TY - JOUR
T1 - Impact of adrenomedullin levels on clinical risk stratification and outcome in subarachnoid haemorrhage
AU - Gracia Arnillas, Maria Pilar
AU - Alvarez-Lerma, Francisco
AU - Masclans, Jose Ramón
AU - Roquer, Jaume
AU - Soriano, Carolina
AU - Manzano, Demian
AU - Zapatero, Ans
AU - Diaz, Yolanda
AU - Duran, Xavi
AU - Castellví, Andrea
AU - Cuadrado, Elisa
AU - Ois, Angel
N1 - Funding Information:
This study was partially supported by Thermo Fisher Scientific and FIS grant from Instituto de Salud Carlos III‐FEDER Pi 14/01420.
Publisher Copyright:
© 2020 Stichting European Society for Clinical Investigation Journal Foundation. Published by John Wiley & Sons Ltd
PY - 2020/11/1
Y1 - 2020/11/1
N2 - Purpose: 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. Methods: 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 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 analysed was 0.78 ± 0.41 nmol/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 <.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). 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.
AB - Purpose: 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. Methods: 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 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 analysed was 0.78 ± 0.41 nmol/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 <.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). 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.
KW - adrenomedullin
KW - critical care department
KW - mortality
KW - neurocritical patient
KW - subarachnoid haemorrhage
UR - http://www.scopus.com/inward/record.url?scp=85087796100&partnerID=8YFLogxK
U2 - 10.1111/eci.13318
DO - 10.1111/eci.13318
M3 - Article
C2 - 32535893
AN - SCOPUS:85087796100
VL - 50
JO - European Journal of Clinical Investigation
JF - European Journal of Clinical Investigation
SN - 0014-2972
IS - 11
M1 - e13318
ER -