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The impact of previous health on the mortality after aneurysmal subarachnoid hemorrhage: analysis of a prospective Swedish multicenter study

PURPOSE: There is an an increasing awareness of the importance of health and lifestyle for stroke diseases like spontaneous subarachnoid hemorrhage (SAH). However, the importance of pre-existing medical conditions for clinical course and mortality after SAH has not been studied. The aim of the prese...

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Detalles Bibliográficos
Autores principales: Ronne Engström, Elisabeth, Baldvinsdóttir, Bryndís, Aineskog, Helena, Alpkvist, Peter, Enblad, Per, Eneling, Johanna, Fridriksson, Steen, Hillman, Jan, Klurfan, Paula, Kronvall, Erik, Lindvall, Peter, Von Vogelsang, Ann-Christin, Nilsson, Ola G., Svensson, Mikael
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Vienna 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9922219/
https://www.ncbi.nlm.nih.gov/pubmed/36633685
http://dx.doi.org/10.1007/s00701-022-05464-8
Descripción
Sumario:PURPOSE: There is an an increasing awareness of the importance of health and lifestyle for stroke diseases like spontaneous subarachnoid hemorrhage (SAH). However, the importance of pre-existing medical conditions for clinical course and mortality after SAH has not been studied. The aim of the present study was to identify pre-existing conditions contributing to mortality after SAH. METHODS: Data were extracted from a Swedish national prospective study on patients with SAH. Variables were defined for age, sex, body mass index (BMI), clinical condition at admission, and for 10 pre-existing medical conditions. Models predicting mortality in three time intervals with all possible subsets of these variables were generated, compared and selected using Akaike’s information criterion. RESULTS: 1155 patients with ruptured aneurysms were included. The mortality within 1 week was 7.6%, 1 month 14.3%, and 1 year 18.7%. The most common pre-existing medical conditions were smoking (57.6%) and hypertension (38.7%). The model’s best predicting mortality within 1 week and from 1 week to 1 month included only the level of consciousness at admission and age, and these two variables were present in all the models among the top 200 in Akaike score for each time period. The most predictive model for mortality between 1 month and 1 year added previous stroke, diabetes, psychiatric disease, and BMI as predictors. CONCLUSION: Mortality within the first month was best predicted simply by initial level of consciousness and age, while mortality within from 1 month to 1 year was significantly influenced by pre-existing medical conditions.