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Comparing clinical only and combined clinical laboratory models for ECPR outcomes in refractory cardiac arrest.
Sci Rep
January 2025
Department Emergency and Critical Care Medicine, Changhua Christian Hospital, Changhua, 50006, Taiwan.
Extracorporeal cardiopulmonary resuscitation (ECPR) improves survival for prolonged cardiac arrest (CA) but carries significant risks and costs due to ECMO. Previous predictive models have been complex, incorporating both clinical data and parameters obtained after CPR or ECMO initiation. This study aims to compare a simpler clinical-only model with a model that includes both clinical and pre-ECMO laboratory parameters, to refine patient selection and improve ECPR outcomes.
View Article and Find Full Text PDFPersonalized neuron-specific enolase level based on EEG pattern for prediction of poor outcome after cardiac arrest.
Ann Intensive Care
January 2025
Medical Intensive Care Unit, AP-HP Centre Université Paris Cité, Cochin hospital, 27 rue du Faubourg Saint Jacques, Paris, 7501, France.
Background: After cardiac arrest (CA), the European recommendations suggest to use a neuron-specific enolase (NSE) level > 60 µg/L at 48-72 h to predict poor outcome. However, the prognostic performance of NSE can vary depending on electroencephalogram (EEG). The objective was to determine whether the NSE threshold which predicts poor outcome varies according to EEG patterns and the effect of electrographic seizures on NSE level.
View Article and Find Full Text PDFPredicting Mortality in Patients Hospitalized With Acute Myocardial Infarction: From the National Cardiovascular Data Registry.
Circ Cardiovasc Qual Outcomes
January 2025
Division of Cardiology Lifespan Cardiovascular Institute, Warren Alpert Medical School of Brown University, Providence, RI (J.D.A.).
Background: In-hospital mortality risk prediction is an important tool for benchmarking quality and patient prognostication. Given changes in patient characteristics and treatments over time, a contemporary risk model for patients with acute myocardial infarction (MI) is needed.
Methods: Data from 313 825 acute MI hospitalizations between January 2019 and December 2020 for adults aged ≥18 years at 784 sites in the National Cardiovascular Data Registry Chest Pain-MI Registry were used to develop a risk-standardized model to predict in-hospital mortality.
A Machine Learning-Based Decision Support System for the Prognostication of Neurological Outcomes in Successfully Resuscitated Out-of-Hospital Cardiac Arrest Patients.
J Clin Med
December 2024
Department of Emergency Medicine, Korea University Anam Hospital, Seoul 02841, Republic of Korea.
This study uses machine learning and multicenter registry data for analyzing the determinants of a favorable neurological outcome in patients with out-of-hospital cardiac arrest (OHCA) and developing decision support systems for various subgroups. The data came from the Korean Cardiac Arrest Research Consortium registry, with 2679 patients who underwent OHCA aged 18 or above with the return of spontaneous circulation (ROSC). The dependent variable was a favorable neurological outcome (Cerebral Performance Category score 1-2), and 68 independent variables were included, e.
View Article and Find Full Text PDFIntroduction: Out-of-hospital cardiac arrest (OHCA) is a critical condition associated with high mortality rates and neurological impairment among survivors. In comatose OHCA patients who achieve return of spontaneous circulation, early risk stratification is important to inform treatment pathways and potentially improve outcomes. A range of prognostic tools have been developed to predict survival and neurological recovery.
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