Abstract
To use classification tree analysis to identify risk factors for nonsurvival in a neurological patients with subarachnoid haemorrhage (SAH) and to propose a clinical model for predicting of mortality. Prospective study of SAH admitted to a Critical Care Department and Stroke Unit over a 2-year period. Middle region of pro-ADM plasma levels (MR-proADM) was measured in EDTA plasma within the first 24hours of hospital admission using the automatic immunofluorescence test. A regression tree was made to identify prognostic models for the development of mortality at 90days. Ninety patients were included. The mean MR-proADM plasma value in the samples analysed was 0.78±0.41nmol/L. MR-proADM plasma levels were significantly associated with mortality at 90days (1.05±0.51nmol/L vs 0.64±0.25nmol/L; P<.001). Regression tree analysis provided an algorithm based on the combined use of clinical variables and one biomarker allowing accurate mortality discrimination of three distinct subgroups with high risk of 90-day mortality ranged from 75% to 100% (AUC 0.9; 95% CI 0.83-0.98). The study established a model (APACHE II, MR-proADM and Hunt&Hess) to predict fatal outcomes in patients with SAH. The proposed decision-making algorithm may help identify patients with a high risk of mortality.
Published Version
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