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

Abstract

IntroductionAccurate 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.ObjectiveWe 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.MethodologyElectronic 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.ResultsObserved 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.ConclusionThe 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.

Estimating the risk of PTSD in recent trauma survivors: results of the International Consortium to Predict PTSD (ICPP)

“…Likelihood estimates, however, may be better suited for quantifying individual risk. As in other areas of medicine, quantifying risk ultimately informs clinical action.…”

“…In line with current medical risk assessment practices (e.g., in oncology, surgery or cardiology), we used the ICPP IPD to develop a prediction function that estimates the probability of PTSD given a set of early, observable risk indicators. Following replicated demonstrations of their predictive yield in classification models, we positioned PTSD symptoms as a key predictor, subsequently enriching the predictive models by including other previously documented and clinically‐obtainable risk indicators available in the ICPP dataset (e.g., gender, trauma type, lifetime trauma history).…”

Anemia and perioperative mortality in Non-cardiac Surgery patients: a secondary analysis based on a single-center retrospective study

“…The patients were followed up for 30 days after their index operation to identify all ICU admissions (stay time >24 hours), blood transfusion and mortality. Mortality data (the primary outcome) were synchronized with the National Electronic Health Records, ensuring a near complete follow-up [18] .…”

Estimated Glomerular Filtration Rate and Postoperative Mortality in Patients Undergoing Non-Cardiac Surgery: A Single-Center Retrospective Study.

“…We downloaded the raw data from the DATADRYAD database (www.datadryad.org). As Diana Xin Hui Chan, et al [20] have uploaded the original data and authorized the ownership to the website, we can perform secondary data analysis on this data to verify different scientific assumptions. (Dryad data package: Chan, Diana Xin Hui et al (2018), Data from: Development of the Combined Assessment of Risk Encountered in Surgery (CARES) surgical risk calculator for prediction of post-surgical mortality and need for intensive care unit admission risk -a single-center retrospective study, Dryad, Dataset, https://doi.org/10.5061/dryad.v142481).…”

“…It is important to note that Chan, Diana Xin Hui et al [20]completed data collection. They conducted a singlecenter retrospective study at Singapore General Hospital, a 1,700-bed tertiary academic hospital [20].…”