@article{discovery1544971,
       publisher = {Oxford University Press (OUP): Policy B},
           month = {July},
          volume = {119},
            note = {This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.},
           pages = {95--105},
           title = {Predicting Postoperative Morbidity in Adult Elective Surgical Patients using the Surgical Outcome Risk Tool (SORT)},
            year = {2017},
         journal = {British Journal of Anaesthesia},
          number = {1},
          author = {Wong, DJN and Oliver, CM and Moonesinghe, SR},
        abstract = {Background: The Surgical Outcome Risk Tool (SORT) is a risk stratification tool that predicts perioperative mortality. We construct a new recalibrated model based on SORT to predict the risk of developing 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 one 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 527 patients. SORT-morbidity was well-calibrated in the validation sample, as assessed using calibration plots and the Hosmer-Lemeshow Test ({\ensuremath{\chi}}2 = 4.87, p = 0.77). It showed acceptable discrimination by Receiver Operator Characteristic (ROC) curve analysis (Area Under the ROC curve, AUROC = 0.72, 95\% CI 0.67-0.77). This compared favourably with POSSUM (AUROC = 0.66, 95\% CI 0.60-0.71), while remaining 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 preoperatively, with clinical judgement, to predict postoperative morbidity risk in major elective surgery.},
             url = {https://doi.org/10.1093/bja/aex117},
        keywords = {Morbidity, Risk Assessment, Postoperative Complications},
            issn = {1471-6771}
}