Risk Prediction Models for Management of Patients following Acute Aortic Dissection.

Abstract

Risk prediction of adverse outcomes post aortic dissection is dependet not only on the postdissection-associated clinical factors but on the very foundation of the risk factors that lead up to the dissection itself. There are various such risk factors existing prior to the dissection which impact the postdissection outcomes. In this paper, we review the literature to critically analyze various risk models, burdened by their significant limitations, that attempt to stratify risk prediction based on postdissection patient characteristics. We further review several studies across the literature that investigate the diverse set of predissection risk factors impacting postdissection outcomes. We have discussed and appraised numerous studies which attempt to develop a tool to stratify risk prediction by incorporating the impacts of different factors: malperfusion, blood biochemistry, and perioperative outcomes. The well-validated Penn classification has clearly demonstrated in the literature the significant impact that malperfusion has on adverse outcomes postdissection. Other risk models, already severely hindered by their limitations, lack such validation. We further discuss additional alluded risk factors, including the impact of predissection aortic size, the syndromic and nonsyndromic natures of dissection, and the effects of family history and genetics, which collectively contribute to the risk of adverse outcomes postdissection and prognosis. To achieve the goal of a true risk model, there remains the vital need for appreciation and appropriate consideration for all such aforementioned factors, from before and after the dissection, as discussed in this paper. By being able to incorporate the value of true risk prediction for a patient into the decision-making framework, it will allow a new page of precision medical decision-making to be written.