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
© Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2018 |
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| Erschienen: |
2018 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:8 |
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| Enthalten in: |
BMJ open - 8(2018), 3 vom: 23. März, Seite e019427 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Chan, Diana Xin Hui [VerfasserIn] |
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| Links: |
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| Themen: |
Icu stay |
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| Anmerkungen: |
Date Completed 13.09.2018 Date Revised 27.03.2024 published: Electronic Dryad: 10.5061/dryad.v142481 Citation Status MEDLINE |
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| doi: |
10.1136/bmjopen-2017-019427 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM282298320 |
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| 100 | 1 | |a Chan, Diana Xin Hui |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a 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 |b a single-center retrospective study |
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| 500 | |a Date Revised 27.03.2024 | ||
| 500 | |a published: Electronic | ||
| 500 | |a Dryad: 10.5061/dryad.v142481 | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted. | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a Validation Study | |
| 650 | 4 | |a icu stay | |
| 650 | 4 | |a postoperative mortality | |
| 650 | 4 | |a risk calculator | |
| 650 | 4 | |a risk prediction | |
| 650 | 4 | |a surgical risk | |
| 700 | 1 | |a Sim, Yilin Eileen |e verfasserin |4 aut | |
| 700 | 1 | |a Chan, Yiong Huak |e verfasserin |4 aut | |
| 700 | 1 | |a Poopalalingam, Ruban |e verfasserin |4 aut | |
| 700 | 1 | |a Abdullah, Hairil Rizal |e verfasserin |4 aut | |
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