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BACKGROUND: The study was designed to evaluate the Acute Physiology And Chronic Health Evaluation (APACHE) II risk scoring system in abdominal aortic aneurysm (AAA) surgery. The aim was to create an APACHE-based risk stratification model for postoperative death. METHODS: Prospective postoperative APACHE II data were collected from patients undergoing AAA repair over a 9-year interval from 24 intensive care units (ICUs) in the Thames region. A multilevel logistic regression model (APACHE-AAA) for in-hospital mortality was developed to adjust for both case mix and the variation in outcome between ICUs. RESULTS: A total of 1896 patients were studied. The in-hospital mortality rate among the 1289 patients who had elective AAA repair was 9.6 (95 per cent confidence interval (c.i.) 8.0 to 11.2) per cent and that among the 605 patients who had an emergency repair was 46.9 (95 per cent c.i. 43.0 to 50.9) per cent. Four independent predictors of death were identified: age (odds ratio (OR) 1.05 (95 per cent c.i. 1.03 to 1.07) per year increase), Acute Physiology Score (OR 1.14 (95 per cent c.i. 1.12 to 1.17) per unit increase), emergency operation (OR 4.86 (95 per cent c.i. 3.64 to 6.52)) and chronic health dysfunction (OR 1.43 (95 per cent c.i. 1.04 to 1.97)). The APACHE-AAA model was internally valid, as shown by calibration (Hosmer-Lemeshow C statistic: chi(2) = 6.14, 8 d.f., P = 0.632), discrimination properties (area under receiver-operator characteristic curve 0.845) and subgroup analysis. There was no significant variation in outcome between hospitals. CONCLUSION: APACHE-AAA was shown to be an accurate risk-stratification model that could be used to quantify the risk of death after AAA surgery. It might also be used to determine the relative impact of ICU over high-dependency unit care.

Original publication




Journal article


Br J Surg

Publication Date





1092 - 1098


APACHE, Adult, Aged, Aortic Aneurysm, Abdominal, Hospital Mortality, Humans, Middle Aged, Multivariate Analysis, Prognosis, Prospective Studies, Regression Analysis, Risk Assessment