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 in | Indian heart journal Vol. 74; no. 6; pp. 513 - 518 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
India
Elsevier, a division of RELX India, Pvt. Ltd
01.11.2022
Elsevier |
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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, |
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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 |
Author_xml | – sequence: 1 givenname: Hendry Purnasidha orcidid: 0000-0002-1140-8154 surname: Bagaswoto fullname: Bagaswoto, Hendry Purnasidha email: arsyadhiracarissa@gmail.com organization: Cardiologist at Cardiology and Vascular Medicine Department of Medical, Public Health, and Nursing Faculty Universitas Gadjah Mada, Yogyakarta, 55281, Indonesia – sequence: 2 givenname: Yuwinda Prima surname: Ardelia fullname: Ardelia, Yuwinda Prima email: dellardelia14@gmail.com organization: Resident of Cardiology and Vascular Medicine Department of Medical, Public Health, and Nursing Faculty Universitas Gadjah Mada, Indonesia – sequence: 3 givenname: Budi Yuli surname: Setianto fullname: Setianto, Budi Yuli email: budyuls@gmail.com organization: Cardiologist at Cardiology and Vascular Medicine Department of Medical, Public Health, and Nursing Faculty Universitas Gadjah Mada, Yogyakarta, 55281, Indonesia |
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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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Keywords | EF STE_ACS Mortality CICU Critical care TAPSE Prognostic CVD ACS NSTE_ACS Risk scores HF AHF |
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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) |
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