ArticlePloS one2016
Causal Structure of Brain Physiology after Brain Injury from Subarachnoid Hemorrhage.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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
8 citing papers in PubMed.
- Toward Causal Relationships of Intracranial Pressure, Cerebrovascular Reactivity, Interventions, and Outcome in Severe Traumatic Brain Injury: A CENTER-TBI High-Resolution Substudy.Neurocritical care · 2026Article
- Causal inference for time series datasets with partially overlapping variables.Journal of biomedical informatics · 2025Article
- The Role of Brain Tissue Oxygenation Monitoring in the Management of Subarachnoid Hemorrhage: A Scoping Review.Neurocritical care · 2023Article
- Classification of Level of Consciousness in a Neurological ICU Using Physiological Data.Neurocritical care · 2023Observational
- Probabilistic prediction of increased intracranial pressure in patients with severe traumatic brain injury.Scientific reports · 2022Article
- Lagged Correlations among Physiological Variables as Indicators of Consciousness in Stroke Patients.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2019Article
- Automated Identification of Causal Moderators in Time-Series Data.Proceedings of machine learning research · 2018Article
- Electroencephalographic Periodic Discharges and Frequency-Dependent Brain Tissue Hypoxia in Acute Brain Injury.JAMA neurology · 2017Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
High frequency physiologic data are routinely generated for intensive care patients. While massive amounts of data make it difficult for clinicians to extract meaningful signals, these data could provide insight into the state of critically ill patients and guide interventions. We develop uniquely customized computational methods to uncover the causal structure within systemic and brain physiologic measures recorded in a neurological intensive care unit after subarachnoid hemorrhage. While the data have many missing values, poor signal-to-noise ratio, and are composed from a heterogeneous patient population, our advanced imputation and causal inference techniques enable physiologic models to be learned for individuals. Our analyses confirm that complex physiologic relationships including demand and supply of oxygen underlie brain oxygen measurements and that mechanisms for brain swelling early after injury may differ from those that develop in a delayed fashion. These inference methods will enable wider use of ICU data to understand patient physiology.
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Registered trials
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