Real-time estimated sequential organ failure assessment (SOFA) score with intervals: Improved risk monitoring with estimated uncertainty in health condition for patients in intensive care units

Purpose: Real-time risk monitoring is critical but challenging in intensive care units (ICUs) due to the lack of real-time updates for most clinical variables. Although real-time predictions have been integrated into various risk-scoring systems to aid monitoring, existing systems do not address unc...

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Main Authors: HE, Yan, LUO, Qian, WANG, Hai, ZHENG, Zhichao, LUO, Haidong, OOI, Oon Cheong
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Language:English
Published: Institutional Knowledge at Singapore Management University 2024
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Online Access:https://ink.library.smu.edu.sg/lkcsb_research/7639
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spelling sg-smu-ink.lkcsb_research-86382025-01-02T08:03:58Z Real-time estimated sequential organ failure assessment (SOFA) score with intervals: Improved risk monitoring with estimated uncertainty in health condition for patients in intensive care units HE, Yan LUO, Qian WANG, Hai ZHENG, Zhichao LUO, Haidong OOI, Oon Cheong Purpose: Real-time risk monitoring is critical but challenging in intensive care units (ICUs) due to the lack of real-time updates for most clinical variables. Although real-time predictions have been integrated into various risk-scoring systems to aid monitoring, existing systems do not address uncertainties in risk assessments. We developed an enhanced risk monitoring framework based on commonly used systems like the Sequential Organ Failure Assessment (SOFA) score by incorporating uncertainties to improve the effectiveness of real-time risk monitoring in ICUs.Methods: This study included 5,351 patients admitted to the Cardiothoracic ICU in the National University Hospital in Singapore. We developed machine learning models to predict long lead-time variables and computed real-time SOFA scores using these predictions. We calculated intervals to capture uncertainties in risk assessments and validated the association of the estimated real-time scores and intervals with mortality and readmission.Results: Our model outperforms the SOFA score in predicting 24-hour mortality: Nagelkerke’s R-squared (0.224 vs. 0.185, p Conclusions: Incorporating uncertainties improved existing scores in real-time monitoring, which could be used to trigger on-demand laboratory tests, potentially improving early detection, reducing unnecessary testing, and thereby lowering healthcare expenditures, mortality, and readmission rates in clinical practice. 2024-12-18T08:00:00Z text https://ink.library.smu.edu.sg/lkcsb_research/7639 Research Collection Lee Kong Chian School Of Business eng Institutional Knowledge at Singapore Management University Risk monitoring SOFA score qSOFA score 24-hour mortality readmission rate Databases and Information Systems Health Information Technology
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Risk monitoring
SOFA score
qSOFA score
24-hour mortality
readmission rate
Databases and Information Systems
Health Information Technology
spellingShingle Risk monitoring
SOFA score
qSOFA score
24-hour mortality
readmission rate
Databases and Information Systems
Health Information Technology
HE, Yan
LUO, Qian
WANG, Hai
ZHENG, Zhichao
LUO, Haidong
OOI, Oon Cheong
Real-time estimated sequential organ failure assessment (SOFA) score with intervals: Improved risk monitoring with estimated uncertainty in health condition for patients in intensive care units
description Purpose: Real-time risk monitoring is critical but challenging in intensive care units (ICUs) due to the lack of real-time updates for most clinical variables. Although real-time predictions have been integrated into various risk-scoring systems to aid monitoring, existing systems do not address uncertainties in risk assessments. We developed an enhanced risk monitoring framework based on commonly used systems like the Sequential Organ Failure Assessment (SOFA) score by incorporating uncertainties to improve the effectiveness of real-time risk monitoring in ICUs.Methods: This study included 5,351 patients admitted to the Cardiothoracic ICU in the National University Hospital in Singapore. We developed machine learning models to predict long lead-time variables and computed real-time SOFA scores using these predictions. We calculated intervals to capture uncertainties in risk assessments and validated the association of the estimated real-time scores and intervals with mortality and readmission.Results: Our model outperforms the SOFA score in predicting 24-hour mortality: Nagelkerke’s R-squared (0.224 vs. 0.185, p Conclusions: Incorporating uncertainties improved existing scores in real-time monitoring, which could be used to trigger on-demand laboratory tests, potentially improving early detection, reducing unnecessary testing, and thereby lowering healthcare expenditures, mortality, and readmission rates in clinical practice.
format text
author HE, Yan
LUO, Qian
WANG, Hai
ZHENG, Zhichao
LUO, Haidong
OOI, Oon Cheong
author_facet HE, Yan
LUO, Qian
WANG, Hai
ZHENG, Zhichao
LUO, Haidong
OOI, Oon Cheong
author_sort HE, Yan
title Real-time estimated sequential organ failure assessment (SOFA) score with intervals: Improved risk monitoring with estimated uncertainty in health condition for patients in intensive care units
title_short Real-time estimated sequential organ failure assessment (SOFA) score with intervals: Improved risk monitoring with estimated uncertainty in health condition for patients in intensive care units
title_full Real-time estimated sequential organ failure assessment (SOFA) score with intervals: Improved risk monitoring with estimated uncertainty in health condition for patients in intensive care units
title_fullStr Real-time estimated sequential organ failure assessment (SOFA) score with intervals: Improved risk monitoring with estimated uncertainty in health condition for patients in intensive care units
title_full_unstemmed Real-time estimated sequential organ failure assessment (SOFA) score with intervals: Improved risk monitoring with estimated uncertainty in health condition for patients in intensive care units
title_sort real-time estimated sequential organ failure assessment (sofa) score with intervals: improved risk monitoring with estimated uncertainty in health condition for patients in intensive care units
publisher Institutional Knowledge at Singapore Management University
publishDate 2024
url https://ink.library.smu.edu.sg/lkcsb_research/7639
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