ArticleNeurocritical care2022
Outcome Prediction of Postanoxic Coma: A Comparison of Automated Electroencephalography Analysis Methods.
Article in Neurocritical care, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 22 citations in OpenAlex.
- Electroencephalogram-based machine learning models to predict neurologic outcome after cardiac arrest: A systematic review.Resuscitation · 2024Pooled it
- EEG for bedside monitoring: the intensivist's point of view.Critical care (London, England) · 2026Review
- EEG-based machine-learning prognostication in comatose patients with indeterminate outcome after cardiac arrest.Critical care (London, England) · 2026Observational
- How much information is needed to predict outcomes after cardiac arrest?Resuscitation · 2025Article
- Artificial intelligence in resuscitation: a scoping review.Resuscitation plus · 2025Review
- Using artificial intelligence to optimize anti-seizure treatment and EEG-guided decisions in severe brain injury.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2025Review
- What does my network learn? Assessing interpretability of deep learning for EEG.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Application of multi-feature-based machine learning models to predict neurological outcomes of cardiac arrest.Resuscitation plus · 2024Review
- AI and machine learning in resuscitation: Ongoing research, new concepts, and key challenges.Resuscitation plus · 2023Review
- Deep learning for EEG-based prognostication after cardiac arrest: from current research to future clinical applications.Frontiers in neurology · 2023Review
Corrections and comments
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Authors and funding
14 authors at 7 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTo compare three computer-assisted quantitative electroencephalography (EEG) prediction models for the outcome prediction of comatose patients after cardiac arrest regarding predictive performance and robustness to artifacts.
methodsA total of 871 continuous EEGs recorded up to 3 days after cardiac arrest in intensive care units of five teaching hospitals in the Netherlands were retrospectively analyzed. Outcome at 6 months was dichotomized as "good" (Cerebral Performance Category 1-2) or "poor" (Cerebral Performance Category 3-5). Three prediction models were implemented: a logistic regression model using two quantitative features, a random forest model with nine features, and a deep learning model based on a convolutional neural network. Data from two centers were used for training and fivefold cross-validation (n = 663), and data from three other centers were used for external validation (n = 208). Model output was the probability of good outcome. Predictive performances were evaluated by using receiver operating characteristic analysis and the calculation of predictive values. Robustness to artifacts was evaluated by using an artifact rejection algorithm, manually added noise, and randomly flattened channels in the EEG.
resultsThe deep learning network showed the best overall predictive performance. On the external test set, poor outcome could be predicted by the deep learning network at 24 h with a sensitivity of 54% (95% confidence interval [CI] 44-64%) at a false positive rate (FPR) of 0% (95% CI 0-2%), significantly higher than the logistic regression (sensitivity 33%, FPR 0%) and random forest models (sensitivity 13%, FPR, 0%) (p < 0.05). Good outcome at 12 h could be predicted by the deep learning network with a sensitivity of 78% (95% CI 52-100%) at a FPR of 12% (95% CI 0-24%) and by the logistic regression model with a sensitivity of 83% (95% CI 83-83%) at a FPR of 3% (95% CI 3-3%), both significantly higher than the random forest model (sensitivity 1%, FPR 0%) (p < 0.05). The results of the deep learning network were the least affected by the presence of artifacts, added white noise, and flat EEG channels.
conclusionsA deep learning model outperformed logistic regression and random forest models for reliable, robust, EEG-based outcome prediction of comatose patients after cardiac arrest.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.