Abstract
Background and objectiveThe CHA2DS2-VASc score is a stroke risk stratification tool that is used in patients with atrial fibrillation (AF). Most of its clinical variables have been associated with poor outcomes in patients with infective endocarditis (IE). In this study, we aimed to determine its utility in predicting outcomes in IE patients.MethodsWe included 35,570 patients with IE from the National Inpatient Sample (NIS), 2009-2012. The CHA2DS2-VASc score was calculated for each patient. Hierarchical logistic regression was used to estimate the adjusted odds ratio for in-hospital mortality for CHA2DS2-VASc scores from 1 to 9, using a score of 0 as the reference score. All clinical characteristics were defined using the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes.ResultsThe mean age of the sample was 57.81 ±14 years. Higher CHA2DS2-VASc scores were associated with increased mortality, and the scores among the sample ranged from 0 for 8.1% to 8 for 21.7%. In the hierarchical logistic regression, after adjusting for age, sex, and relevant comorbidities, as the score increased, so did the odds for overall mortality.ConclusionIn patients with IE, the CHA2DS2-VASc score may serve as a risk assessment tool with which to predict outcomes. Further studies are needed to replicate these findings.
Highlights
Infective endocarditis (IE) is a life-threatening infection of the valvular or paravalvular heart structures; it has an incidence rate of approximately 100 cases per one million patient-years [1,2]
The CHA2DS2-VASc score is a stroke risk stratification tool that is used in patients with atrial fibrillation (AF)
Most of its clinical variables have been associated with poor outcomes in patients with infective endocarditis (IE)
Summary
Infective endocarditis (IE) is a life-threatening infection of the valvular or paravalvular heart structures; it has an incidence rate of approximately 100 cases per one million patient-years [1,2]. It is a relatively uncommon disease, the in-hospital mortality rate associated with it can be as high as 20% [3]. One of the significant challenges associated with IE management is appropriately identifying and stratifying patients based on the increased risk of complications [2]. A scoring system that predicts complications may help with early risk stratification and determine who will benefit from further interventions. We aimed to determine its utility in predicting outcomes in IE patients
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