4.6 Article

APACHE-II score and Killip class for patients with acute myocardial infarction

Journal

INTENSIVE CARE MEDICINE
Volume 36, Issue 9, Pages 1579-1586

Publisher

SPRINGER
DOI: 10.1007/s00134-010-1832-6

Keywords

Intensive care; Myocardial infarction; Severity of illness index; Outcome and process assessment; Mortality prediction; Acute physiology and chronic health evaluation (APACHE); Killip class

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To analyse the influence on the prognosis of intensive care unit (ICU) patients with acute myocardial infarction (AMI): prognostic index score, Killip class, AMI site, thrombolysis and other variables that might improve prognostic capacity and functioning of the APACHE-II index. Cohort study using prospectively gathered ARIAM project data. ICUs from 129 Spanish hospitals. ICU-admitted AMI patients in ARIAM database during 4-year period were retrospectively studied. The sample comprised 6,458 patients, 76.8% males, age 64.97 +/- A 12.56 years, APACHE-II score 9.49 +/- A 7.03 points and ICU mortality 8.9%. Mortality was higher for females (p < 0.001), anterior AMI site (p < 0.001), previous AMI (p < 0.001), delay-to-hospital arrival > 180 min (p = 0.003) and non-receipt of thrombolysis (p = 0.015). ICU mortality was related to age (p < 0.001) and APACHE-II score (p < 0.001). In multivariate analysis, it was related to APACHE-II (OR 1.16), age (OR 1.05), gender (OR 1.64), previous AMI (OR 1.57), anterior AMI (OR 2.05) and delay > 180 min (OR 1.37). Killip class, gathered in 1,893 patients, was significantly associated with ICU mortality, and two predictive models were constructed for this group using multivariate analysis. Area under ROC curve was 0.94 in one (Killip class, age, gender, APACHE-II) versus 0.92 in the other (same variables without APACHE-II). APACHE-II score and Killip class are useful for assessing the severity of patients with AMI and are complementary. Each can be used with a few commonly gathered clinical variables to construct prognostic models to assess severity. Their joint application yields a model with excellent discrimination capacity.

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