TY  - JOUR
AU  - Hernandez-Vaquero, Daniel
AU  - Díaz, Rocío
AU  - Pascual, Isaac
AU  - Álvarez, Rubén
AU  - Alperi, Alberto
AU  - Rozado, Jose
AU  - Morales, Carlos
AU  - Silva, Jacobo
AU  - Morís, César
PY  - 2017
TI  - Predictive risk models for proximal aortic surgery
JF  - Journal of Thoracic Disease; Vol 9, Supplement 6 (May 26, 2017): Journal of Thoracic Disease (Aortic Diseases)
Y2  - 2017
KW  - 
N2  - 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.
UR  - https://jtd.amegroups.org/article/view/12424