D J N Wong1,2,3, C M Oliver1,2, S R Moonesinghe1,2,3. 1. UCL/UCLH Surgical Outcome Research Centre (SOuRCe), 3rd Floor, Maple Link Corridor, University College Hospital, 235 Euston Road, London NW1 2BU, UK. 2. National Institute of Academic Anaesthesia Health Services Research Centre (NIAA HSRC), Royal College of Anaesthetists, Churchill House, 35 Red Lion Square, London WC1R 4SG, UK. 3. Department of Applied Health Research (DAHR), University College London, 1-19 Torrington Place, London WC1E 7HB, UK.
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 (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.
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.
Authors: Joshua A Bloomstone; Benjamin T Houseman; Evora Vicents Sande; Ann Brantley; Jessica Curran; Gerald A Maccioli; Tania Haddad; James Steinshouer; David Walker; Ramani Moonesinghe Journal: Perioper Med (Lond) Date: 2020-09-21
Authors: Babak Saravi; Frank Hassel; Sara Ülkümen; Alisia Zink; Veronika Shavlokhova; Sebastien Couillard-Despres; Martin Boeker; Peter Obid; Gernot Michael Lang Journal: J Pers Med Date: 2022-03-22
Authors: T Reeves; S Bates; T Sharp; K Richardson; S Bali; J Plumb; H Anderson; J Prentis; M Swart; D Z H Levett Journal: Perioper Med (Lond) Date: 2018-01-26
Authors: S Ramani Moonesinghe; Dermot McGuckin; Peter Martin; James Bedford; Duncan Wagstaff; David Gilhooly; Cristel Santos; Jonathan Wilson; Jenny Dorey; Irene Leeman; Helena Smith; Cecilia Vindrola-Padros; Kylie Edwards; Georgina Singleton; Michael Swart; Rachel Baumber; Arun Sahni; Samantha Warnakulasuriya; Ravi Vohra; Helen Ellicott; Anne-Marie Bougeard; Maria Chazapis; Aleksandra Ignacka; Martin Cripps; Alexandra Brent; Sharon Drake; James Goodwin; Dorian Martinez; Karen Williams; Pritam Singh; Matthew Bedford; Abigail E Vallance; Katie Samuel; Jose Lourtie; Dominic Olive; Christine Taylor; Olga Tucker; Giuseppe Aresu; Andrew Swift; Naomi Fulop; Mike Grocott Journal: Perioper Med (Lond) Date: 2022-08-09
Authors: Jun Ho Lee; Minjong Ki; Seungseo Choi; Cheol Jong Woo; Deokkyu Kim; Hyungsun Lim; Dong-Chan Kim Journal: Korean J Anesthesiol Date: 2020-10-30