Predicting perioperative mortality after oesophagectomy: a systematic review of performance and methods of multivariate models.

Predicting risk of perioperative mortality after oesophagectomy for cancer may assist patients to make treatment choices and allow balanced comparison of providers. The aim of this systematic review of multivariate prediction models is to report their performance in new patients, and compare study methods against current recommendations. We used PRISMA guidelines and searched Medline, Embase, and standard texts from 1990 to 2012. Inclusion criteria were English language articles reporting development and validation of prediction models of perioperative mortality after open oesophagectomy. Two reviewers screened articles and extracted data for methods, results, and potential biases. We identified 11 development, 10 external validation, and two clinical impact studies. Overestimation of predicted mortality was common (5-200% error), discrimination was poor to moderate (area under receiver operator curves ranged from 0.58 to 0.78), and reporting of potential bias was poor. There were potentially important case mix differences between modelling and validation samples, and sample sizes were considerably smaller than is currently recommended. Steyerberg and colleagues' model used the most 'transportable' predictors and was validated in the largest sample. Most models have not been adequately validated and reported performance has been unsatisfactory. There is a need to clarify definition, effect size, and selection of currently available candidate predictors for inclusion in prediction models, and to identify new ones strongly associated with outcome. Adoption of prediction models into practice requires further development and validation in well-designed large sample prospective studies.