Please use this identifier to cite or link to this item: https://doi.org/10.1136/bmjopen-2017-019427
Title: Development of the Combined Assessment of Risk Encountered in Surgery (CARES) surgical risk calculator for prediction of postsurgical mortality and need for intensive care unit admission risk: A single-center retrospective study
Authors: Chan, D.X.H.
Sim, Y.E.
Chan, Y.H. 
Poopalalingam, R.
Abdullah, H.R.
Keywords: icu stay
postoperative mortality
risk calculator
risk prediction
surgical risk
Issue Date: 2018
Publisher: BMJ Publishing Group
Citation: Chan, D.X.H., Sim, Y.E., Chan, Y.H., Poopalalingam, R., Abdullah, H.R. (2018). Development of the Combined Assessment of Risk Encountered in Surgery (CARES) surgical risk calculator for prediction of postsurgical mortality and need for intensive care unit admission risk: A single-center retrospective study. BMJ Open 8 (3) : e019427. ScholarBank@NUS Repository. https://doi.org/10.1136/bmjopen-2017-019427
Rights: Attribution-NonCommercial-NoDerivatives 4.0 International
Abstract: Introduction Accurate surgical risk prediction is paramount in clinical shared decision making. Existing risk calculators have limited value in local practice due to lack of validation, complexities and inclusion of non-routine variables. Objective We aim to develop a simple, locally derived and validated surgical risk calculator predicting 30-day postsurgical mortality and need for intensive care unit (ICU) stay (>24 hours) based on routinely collected preoperative variables. We postulate that accuracy of a clinical history-based scoring tool could be improved by including readily available investigations, such as haemoglobin level and red cell distribution width. Methodology Electronic medical records of 90 785 patients, who underwent non-cardiac and non-neuro surgery between 1 January 2012 and 31 October 2016 in Singapore General Hospital, were retrospectively analysed. Patient demographics, comorbidities, laboratory results, surgical priority and surgical risk were collected. Outcome measures were death within 30 days after surgery and ICU admission. After excluding patients with missing data, the final data set consisted of 79 914 cases, which was divided randomly into derivation (70%) and validation cohort (30%). Multivariable logistic regression analysis was used to construct a single model predicting both outcomes using Odds Ratio (OR) of the risk variables. The ORs were then assigned ranks, which were subsequently used to construct the calculator. Results Observed mortality was 0.6%. The Combined Assessment of Risk Encountered in Surgery (CARES) surgical risk calculator, consisting of nine variables, was constructed. The area under the receiver operating curve (AUROC) in the derivation and validation cohorts for mortality were 0.934 (0.917-0.950) and 0.934 (0.912-0.956), respectively, while the AUROC for ICU admission was 0.863 (0.848-0.878) and 0.837 (0.808-0.868), respectively. CARES also performed better than the American Society of Anaesthesiologists-Physical Status classification in terms of AUROC comparison. Conclusion The development of the CARES surgical risk calculator allows for a simplified yet accurate prediction of both postoperative mortality and need for ICU admission after surgery. © 2018 Article author(s).
Source Title: BMJ Open
URI: https://scholarbank.nus.edu.sg/handle/10635/209670
ISSN: 2044-6055
DOI: 10.1136/bmjopen-2017-019427
Rights: Attribution-NonCommercial-NoDerivatives 4.0 International
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