Predicting risk in procedures for aortic stenosis: the next step forward.

Profiling risk in patients undergoing medical procedures serves many purposes [1]. First, it allows outcomes prediction for individual patients, so that both patient and caregiver can have betterinformed decision-making regarding the advisability and risks of undergoing a specific medical procedure. Secondly, patients undergoing medical procedures frequently have co-morbidities that cause varying levels of risk and therefore may adversely impact the outcomes of a procedure. When comparing different modalities of treatment or different caregivers, risk adjustment allows for a balanced analysis of outcomes by accounting for the risk-factor variation between different patient cohorts. This correction allows for a more ‘level playing field’ of outcomes assessment, and this ‘apples-to-apples’ comparison is one of the benefits of clinical outcomes databases over administrative databases that have limited ability to ‘risk adjust’. Risk adjustment, therefore, allows for a more meaningful analysis of hospitals or therapies for comparative safety and/or effectiveness of treatment. One could, for example, compare two standard procedures, e.g. coronary bypass surgery vs percutaneous coronary intervention, or a new procedure with an existing standard, transcatheter aortic valve implantation (TAVI) vs surgical aortic valve replacement (SAVR) or outcomes between different centres. Public reporting of surgical outcomes in the USA is done by risk-adjusted results in which the observed outcome divided by the expected outcome is based on known patient risk factors. Without the risk adjustment that takes into account these patient-specific factors that may adversely affect outcomes, meaningful comparison is not possible. LIMITATIONS OF RISK ALGORITHMS When employing risk adjustment, there are important limitations that have to be taken into account in order to obtain valid information and not misinformation from the ‘correction’ [2]. First, risk algorithms are accurate only for the population and in the timeframe in which they are developed and validated. Secondly, risk adjustment loses accuracy at the extremes of the population studied when there are too few patients upon whom to build a statistically valid model. This ‘tail of the bell-shaped curve’ is where high-risk patients with aortic stenosis reside, and this accounts for some of the overestimation of risk seen with many models [3, 4]. Thirdly, risk algorithms cannot reliably be directly applied to populations and treatments other than those in which they were developed. The implications being that although both TAVI and SAVR are used in treating patients with aortic stenosis, AVR risk algorithms are based on ‘surgical’ AVR outcomes and therefore may not to be directly applicable to TAVI. Fourthly, risk algorithms cannot account for variables not collected or analysed. This lack of accounting is due to either the fact that the occurrence of the factor or condition is so small that it’s impact cannot be measured, e.g. porcelain aorta, or that it was not previously known to be a factor that was causal or able to be accurately measured or quantified. The role of frailty and its impact on outcomes of treatment is a case in point. Fifthly, all risk predictors fall prey to the phenomenon of ‘garbage in equals garbage out’. Unless, the factors on which the algorithm is formulated are based on complete and accurate data, it is more likely that an inaccurate predictor will result. A corollary of this is that the risk predictor must be ‘user friendly’. The greater the number of variables collected in the formulation of the risk algorithm, the more accurate the prediction of risk; however, the more burdensome the collection of data required, then the less complete and accurate will be the information. So there needs to be a balance between including all information that is likely to be a factor in causing risk and ‘user-friendliness’ in order to facilitate complete and accurate collection and to assure that the tool is routinely employed in decision-making. Indeed, one risk algorithm for aortic stenosis, the ACEF score, provides reasonable prediction using only three factors, such as age, serum creatinine and ejection fraction [5].