: D000368:Aged; D000784:Aortic Dissection / Q000175:diagnosis / Q000401:mortality*; D001014:Aortic Aneurysm / Q000175:diagnosis / Q000401:mortality*; D005260:Female; D005544:Forecasting; D017052:Hospital Mortality; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D012307:Risk Factors; D016019:Survival Analysis