ArticleJournal of clinical monitoring and computing2016
Trending autoregulatory indices during treatment for traumatic brain injury.
Article in Journal of clinical monitoring and computing, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Interpolation and Imputation Strategies for Missing Segments in Continuous Pressure-Flow Cerebral Bio-Signals: A Systematic Scoping Review.Sensors (Basel, Switzerland) · 2026Article
- A 1-Dimensional Physiological Signal Prediction Method Based on Composite Feature Preprocessing and Multi-Scale Modeling.Sensors (Basel, Switzerland) · 2025Article
- Deep latent force models: ODE-based process convolutions for Bayesian deep learning.Machine learning · 2025Article
- Emulating clinical pressure waveforms in cell culture using an Arduino-controlled millifluidic 3D-printed platform for 96-well plates.Lab on a chip · 2023Article
- Dynamic detection and reversal of myocardial ischemia using an artificially intelligent bioelectronic medicine.Science advances · 2022Article
- Machine Learning-Based Continuous Intracranial Pressure Prediction for Traumatic Injury Patients.IEEE journal of translational engineering in health and medicine · 2022Article
- Non-Invasive Hemodynamics Monitoring System Based on Electrocardiography via Deep Convolutional Autoencoder.Sensors (Basel, Switzerland) · 2021Article
- Clinical Decision Support for Traumatic Brain Injury: Identifying a Framework for Practical Model-Based Intracranial Pressure Estimation at Multihour Timescales.JMIR medical informatics · 2021Article
- Near-infrared Spectroscopy-derived Cerebral Autoregulation Indices Independently Predict Clinical Outcome in Acutely Ill Comatose Patients.Journal of neurosurgical anesthesiology · 2020Observational
- Non-Invasive Blood Pressure Estimation from ECG Using Machine Learning Techniques.Sensors (Basel, Switzerland) · 2018Article
- Journal of clinical monitoring and computing 2016 end of year summary: monitoring cerebral oxygenation and autoregulation.Journal of clinical monitoring and computing · 2017Review
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Our goal is to use automatic data monitoring for reliable prediction of episodes of intracranial hypertension in patients with traumatic brain injury. Here we test the validity of our method on retrospective patient data. We developed the Continuous Hemodynamic Autoregulatory Monitor (CHARM), that siphons and stores signals from existing monitors in the surgical intensive care unit (SICU), efficiently compresses them, and standardizes the search for statistical relationships between any proposed index and adverse events. CHARM uses an automated event detector to reliably locate episodes of elevated intracranial pressure (ICP), while eliminating artifacts within retrospective patient data. A graphical user interface allowed data scanning, selection of criteria for events, and calculating indices. The pressure reactivity index (PRx), defined as the least square linear regression slope of intracranial pressure versus arterial BP, was calculated for a single case that spanned 259 h. CHARM collected continuous records of ABP, ICP, ECG, SpO2, and ventilation from 29 patients with TBI over an 18-month period. Analysis of a single patient showed that PRx data distribution in the single hours immediately prior to all 16 intracranial hypertensive events, significantly differed from that in the 243 h that did not precede such events (p < 0.0001). The PRx index, however, lacked sufficient resolution as a real-time predictor of IH in this patient. CHARM streamlines the search for reliable predictors of intracranial hypertension. We report statistical evidence supporting the predictive potential of the pressure reactivity index.
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