eprintid: 1544971
rev_number: 36
eprint_status: archive
userid: 608
dir: disk0/01/54/49/71
datestamp: 2017-03-14 15:35:50
lastmod: 2021-09-26 22:29:23
status_changed: 2017-03-14 15:35:50
type: article
metadata_visibility: show
creators_name: Wong, DJN
creators_name: Oliver, CM
creators_name: Moonesinghe, SR
title: Predicting Postoperative Morbidity in Adult Elective Surgical Patients using the Surgical Outcome Risk Tool (SORT)
ispublished: pub
divisions: UCL
divisions: B02
divisions: C10
divisions: D16
divisions: G88
keywords: Morbidity, Risk Assessment, Postoperative Complications
note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
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 (χ² = 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.
date: 2017-07
date_type: published
publisher: Oxford University Press (OUP): Policy B
official_url: https://doi.org/10.1093/bja/aex117
oa_status: green
full_text_type: other
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 1213832
doi: 10.1093/bja/aex117
lyricists_name: Moonesinghe, Suneetha
lyricists_name: Oliver, Charles
lyricists_name: Wong, Danny
lyricists_id: SRMOO13
lyricists_id: CMOLI32
lyricists_id: DJNWO18
actors_name: Wong, Danny
actors_id: DJNWO18
actors_role: owner
full_text_status: public
publication: British Journal of Anaesthesia
volume: 119
number: 1
pagerange: 95-105
issn: 1471-6771
citation:        Wong, DJN;    Oliver, CM;    Moonesinghe, SR;      (2017)    Predicting Postoperative Morbidity in Adult Elective Surgical Patients using the Surgical Outcome Risk Tool (SORT).                   British Journal of Anaesthesia , 119  (1)   pp. 95-105.    10.1093/bja/aex117 <https://doi.org/10.1093/bja%2Faex117>.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/1544971/1/Manuscript_accepted.pdf