Predicting postoperative morbidity in adult elective surgical patients using the Surgical Outcome Risk Tool (SORT)

Abstract

Background. The Surgical Outcome Risk Tool (SORT) is a risk stratification instrument used to predict perioperative mortality. We wanted to evaluate and refine SORT for better prediction of the risk of postoperative morbidity.Methods. We analysed prospectively collected data from a single-centre cohort of adult patients undergoing major elective surgery. The data set was split randomly into derivation and validation samples. We used logistic regression to construct a model in the derivation sample to predict postoperative morbidity as defined using the validated Postoperative Morbidity Survey (POMS) assessed at 1 week after surgery. Performance of this 'SORT-morbidity' model was then tested in the validation sample and compared against the Physiological and Operative Severity Score for the enUmeration of Mortality and morbidity (POSSUM).Results. The SORT-morbidity model was constructed using a derivation sample of 1056 patients and validated in a further 527 patients. SORT-morbidity was well calibrated in the validation sample, as assessed using calibration plots and the Hosmer–Lemeshow test (χ²=4.87, P=0.77). It showed acceptable discrimination by receiver operating characteristic curve analysis [area under the receiver operating characteristic curve (AUROC)=0.72, 95% confidence interval: 0.67–0.77]. This compared favourably with POSSUM (AUROC=0.66, 95% confidence interval: 0.60–0.71), whilst being simpler to use. Linear shrinkage factors were estimated, which allow the SORT-morbidity model to predict a range of alternative morbidity outcomes with greater accuracy, including low- and high-grade morbidity, and POMS at later time points.Conclusions. SORT-morbidity can be used before surgery, with clinical judgement, to predict postoperative morbidity risk in major elective surgery.

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