ArticleCritical care medicine2019
Quantitative Electroencephalogram Trends Predict Recovery in Hypoxic-Ischemic Encephalopathy.
Article in Critical care medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it, 35 citations in OpenAlex.
- Electroencephalogram-based machine learning models to predict neurologic outcome after cardiac arrest: A systematic review.Resuscitation · 2024Pooled it
- Association of EEG characteristics with outcomes following pediatric ICU cardiac arrest: A secondary analysis of the ICU-RESUScitation trial.Resuscitation · 2024Trial
- Prediction of Neurological Outcome and Mortality in Cardiac Arrest Patients: An Explainable Machine Learning Study Integrating HRV, EEG, and Clinical Features.Neurocritical care · 2026Article
- EEG-based machine-learning prognostication in comatose patients with indeterminate outcome after cardiac arrest.Critical care (London, England) · 2026Observational
- Prognostic assessment of comatose patients after cardiac arrest: construction of a simplified monitoring EEG risk score across different time windows.Frontiers in neurology · 2026Article
- Pediatric cardiac arrest outcome prediction using data-driven machine learning of early quantitative electroencephalogram (qEEG) features.Resuscitation · 2025Article
- How much information is needed to predict outcomes after cardiac arrest?Resuscitation · 2025Article
- Explainable machine learning model based on EEG, ECG, and clinical features for predicting neurological outcomes in cardiac arrest patient.Scientific reports · 2025Article
- Application of multi-feature-based machine learning models to predict neurological outcomes of cardiac arrest.Resuscitation plus · 2024Review
- Neurophysiology State Dynamics Underlying Acute Neurologic Recovery After Cardiac Arrest.Neurology · 2023Article
- Prognosis of consciousness disorders in the intensive care unit.Presse medicale (Paris, France : 1983) · 2023Review
- Artificial Intelligence in Resuscitation: A Scoping Review.Journal of clinical medicine · 2023Article
- Prognostication after cardiac arrest: how EEG and evoked potentials may improve the challenge.Annals of intensive care · 2022Review
- Recent applications of quantitative electroencephalography in adult intensive care units: a comprehensive review.Journal of neurology · 2022Review
- Predicting Neurological Outcome From Electroencephalogram Dynamics in Comatose Patients After Cardiac Arrest With Deep Learning.IEEE transactions on bio-medical engineering · 2022Article
- Predicting neurological outcome in comatose patients after cardiac arrest with multiscale deep neural networks.Resuscitation · 2021Article
- Artificial Intelligence Analysis of EEG Amplitude in Intensive Heart Care.Journal of healthcare engineering · 2021Article
- Narrative Review of Neurologic Complications in Adults on ECMO: Prevalence, Risks, Outcomes, and Prevention Strategies.Frontiers in medicine · 2021Review
- Comparison of parametric and nonparametric methods for outcome prediction using longitudinal data after cardiac arrest.Resuscitation · 2020Article
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Authors and funding
16 authors at 7 institutions in 2 countries.
Funding
Abstract
objectivesElectroencephalogram features predict neurologic recovery following cardiac arrest. Recent work has shown that prognostic implications of some key electroencephalogram features change over time. We explore whether time dependence exists for an expanded selection of quantitative electroencephalogram features and whether accounting for this time dependence enables better prognostic predictions.
designRetrospective.
settingICUs at four academic medical centers in the United States. PATIENTS: Comatose patients with acute hypoxic-ischemic encephalopathy.
interventionsNone. MEASUREMENTS AND MAIN
resultsWe analyzed 12,397 hours of electroencephalogram from 438 subjects. From the electroencephalogram, we extracted 52 features that quantify signal complexity, category, and connectivity. We modeled associations between dichotomized neurologic outcome (good vs poor) and quantitative electroencephalogram features in 12-hour intervals using sequential logistic regression with Elastic Net regularization. We compared a predictive model using time-varying features to a model using time-invariant features and to models based on two prior published approaches. Models were evaluated for their ability to predict binary outcomes using area under the receiver operator curve, model calibration (how closely the predicted probability of good outcomes matches the observed proportion of good outcomes), and sensitivity at several common specificity thresholds of interest. A model using time-dependent features outperformed (area under the receiver operator curve, 0.83 ± 0.08) one trained with time-invariant features (0.79 ± 0.07; p < 0.05) and a random forest approach (0.74 ± 0.13; p < 0.05). The time-sensitive model was also the best-calibrated.
conclusionsThe statistical association between quantitative electroencephalogram features and neurologic outcome changed over time, and accounting for these changes improved prognostication performance.
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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.