Dataset Information

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:

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.

SUBMITTER: Chan DXH

PROVIDER: S-EPMC5875658 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature

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Publications

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.

Chan Diana Xin Hui DXH Sim Yilin Eileen YE Chan Yiong Huak YH Poopalalingam Ruban R Abdullah Hairil Rizal HR

BMJ open 20180323 3

<h4>Introduction</h4>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.<h4>Objective</h4>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 ...[more]

PMID: 29574442

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Project description:ObjectiveTo compare the performance of machine learning models against the traditionally derived Combined Assessment of Risk Encountered in Surgery (CARES) model and the American Society of Anaesthesiologists-Physical Status (ASA-PS) in the prediction of 30-day postsurgical mortality and need for intensive care unit (ICU) stay >24 hours.BackgroundPrediction of surgical risk preoperatively is important for clinical shared decision-making and planning of health resources such as ICU beds. The current growth of electronic medical records coupled with machine learning presents an opportunity to improve the performance of established risk models.MethodsAll patients aged 18 years and above who underwent noncardiac and nonneurological surgery at Singapore General Hospital (SGH) between 1 January 2012 and 31 October 2016 were included. Patient demographics, comorbidities, preoperative laboratory results, and surgery details were obtained from their electronic medical records. Seventy percent of the observations were randomly selected for training, leaving 30% for testing. Baseline models were CARES and ASA-PS. Candidate models were trained using random forest, adaptive boosting, gradient boosting, and support vector machine. Models were evaluated on area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC).ResultsA total of 90,785 patients were included, of whom 539 (0.6%) died within 30 days and 1264 (1.4%) required ICU admission >24 hours postoperatively. Baseline models achieved high AUROCs despite poor sensitivities by predicting all negative in a predominantly negative dataset. Gradient boosting was the best performing model with AUPRCs of 0.23 and 0.38 for mortality and ICU admission outcomes respectively.ConclusionsMachine learning can be used to improve surgical risk prediction compared to traditional risk calculators. AUPRC should be used to evaluate model predictive performance instead of AUROC when the dataset is imbalanced.

Project description:Background and aimsRisk factors for poor outcomes after critical illness are incompletely understood. While nutritional risk is associated with mortality in critically ill patients, its association with disability, cognitive, and health-related quality of life is unclear in survivors of critical illness. This study's objective was to determine whether greater nutritional risk at ICU admission is associated with greater disability, worse cognition, and worse HRQOL at 3 and 12-month follow-up.MethodsWe enrolled adults (≥18 years of age) with respiratory failure or shock treated in medical and surgical intensive care units from two U.S. centers. We measured nutritional risk using the modified Nutrition Risk in Critically Ill (mNUTRIC) score (range 0-9 [highest risk]) at intensive care unit admission. We measured associations between mNUTRIC scores and discharge destination, disability in basic activities of daily living (ADLs) using the Katz ADL, instrumental ADLs using the Functional Activities Questionnaire (FAQ), global cognition using the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS), executive function using the Trail Making Test Part B (Trails B), and health-related quality of life using the SF-36, adjusting for sex, education, BMI, baseline frailty, disability, and cognition, severity of illness, days of delirium, coma, and mechanical ventilation.ResultsOf the 821 patients enrolled in the ICU, 636 patients survived to hospital discharge. We assessed outcomes in 448 of 535 survivors (84%) at 3 months and 382 of 476 survivors (80%) at 12 months. Higher mNUTRIC scores predicted greater odds of discharge to an institution (OR 2.0, 95% CI: 1.6 to 2.6; P < 0.01). Higher mNUTRIC scores were associated with a trend towards greater disability in basic activities of daily living (IRR 1.3, 95% CI 1.0 to 1.7) at 3 months that did not reach significance (p = 0.09) with no association demonstrated at 12 months. There were no associations between mNUTRIC scores and FAQ, RBANS, or Trails B scores. mNUTRIC scores were inconsistently associated with SF-36 physical and mental component scale scores.ConclusionsGreater nutritional risk at ICU admission is associated with disability in survivors of critical illness. Future studies should evaluate interventions in those at high nutritional risk as a means to speed recovery.

Project description:Background To develop a highly discriminative machine learning model for the prediction of intensive care unit admission (>24h) using the easily available preoperative information from electronic health records. An accurate prediction model for ICU admission after surgery is of great importance for surgical risk assessment and appropriate utilization of ICU resources. Method Data were collected retrospectively from a large hospital, comprising 135,442 adult patients who underwent surgery except for cardiac surgery between 1 January 2014, and 31 July 2018 in China. Multiple existing predictive machine learning algorithms were explored to construct the prediction model, including logistic regression, random forest, adaptive boosting, and gradient boosting machine. Four secondary analyses were conducted to improve the interpretability of the results. Results A total of 2702 (2.0%) patients were admitted to the intensive care unit postoperatively. The gradient boosting machine model attained the highest area under the receiver operating characteristic curve of 0.90. The machine learning models predicted intensive care unit admission better than the American Society of Anesthesiologists Physical Status (area under the receiver operating characteristic curve: 0.68). The gradient boosting machine recognized several features as highly significant predictors for postoperatively intensive care unit admission. By applying subgroup analysis and secondary analysis, we found that patients with operations on the digestive, respiratory, and vascular systems had higher probabilities for intensive care unit admission. Conclusion Compared with conventional American Society of Anesthesiologists Physical Status and logistic regression model, the gradient boosting machine could improve the performance in the prediction of intensive care unit admission. Machine learning models could be used to improve the discrimination and identify the need for intensive care unit admission after surgery in elective noncardiac surgical patients, which could help manage the surgical risk.

Dataset Information

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.

Introduction

Objective

Methodology

Results

Conclusion

Publications

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.

Similar Datasets

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