Editorial Comment: EuroSCORE II and the art and science of risk modelling

Abstract

Chalmers et al. [4] applied the model to a 5500-patient cohort, concluded that EuroSCORE II is globally better calibrated and found better overall discrimination with a C-statistic of 0.79 (old model 0.77), with best performance in mitral (0.87) and coronary (0.79) surgery and weakest in isolated aortic valve replacement (0.69), marginally better than the old model (0.67). Pooling contemporaneous multi-institutional data provides optimal model validation. A single institution performing exceptionally in one type of surgery may perceive a lack of fit in the entire cohort, as Chalmers indeed found (Hosmer–Lemeshow P-value <0.05) for EuroSCORE II overall, but not in any subdivision. Considering this and other limitations of a single-institution study, the model has achieved its objective of better calibration and discrimination in global cardiac surgery. EuroSCORE II riskstratifies using factors including operation type. Lower discrimination when these are neutralized is therefore unsurprising. We advise caution in applying the model to narrow patient subsets. In the editorials [2, 3], many comments reiterate issues already addressed in the discussion section of the original paper [1]. As Sergeant states, EuroSCORE is widely used and has surgeons’ confidence. There has been misuse, particularly in evaluating patients for transcatheter aortic valve implantation (TAVI), and in predicting non-mortality events. The former is avoidable by our recommendation for using risk-adjusted mortality ratios (RAMR) [1], but that is not enough. Conventional surgery risk factors differ from those of TAVI, and we shall explore our TAVI data to illuminate this. Predicting non-mortality events using EuroSCORE has often succeeded, but its purpose remains to evaluate expeditiously the risk of death. SELECTING AND HANDLING RISK VARIABLES