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.
- surgical risk
- postoperative mortality
- icu stay
- risk prediction
- risk calculator
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Contributors DXHC: data analysis and interpretation, prepared draft manuscript, revised draft manuscript and approved final manuscript for submission. YES: data acquisition and curation, data analysis and interpretation, revised draft manuscript and approved final manuscript for submission. YHC: data analysis and interpretation, revised draft manuscript and approved final manuscript for submission. RP: funding acquisition and approved final manuscript for submission. HRA: designed and conceptualised study, funding acquisition, data acquisition and curation, data analysis and interpretation, revised draft manuscript and approved final manuscript for submission. All the authors agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.
Funding This work was supported by the department fund of Department of Anaesthesiology, Singapore General Hospital. The funding sources had no role in the design of this study and the analysis and interpretation of the results.
Disclaimer The authors affirm that this manuscript is an honest, accurate and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned have been explained.
Competing interests HRA is a recipient of SingHealth Duke-NUS Nurturing Clinician Scientists Scheme Award, project number 12/FY2017/P1/15-A29.
Patient consent Not required.
Ethics approval This study has been approved by the SingHealth Centralised Institutional Review Board (CIRB).
Provenance and peer review Not commissioned; externally peer reviewed.
Data sharing statement Extra data can be accessed via the Dryad data repository at http://datadryad.org/ with the doi: 10.5061/dryad.v142481.
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