Risk assessment methods for cardiac surgery and intervention

Surgical risk models estimate operative outcomes while controlling for heterogeneity in 'case mix' within and between institutions. In cardiac surgery, risk models are used for patient counselling, surgical decision-making, clinical research, quality assurance and improvement, and financial reimbursement. Importantly, risk models are only as good as the databases from which they are derived; physicians and investigators should, therefore, be aware of shortcomings of clinical and administrative databases used for modelling risk estimates. The most frequently modelled outcome in cardiac surgery is 30-day mortality. However, results of randomized trials to compare conventional surgery versus transcatheter aortic valve implantation (TAVI) indicate attrition of surgical patients at 2-4 months postoperatively, suggesting that 3-month survival or mortality might be an appropriate procedural end point worth modelling. Risk models are increasingly used to identify patients who might be better-suited for TAVI. However, the appropriateness of available statistical models in this application is controversial, particularly given the tendency of risk models to misestimate operative mortality in high-risk patient subsets. Incorporation of new risk factors (such as previous mediastinal radiation, liver failure, and frailty) in future surgical or interventional risk-prediction tools might enhance model performance, and thereby optimize patient selection for TAVI.