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

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 (χ 2 =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.