Predictors of In-Hospital Death After Aneurysmal Subarachnoid Hemorrhage: Analysis of a Nationwide Database (Swiss SOS [Swiss Study on Aneurysmal Subarachnoid Hemorrhage])
BACKGROUND AND PURPOSE To identify predictors of in-hospital mortality in patients with aneurysmal subarachnoid hemorrhage and to estimate their impact. METHODS Retrospective analysis of prospective data from a nationwide multicenter registry on all aneurysmal subarachnoid hemorrhage cases admitted to a tertiary neurosurgical department in Switzerland (Swiss SOS [Swiss Study on Aneurysmal Subarachnoid Hemorrhage]; 2009-2015). Both clinical and radiological independent predictors of in-hospital mortality were identified, and their effect size was determined by calculating adjusted odds ratios (aORs) using multivariate logistic regression. Survival was displayed using Kaplan-Meier curves. RESULTS Data of n=1866 aneurysmal subarachnoid hemorrhage patients in the Swiss SOS database were available. In-hospital mortality was 20% (n=373). In n=197 patients (10.6%), active treatment was discontinued after hospital admission (no aneurysm occlusion attempted), and this cohort was excluded from analysis of the main statistical model. In the remaining n=1669 patients, the rate of in-hospital mortality was 13.9% (n=232). Strong independent predictors of in-hospital mortality were rebleeding (aOR, 7.69; 95% confidence interval, 3.00-19.71; P<0.001), cerebral infarction attributable to delayed cerebral ischemia (aOR, 3.66; 95% confidence interval, 1.94-6.89; P<0.001), intraventricular hemorrhage (aOR, 2.65; 95% confidence interval, 1.38-5.09; P=0.003), and new infarction post-treatment (aOR, 2.57; 95% confidence interval, 1.43-4.62; P=0.002). CONCLUSIONS Several-and among them modifiable-factors seem to be associated with in-hospital mortality after aneurysmal subarachnoid hemorrhage. Our data suggest that strategies aiming to reduce the risk of rebleeding are most promising in patients where active treatment is initially pursued. CLINICAL TRIAL REGISTRATION URL: http://www.clinicaltrials.gov. Unique identifier: NCT03245866.
Abstract
BACKGROUND AND PURPOSE To identify predictors of in-hospital mortality in patients with aneurysmal subarachnoid hemorrhage and to estimate their impact. METHODS Retrospective analysis of prospective data from a nationwide multicenter registry on all aneurysmal subarachnoid hemorrhage cases admitted to a tertiary neurosurgical department in Switzerland (Swiss SOS [Swiss Study on Aneurysmal Subarachnoid Hemorrhage]; 2009-2015). Both clinical and radiological independent predictors of in-hospital mortality were identified, and their effect size was determined by calculating adjusted odds ratios (aORs) using multivariate logistic regression. Survival was displayed using Kaplan-Meier curves. RESULTS Data of n=1866 aneurysmal subarachnoid hemorrhage patients in the Swiss SOS database were available. In-hospital mortality was 20% (n=373). In n=197 patients (10.6%), active treatment was discontinued after hospital admission (no aneurysm occlusion attempted), and this cohort was excluded from analysis of the main statistical model. In the remaining n=1669 patients, the rate of in-hospital mortality was 13.9% (n=232). Strong independent predictors of in-hospital mortality were rebleeding (aOR, 7.69; 95% confidence interval, 3.00-19.71; P<0.001), cerebral infarction attributable to delayed cerebral ischemia (aOR, 3.66; 95% confidence interval, 1.94-6.89; P<0.001), intraventricular hemorrhage (aOR, 2.65; 95% confidence interval, 1.38-5.09; P=0.003), and new infarction post-treatment (aOR, 2.57; 95% confidence interval, 1.43-4.62; P=0.002). CONCLUSIONS Several-and among them modifiable-factors seem to be associated with in-hospital mortality after aneurysmal subarachnoid hemorrhage. Our data suggest that strategies aiming to reduce the risk of rebleeding are most promising in patients where active treatment is initially pursued. CLINICAL TRIAL REGISTRATION URL: http://www.clinicaltrials.gov. Unique identifier: NCT03245866.
TrendTerms displays relevant terms of the abstract of this publication and related documents on a map. The terms and their relations were extracted from ZORA using word statistics. Their timelines are taken from ZORA as well. The bubble size of a term is proportional to the number of documents where the term occurs. Red, orange, yellow and green colors are used for terms that occur in the current document; red indicates high interlinkedness of a term with other terms, orange, yellow and green decreasing interlinkedness. Blue is used for terms that have a relation with the terms in this document, but occur in other documents.
You can navigate and zoom the map. Mouse-hovering a term displays its timeline, clicking it yields the associated documents.
Stienen, Martin Nikolaus