The risk of stroke and death in patients with aortic and mitral valve endocarditis

Citation
Ch. Cabell et al., The risk of stroke and death in patients with aortic and mitral valve endocarditis, AM HEART J, 142(1), 2001, pp. 75-80
Citations number
28
Categorie Soggetti
Cardiovascular & Respiratory Systems","Cardiovascular & Hematology Research
Journal title
AMERICAN HEART JOURNAL
ISSN journal
00028703 → ACNP
Volume
142
Issue
1
Year of publication
2001
Pages
75 - 80
Database
ISI
SICI code
0002-8703(200107)142:1<75:TROSAD>2.0.ZU;2-J
Abstract
Background Previous studies have generated inconsistent results when attemp ting to define predictors of stroke and death in patients with endocarditis . We sought to examine the relationship between vegetation 2-dimensional si ze and stroke in those with infective endocarditis (IE) and to identify dif ferences between aortic valve (AV) and mitral valve (MV) IE with regard to clinical characteristics, echocardiographic findings, stroke, and death. Methods We used the Duke Endocarditis Database to examine 145 episodes of d efinite IE involving the AV, n = 62, or MV, n = 83, A logistic regression m odel was developed to analyze important variables in predicting stroke, and a Cox proportional hazards model was used in predicting mortality. Results The mitral valve was infected in 57% of the cases. Vegetations were more commonly detected in patients with MV IE (92.8% vs 66. 1%, P =.001) a nd these MV vegetations were significantly larger (P < .05). Thirty-four of 145 episodes (23.4%) were complicated by stroke. MV IE was associated with a greater stroke rate, 32.5% versus 11.3% (P =.003). Strokes tended to occ ur early in the course of illness, particularly in MV IE, In the multivaria ble model, the independent predictors of stroke were MV IE (P =.04) and veg etation length (P =.03). Independent predictors of 1-year mortality were ag e (P =.02) and vegetation area (P =.048). Conclusion stroke is more common in patients with MV IE. Vegetation 2-dimen sional size and characteristics are important predictors of stroke and mort ality. These findings may lead to predictive models that allow physicians t o identify high-risk patients who need aggressive treatment strategies to p revent long-term morbidity and mortality.