First 24-h Sardjito Cardiovascular Intensive Care (SCIENCE) admission risk score to predict mortality in cardiovascular intensive care unit (CICU)

The application of prognostic scoring systems to identify risk of death within 24 h of CICU admission has significant consequences for clinical decision-making. Previous score of parameters collected after 24 h was considered too late to predict mortality. As a result, we attempted to develop a CICU...

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Published inIndian heart journal Vol. 74; no. 6; pp. 513 - 518
Main Authors Bagaswoto, Hendry Purnasidha, Ardelia, Yuwinda Prima, Setianto, Budi Yuli
Format Journal Article
LanguageEnglish
Published India Elsevier, a division of RELX India, Pvt. Ltd 01.11.2022
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Abstract The application of prognostic scoring systems to identify risk of death within 24 h of CICU admission has significant consequences for clinical decision-making. Previous score of parameters collected after 24 h was considered too late to predict mortality. As a result, we attempted to develop a CICU admission risk score to predict hospital mortality using indicators collected within 24 h. Data were obtained from SCIENCE registry from January 1, 2021 to December 21, 2021. Outcomes of 657 patients (mean age 58.91 ± 12.8 years) were recorded retrospectively. Demography, risk factors, comorbidities, vital signs, laboratory and echocardiography data at 24-h of patient admitted to CICU were analysed by multivariate logistic regression to create two models of scoring system (probability and cut-off model) to predict in-hospital mortality of any cause. From a total of 657 patients, the hospital mortality was 15%. The significant predictors of mortality were male, acute heart failure, hemodynamic instability, pneumonia, baseline creatinine ≥1.5 mg/dL, TAPSE <17 mm, and the use of mechanical ventilator within first 24-h of CICU admission. Based on Receiver Operating Characteristic (ROC) curve analysis a cut off of ≥3 is considered to be a high risk of in-hospital mortality (sensitivity 75% and specificity 65%). The initial 24-h SCIENCE admission risk rating system can be used to predict in-hospital mortality in patients admitted to the CICU with a high degree of sensitivity and specificity,
AbstractList The application of prognostic scoring systems to identify risk of death within 24 h of CICU admission has significant consequences for clinical decision-making. Previous score of parameters collected after 24 h was considered too late to predict mortality. As a result, we attempted to develop a CICU admission risk score to predict hospital mortality using indicators collected within 24 h. Data were obtained from SCIENCE registry from January 1, 2021 to December 21, 2021. Outcomes of 657 patients (mean age 58.91 ± 12.8 years) were recorded retrospectively. Demography, risk factors, comorbidities, vital signs, laboratory and echocardiography data at 24-h of patient admitted to CICU were analysed by multivariate logistic regression to create two models of scoring system (probability and cut-off model) to predict in-hospital mortality of any cause. From a total of 657 patients, the hospital mortality was 15%. The significant predictors of mortality were male, acute heart failure, hemodynamic instability, pneumonia, baseline creatinine ≥1.5 mg/dL, TAPSE <17 mm, and the use of mechanical ventilator within first 24-h of CICU admission. Based on Receiver Operating Characteristic (ROC) curve analysis a cut off of ≥3 is considered to be a high risk of in-hospital mortality (sensitivity 75% and specificity 65%). The initial 24-h SCIENCE admission risk rating system can be used to predict in-hospital mortality in patients admitted to the CICU with a high degree of sensitivity and specificity.
Background and objectives: The application of prognostic scoring systems to identify risk of death within 24 h of CICU admission has significant consequences for clinical decision-making. Previous score of parameters collected after 24 h was considered too late to predict mortality. As a result, we attempted to develop a CICU admission risk score to predict hospital mortality using indicators collected within 24 h. Methods: Data were obtained from SCIENCE registry from January 1, 2021 to December 21, 2021. Outcomes of 657 patients (mean age 58.91 ± 12.8 years) were recorded retrospectively. Demography, risk factors, comorbidities, vital signs, laboratory and echocardiography data at 24-h of patient admitted to CICU were analysed by multivariate logistic regression to create two models of scoring system (probability and cut-off model) to predict in-hospital mortality of any cause. Results: From a total of 657 patients, the hospital mortality was 15%. The significant predictors of mortality were male, acute heart failure, hemodynamic instability, pneumonia, baseline creatinine ≥1.5 mg/dL, TAPSE <17 mm, and the use of mechanical ventilator within first 24-h of CICU admission. Based on Receiver Operating Characteristic (ROC) curve analysis a cut off of ≥3 is considered to be a high risk of in-hospital mortality (sensitivity 75% and specificity 65%). Conclusion: The initial 24-h SCIENCE admission risk rating system can be used to predict in-hospital mortality in patients admitted to the CICU with a high degree of sensitivity and specificity,
Author Setianto, Budi Yuli
Ardelia, Yuwinda Prima
Bagaswoto, Hendry Purnasidha
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/36370802$$D View this record in MEDLINE/PubMed
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Copyright 2022 Cardiological Society of India
Copyright © 2022 Cardiological Society of India. Published by Elsevier, a division of RELX India, Pvt. Ltd. All rights reserved.
2022 Cardiological Society of India. Published by Elsevier, a division of RELX India, Pvt. Ltd. 2022 Cardiological Society of India
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– notice: Copyright © 2022 Cardiological Society of India. Published by Elsevier, a division of RELX India, Pvt. Ltd. All rights reserved.
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Issue 6
Keywords EF
STE_ACS
Mortality
CICU
Critical care
TAPSE
Prognostic
CVD
ACS
NSTE_ACS
Risk scores
HF
AHF
Language English
License This is an open access article under the CC BY-NC-ND license.
Copyright © 2022 Cardiological Society of India. Published by Elsevier, a division of RELX India, Pvt. Ltd. All rights reserved.
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Snippet The application of prognostic scoring systems to identify risk of death within 24 h of CICU admission has significant consequences for clinical...
Background and objectives: The application of prognostic scoring systems to identify risk of death within 24 h of CICU admission has significant consequences...
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SubjectTerms Aged
Coronary Care Units
Critical Care
Female
Hospital Mortality
Humans
Intensive Care Units
Male
Middle Aged
Mortality
Prognostic
Research Brief
Retrospective Studies
Risk Assessment
Risk Factors
Risk scores
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Title First 24-h Sardjito Cardiovascular Intensive Care (SCIENCE) admission risk score to predict mortality in cardiovascular intensive care unit (CICU)
URI https://dx.doi.org/10.1016/j.ihj.2022.11.002
https://www.ncbi.nlm.nih.gov/pubmed/36370802
https://pubmed.ncbi.nlm.nih.gov/PMC9773286
https://doaj.org/article/e3d1d006170142528d47e623bc46773d
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