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  • Title: Predictors of mortality after aortic valve replacement.
    Author: Tjang YS, van Hees Y, Körfer R, Grobbee DE, van der Heijden GJ.
    Journal: Eur J Cardiothorac Surg; 2007 Sep; 32(3):469-74. PubMed ID: 17658266.
    Abstract:
    Aortic valve replacement (AVR) is recommended as a standard surgical procedure for aortic valve disease. Still the evidence for commonly claimed predictors of post-AVR prognosis, in particular mortality, appears scant. This systematic review reports on the evidence for predictors of post-AVR mortality, and may be helpful in pre-surgical risk-stratification. In PubMed, we searched for original reports of post-AVR follow-up studies. We assessed the quality of study design and methods with a standardized checklist. Data of the reported predictors of mortality and outcomes were extracted. Twenty-eight studies met our inclusion criteria. Sixteen studies were considered of high quality. There is strong evidence that the risk of early mortality is increased by emergency surgery, while the risk of late mortality is increased with older age and preoperative atrial fibrillation. There is moderate evidence that the risk of early mortality is increased by older age, aortic insufficiency, coronary artery disease, longer cardiopulmonary bypass time, reduced left ventricular ejection fraction (LV-EF), infective endocarditis, hypertension, mechanical valves, preoperative pacing, dialysis-dependent renal failure and valve size; and that the risk for late mortality is increased by emergency surgery and urgency of the operation. There is little evidence for high New York Heart Association class, concomitant coronary artery bypass graft and many other commonly claimed risk factors for post-AVR mortality. The reported evidence on predictors of post-AVR mortality will help for pre-surgical risk-stratification, i.e. to discern patients at high or low risk for early and late post-AVR mortality. Future prognostic studies should take the evidence from this review into account and should focus on derivation of a predictive model for post-AVR survival.
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