ArticleJournal of the American Heart Association2019
Derivation and Validation of a Novel Cardiac Intensive Care Unit Admission Risk Score for Mortality.
Article in Journal of the American Heart Association, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed.
- Pressure-Adjusted Heart Rate by Echocardiography and Mortality in the Cardiac Intensive Care Unit.JACC. Advances · 2026Article
- Sardjito Cardiovascular Intensive Care Score as an Alternative to Mayo Cardiac Admission Risk Score for Predicting Mortality in Cardiovascular Intensive Care Patients.Cardiology research · 2026Article
- Right Ventricular Function and Mortality in Critically Ill Patients With Heart Failure and Cardiogenic Shock.International journal of heart failure · 2026Article
- Timing of Antibiotics in ICU Pneumonia: An Observational Association Between Early Treatment and Higher Mortality.Antibiotics (Basel, Switzerland) · 2026Article
- Echocardiographic Predictors of Mortality in Cardiac Intensive Care Unit Patients With Pulmonary Hypertension.The American journal of cardiology · 2025Article
- Pulmonary Effective Arterial Elastance by Echocardiography and Mortality in the Cardiac Intensive Care Unit.JACC. Advances · 2025Article
- Clinical Profiles and Outcomes of Adult Congenital Heart Disease Patients in the Cardiac Intensive Care Unit.JACC. Advances · 2025Article
- Noninvasive Hemodynamic Characterization of Cardiohepatic Syndrome in the Cardiac Intensive Care Unit.Journal of the American Heart Association · 2025Observational
- Cardiac autonomic function score: a novel risk stratification tool in the cardiac intensive care unit based on periodic repolarization dynamics and deceleration capacity of heart rate (LMU-eICU study).European heart journal. Digital health · 2025Article
- Artificial Intelligence ECG Diastolic Dysfunction and Survival in Cardiac Intensive Care Unit Patients.Journal of the American Heart Association · 2025Article
- Supervised machine learning including environmental factors to predict in-hospital outcomes in acute heart failure patients.European heart journal. Digital health · 2025Article
- Machine learning score to predict in-hospital outcomes in patients hospitalized in cardiac intensive care unit.European heart journal. Digital health · 2025Article
- Hepatic Biomarker Abnormalities in the Cardiac Intensive Care Unit: Proposed Criteria for Cardiohepatic Syndrome.JACC. Advances · 2025Article
- Unsupervised machine learning to identify subphenotypes among cardiac intensive care unit patients with heart failure.ESC heart failure · 2024Article
- Mortality Risk Stratification Utilizing Artificial Intelligence Electrocardiogram for Hyperkalemia in Cardiac Intensive Care Unit Patients.JACC. Advances · 2024Article
- Association between the shock index on admission and in-hospital mortality in the cardiac intensive care unit.PloS one · 2024Article
- Admission Total Leukocyte Count as a Predictor of Mortality in Cardiac Intensive Care Unit Patients.JACC. Advances · 2024Article
- More Alike Than Not? Predicting Mortality in the Cardiac and Medical Intensive Care Units.JACC. Advances · 2024Article
- Optimized Risk Score to Predict Mortality in Patients With Cardiogenic Shock in the Cardiac Intensive Care Unit.Journal of the American Heart Association · 2023Article
- A simple APACHE IV risk dynamic nomogram that incorporates early admitted lactate for the initial assessment of 28-day mortality in critically ill patients with acute myocardial infarction.BMC cardiovascular disorders · 2022Article
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Authors and funding
10 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Background There are no risk scores designed specifically for mortality risk prediction in unselected cardiac intensive care unit (CICU) patients. We sought to develop a novel CICU-specific risk score for prediction of hospital mortality using variables available at the time of CICU admission. Methods and Results A database of CICU patients admitted from January 1, 2007 to April 30, 2018 was divided into derivation and validation cohorts. The top 7 predictors of hospital mortality were identified using stepwise backward regression, then used to develop the Mayo CICU Admission Risk Score (M-CARS), with integer scores ranging from 0 to 10. Discrimination was assessed using area under the receiver-operator curve analysis. Calibration was assessed using the Hosmer-Lemeshow statistic. The derivation cohort included 10 004 patients and the validation cohort included 2634 patients (mean age 67.6 years, 37.7% females). Hospital mortality was 9.2%. Predictor variables included in the M-CARS were cardiac arrest, shock, respiratory failure, Braden skin score, blood urea nitrogen, anion gap and red blood cell distribution width at the time of CICU admission. The M-CARS showed a graded relationship with hospital mortality (odds ratio 1.84 for each 1-point increase in M-CARS, 95% CI 1.78-1.89). In the validation cohort, the M-CARS had an area under the receiver-operator curve of 0.86 for hospital mortality, with good calibration (P=0.21). The 47.1% of patients with M-CARS <2 had hospital mortality of 0.8%, and the 5.2% of patients with M-CARS >6 had hospital mortality of 51.6%. Conclusions Using 7 variables available at the time of CICU admission, the M-CARS can predict hospital mortality in unselected CICU patients with excellent discrimination.
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