ArticleNeurocritical care2018
Electronic Health Data Predict Outcomes After Aneurysmal Subarachnoid Hemorrhage.
Article in Neurocritical care, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prediction and Risk Assessment Models for Subarachnoid Hemorrhage: A Systematic Review on Case Studies.BioMed research international · 2022Pooled it
- Denosumab for Prevention of Acute Onset Immobilization-Induced Alterations of Bone Turnover: A Randomized Controlled Trial.Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research · 2022Trial
- Predictors of Outcome Clusters in Patients With Unruptured Intracranial Aneurysms Treated With Microsurgery: An Unsupervised Machine Learning Analysis.Neurosurgery practice · 2026Article
- Personalized treatment approaches in neurocritical care.Acute and critical care · 2026Article
- Incidence and risk factors of extremely poor functional outcome after burr-hole evacuation of chronic subdural hematoma in octogenarians and nonagenarians- a retrospective cohort analysis.BMC anesthesiology · 2025Article
- Machine learning modeling for outcome prediction of hospitalized patients with aneurysmal subarachnoid hemorrhage.Interventional neuroradiology : journal of peritherapeutic neuroradiology, surgical procedures and related neurosciences · 2025Article
- Clinical Prediction Models in Neurocritical Care: An Overview of the Literature and Example Application to Prediction of Hospital Mortality in Traumatic Brain Injury.Neurocritical care · 2025Review
- Antiseizure Medication Use and Outcomes After Suspected or Confirmed Acute Symptomatic Seizures.JAMA neurology · 2024Article
- Machine learning to predict mortality for aneurysmal subarachnoid hemorrhage (aSAH) using a large nationwide EHR database.PLOS digital health · 2023Article
- A visualized MAC nomogram online predicts the risk of three-month mortality in Chinese elderly aneurysmal subarachnoid hemorrhage patients undergoing endovascular coiling.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2023Article
- Review
- Antiseizure Medication Treatment and Outcomes in Patients with Subarachnoid Hemorrhage Undergoing Continuous EEG Monitoring.Neurocritical care · 2022Observational
- A novel visual dynamic nomogram to online predict the risk of unfavorable outcome in elderly aSAH patients after endovascular coiling: A retrospective study.Frontiers in neuroscience · 2022Article
- Machine Learning and Intracranial Aneurysms: From Detection to Outcome Prediction.Acta neurochirurgica. Supplement · 2022Article
- Glycemic indices predict outcomes after aneurysmal subarachnoid hemorrhage: a retrospective single center comparative analysis.Scientific reports · 2021Article
- Clinical EEG slowing correlates with delirium severity and predicts poor clinical outcomes.Neurology · 2019Article
- Effect of epileptiform abnormality burden on neurologic outcome and antiepileptic drug management after subarachnoid hemorrhage.Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology · 2018Article
- Sedation Patterns and Hyperosmolar Therapy in Emergency Departments were Associated with Blood Pressure Variability and Outcomes in Patients with Spontaneous Intracranial Hemorrhage.Journal of emergencies, trauma, and shockArticle
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Authors and funding
14 authors.
Funding
Abstract
backgroudUsing electronic health data, we sought to identify clinical and physiological parameters that in combination predict neurologic outcomes after aneurysmal subarachnoid hemorrhage (aSAH).
methodsWe conducted a single-center retrospective cohort study of patients admitted with aSAH between 2011 and 2016. A set of 473 predictor variables was evaluated. Our outcome measure was discharge Glasgow Outcome Scale (GOS). For laboratory and physiological data, we computed the minimum, maximum, median, and variance for the first three admission days. We created a penalized logistic regression model to determine predictors of outcome and a multivariate multilevel prediction model to predict poor (GOS 1-2), intermediate (GOS 3), or good (GOS 4-5) outcomes.
resultsOne hundred and fifty-three patients met inclusion criteria; most were discharged with a GOS of 3. Multivariate analysis predictors of mortality (AUC 0.9198) included APACHE II score, Glasgow Come Scale (GCS), white blood cell (WBC) count, mean arterial pressure, variance of serum glucose, intracranial pressure (ICP), and serum sodium. Predictors of death/dependence versus independence (GOS 4-5)(AUC 0.9456) were levetiracetam, mechanical ventilation, WBC count, heart rate, ICP variance, GCS, APACHE II, and epileptiform discharges. The multiclass prediction model selected GCS, admission APACHE II, periodic discharges, lacosamide, and rebleeding as significant predictors; model performance exceeded 80% accuracy in predicting poor or good outcome and exceeded 70% accuracy for predicting intermediate outcome.
conclusionsVariance in early physiologic data can impact patient outcomes and may serve as targets for early goal-directed therapy. Electronically retrievable features such as ICP, glucose levels, and electroencephalography patterns should be considered in disease severity and risk stratification scores.
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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.