ArticleScientific reports2016
Predicting the outcomes for out-of-hospital cardiac arrest patients using multiple biomarkers and suspension microarray assays.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Chest compression quality decreases in hypoxic conditions simulating an airliner cabin at cruising altitude: a randomized, controlled, double-blind Manikin Study.Scientific reports · 2024Trial
- Prognostic value of brain natriuretic peptide and N-terminal pro b-type natriuretic peptide in patients with cardiac arrest: A systematic review and meta-analysis.Pakistan journal of medical sciences · 2026Article
- Prognostic Value of Serum S100B Protein for Neurological Outcomes After Cardiac Arrest: A Systematic Review and Meta-Analysis.Journal of clinical medicine · 2025Review
- Galectin-3 Predicts Long-Term Risk of Cerebral Disability and Mortality in Out-of-Hospital Cardiac Arrest Survivors.Journal of personalized medicine · 2024Article
- Biomarkers associated with mortality in pediatric patients with cardiac arrest and acute respiratory distress syndrome.Resuscitation · 2022Article
- Risk prediction models for out-of-hospital cardiac arrest outcomes in England.European heart journal. Quality of care & clinical outcomes · 2021Article
- Serum S100 Protein Is a Reliable Predictor of Brain Injury After Out-of-Hospital Cardiac Arrest: A Cohort Study.Frontiers in cardiovascular medicine · 2021Article
- Endothelial BMP4 Promotes Leukocyte Rolling and Adhesion and Is Elevated in Patients After Survived Out-of-Hospital Cardiac Arrest.Inflammation · 2020Article
- Clinical and Hematological Predictors for Return of Spontaneous Circulation in Patients With Out-of-Hospital Cardiac Arrest.Journal of acute medicine · 2020Article
- Prognostic value of the delta neutrophil index in pediatric cardiac arrest.Scientific reports · 2020Article
- Tissue-Specific Metabolic Profiles After Prolonged Cardiac Arrest Reveal Brain Metabolome Dysfunction Predominantly After Resuscitation.Journal of the American Heart Association · 2019Article
- Semaphorin 3F Promotes Transendothelial Migration of Leukocytes in the Inflammatory Response After Survived Cardiac Arrest.Inflammation · 2019Article
- Characterising risk of in-hospital mortality following cardiac arrest using machine learning: A retrospective international registry study.PLoS medicine · 2018Article
- Associations between blood glucose level and outcomes of adult in-hospital cardiac arrest: a retrospective cohort study.Cardiovascular diabetology · 2016Observational
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Predicting the prognosis for cardiac arrest is still challenging. Combining biomarkers from diverse pathophysiological pathways may provide reliable indicators for the severity of injury and predictors of long-term outcomes. We investigated the feasibility of using a multimarker strategy with key independent biomarkers to improve the prediction of outcomes in cardiac arrest. Adult out-of-hospital cardiac arrest patients with sustained return of spontaneous circulation were prospectively enrolled in this study. Blood samples were taken at 2 and 24 hours after cardiac arrest. Suspension microarray assays were used to test 21 different biomarkers. A total of 99 patients were enrolled, 45 of whom survived to hospital discharge. We identified 11 biomarkers that, when combined with clinical variables and factors of APACHE II score and history of arrhythmia, were independent determinants for outcome of in-hospital mortality (concordance = 0.9249, standard error = 0.0779). Three biomarkers combined with APACHE II and age were independent determinants for favorable neurological outcome at hospital discharge (area under the receiver-operator characteristic curve, 0.938; 95% confidence interval, 0.854 ~ 1.0). In conclusion, a systemic multiple biomarker approach using suspension microarray assays can identify independent predictors and model the outcomes of cardiac arrest patients during the post-cardiac arrest period.
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