Article Abstract

Predictive risk models for proximal aortic surgery

Authors: Daniel Hernandez-Vaquero, Rocío Díaz, Isaac Pascual, Rubén Álvarez, Alberto Alperi, Jose Rozado, Carlos Morales, Jacobo Silva, César Morís

Abstract

Predictive risk models help improve decision making, information to our patients and quality control comparing results between surgeons and between institutions. The use of these models promotes competitiveness and led to increasingly better results. All these virtues are of utmost importance when the surgical operation entails high-risk. Although proximal aortic surgery is less frequent than other cardiac surgery operations, this procedure itself is more challenging and technically demanding than other common cardiac surgery techniques. The aim of this study is to review the current status of predictive risk models for patients who undergo proximal aortic surgery, which means aortic root replacement, supracoronary ascending aortic replacement or aortic arch surgery.


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