Aortic valve replacement for aortic stenosis in patients with left ventricular dysfunction.

BACKGROUND: The purpose of this study was to assess the impact of left ventricular dysfunction and other risk factors on short- and mid-term outcomes after aortic valve replacement for aortic stenosis.

METHODS: From January 1, 2002, to December 31, 2007, 773 consecutive patients underwent primary aortic valve replacement for aortic stenosis at a single institution; concomitant coronary artery bypass graft surgery (CABG) was performed in 45.4% (351 of 773). Multivariable regression analysis was used to identify predictors of in-hospital mortality, with ejection fraction (EF) as the primary variable of interest. After discharge, survival status was determined using the Social Security Death Index. A Cox proportional hazards regression model was used to identify predictors of mid-term mortality.

RESULTS: On univariable analysis, EF (odds ratio [OR] 0.979, 95% confidence interval [CI]: 0.960 to 0.999, p = 0.044) but not concomitant CABG emerged as a predictor of in-hospital mortality. However, on multivariable analysis, neither EF nor concomitant CABG was associated with increased in-hospital mortality. Multivariable predictors of in-hospital mortality included age, emergent status, and prolonged bypass time. On univariable analysis, mid-term mortality was associated with EF and concomitant CABG (OR 0.979, 95% CI: 0.966 to 0.991, p = 0.001, and OR 1.61, 95% CI: 1.11 to 2.36, p = 0.013, respectively). However, after multivariable adjustment, only EF was associated with mid-term mortality (adjusted OR 0.985, 95% CI: 0.970 to 1.00, p = 0.049). Other multivariable predictors of mid-term mortality included age, dialysis-dependent renal failure, previous stroke, and peripheral vascular disease.

CONCLUSIONS: Left ventricular dysfunction, in addition to other patient comorbidities, may negatively impact survival after aortic valve replacement. Careful consideration of the cumulative effect of these multiple risk factors is necessary to optimize patient outcomes.