Impact of adrenomedullin levels on clinical risk stratification and outcome in subarachnoid hemorrhage.

PURPOSE To use classification tree analysis to identify risk factors for non-survival in a neurological patients with subarachnoid hemorrhage (SAH) and to propose a clinical model for predicting of mortality. METHODS Prospective study of SAH admitted to a Critical Care Department and Stroke Unit over a two-year period. Middle region of Pro-ADM plasma levels (MR-proADM) were measured in EDTA plasma within the first 24 hours of hospital admission using the automatic immunofluorescence test. A regression tree was made to identify prognostic models for the development of mortality at 90 days. RESULTS Ninety patients were included. The mean MR-proADM plasma value in the samples analyzed was 0,78 ± 0,41nmol/l. MR-proADM plasma levels were significantly associated with mortality at 90 days (1.05 ± 0.51 nmol/L vs 0.64 ± 0.25 nmol/L; p< 0.001). 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%IC 0,83-0,98). CONCLUSIONS 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.