An @entity8 : a predictor of @entity943 and mortality in XXXX with @entity296 and @entity712 .

@entity1 @entity8 @entity712 @entity296 @entity1165 @entity737 @entity177 @entity2224 @entity1011 @entity943
BACKGROUND: This study investigated the overall mortality and the incidence of @entity1165 ( @entity1165 ) in 99 @entity1 with @entity296 ( @entity296 ) and with an @entity737 ( @entity737 ) suffering from @entity712 . METHODS: We performed a stepwise regression model to identify independent risk factors for the occurrence of @entity943 . Using a Cox regression model, independent risk factors for total mortality were evaluated and, subsequently, a Kaplan-Meier analysis was applied. The primary endpoint of this study was the identification of independent predictors of overall mortality and the incidence of malignant @entity943 . RESULTS: One hundred twenty-five @entity1165 (>= 310 ms), 51 fast @entity1165 (between 310 ms and 240 ms), and 48 episodes of @entity2224 ( <=240 ms) were documented in 32 @entity1 . Independent predictors of @entity943 detected and treated by the @entity737 included female gender (odds ratio [OR] 3.4), lack of statin therapy (OR 3.5), and increased serum @entity1011 (OR 3.7). The Kaplan-Meier analysis showed no difference in survival between @entity1 with or without @entity1165 . Total mortality was predicted by increased age (OR 2.3) and an @entity8 (OR 1.9), independently. CONCLUSIONS: In this cohort of @entity296 @entity1 with @entity712 , female gender, lack of statin therapy, and increased @entity1011 represented independent risk factors for the incidence of malignant @entity943 . Furthermore, @entity177 and age favored total mortality. Considering these results, @entity8 might represent a valuable noninvasive tool to identify @entity296 @entity1 who, despite @entity737 implantation, have the highest risk of mortality and therefore require a particularly thorough follow-up.
