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  • Title: Predicting death in patients with acute type a aortic dissection.
    Author: Mehta RH, Suzuki T, Hagan PG, Bossone E, Gilon D, Llovet A, Maroto LC, Cooper JV, Smith DE, Armstrong WF, Nienaber CA, Eagle KA, International Registry of Acute Aortic Dissection (IRAD) Investigators.
    Journal: Circulation; 2002 Jan 15; 105(2):200-6. PubMed ID: 11790701.
    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. METHODS AND 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.
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