HDF5-Based Data Format for Archiving Complex Neuro-monitoring Data in Traumatic Brain Injury Patients

Cabeleira, Manuel; Ercole, Ari; Smielewski, Peter

doi:10.1007/978-3-319-65798-1_26

Cited by 10 publications

(5 citation statements)

References 3 publications

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“…Integrated platforms, e.g., Component Neuromonitoring Systems (Moberg Solutions, Inc., Ambler, PA), BedMaster (Excel Medical, Inc., Jupiter, FL), and ICM+ (Cambridge, UK), have improved data synchronization and device labeling for many of the devices used in neurocritical care. Recently, experts working primarily with physiologic data have begun coalescing around a common file format, HDF5, which is extensible and capable of handling a variety of data sizes including both high-frequency time series and intermittent laboratory data [58]. Efforts to harmonize physiologic data between sites and studies and to develop frameworks to identify physiologic phenotypes should lead to new insights.…”

Section: Challenges In Implementing Big Data At the Bedsidementioning

confidence: 99%

Neurocritical Care: Bench to Bedside (Eds. Claude Hemphill, Michael James) Integrating and Using Big Data in Neurocritical Care

Foreman

2020

Neurotherapeutics

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The critical care environment drives huge volumes of data, and clinicians are tasked with quickly processing this data and responding to it urgently. The neurocritical care environment increasingly involves EEG, multimodal intracranial monitoring, and complex imaging which preclude comprehensive human synthesis, and requires new concepts to integrate data into clinical care. By definition, Big Data is data that cannot be handled using traditional infrastructures and is characterized by the volume, variety, velocity, and variability of the data being produced. Big Data in the neurocritical care unit requires rethinking of data storage infrastructures and the development of tools and analytics to drive advancements in the field. Preprocessing, feature extraction, statistical inference, and analytic tools are required in order to achieve the primary goals of Big Data for clinical use: description, prediction, and prescription. Barriers to its use at bedside include a lack of infrastructure development within the healthcare industry, lack of standardization of data inputs, and ultimately existential and scientific concerns about the outputs that result from the use of tools such as artificial intelligence. However, as implied by the fundamental theorem of biomedical informatics, physicians remain central to the development and utility of Big Data to improve patient care.

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Section: Challenges In Implementing Big Data At the Bedsidementioning

confidence: 99%

Neurocritical Care: Bench to Bedside (Eds. Claude Hemphill, Michael James) Integrating and Using Big Data in Neurocritical Care

Foreman

2020

Neurotherapeutics

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“…Data preparation. High-frequency monitoring data, including the 1-min average physiological data (including ICP, MAP, CPP, PRx, CPPopt) and the six times daily review results, were anonymized and packaged into HDF5 files, 16 using ICM+, and imported into MATLAB (Release 2019b; The MathWorks, Inc., Natick, MA) for further cal-culations. Patients' total mean (covering the study protocol period) and 1-h mean physiological values were calculated after automated removal of non-physiological values in MATLAB.…”

Section: Discussionmentioning

confidence: 99%

Targeting Autoregulation-Guided Cerebral Perfusion Pressure after Traumatic Brain Injury (COGiTATE): A Feasibility Randomized Controlled Clinical Trial

Tas

Beqiri

Kaam

et al. 2021

Journal of Neurotrauma

Self Cite

116

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Managing traumatic brain injury (TBI) patients with a cerebral perfusion pressure (CPP) near to the cerebral autoregulation (CA)-guided ''optimal'' CPP (CPPopt) value is associated with improved outcome and might be useful to individualize care, but has never been prospectively evaluated. This study evaluated the feasibility and safety of CA-guided CPP management in TBI patients requiring intracranial pressure monitoring and therapy (TBIicp patients). The CPPopt Guided Therapy: Assessment of Target Effectiveness (COGiTATE) parallel two-arm feasibility trial took place in four tertiary centers. TBIicp patients were randomized to either the Brain Trauma Foundation (BTF) guideline CPP target range (control group) or to the individualized CA-guided CPP targets (intervention group). CPP targets were guided by six times daily software-based alerts for up to 5 days. The primary feasibility end-point was the percentage of time with CPP concordant (-5 mm Hg) with the set CPP targets. The main secondary safety endpoint was an increase in therapeutic intensity level (TIL) between the control and intervention group. Twenty-eight patients were randomized to the control and 32 patients to the intervention group. CPP in the intervention group was in the target range for 46.5% (interquartile range, 41.2-58) of the monitored time, significantly higher than the feasibility target specified in the published protocol (36%; p < 0.001). There were no significant differences between groups for TIL or for other safety end-points. Conclusively, targeting an individual and dynamic CA-guided CPP is feasible and safe in TBIicp patients. This encourages a prospective trial powered for clinical outcomes.

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“…The open-source hierarchical data format has recently gained popularity as a data standard to store ICU data sets. 61 The format and associated tools are freely available and can hold large amounts of almost any kind of annotated data (numeric and waveform). Widespread utilization of a robust platform such as HDF5 is crucial for data sharing and developing large multicenter physiologic data repositories.…”

Section: Neuromonitoring To Direct Precision Medicinementioning

confidence: 99%

Precision Medicine in Neurocritical Care for Cerebrovascular Disease Cases

Petersen

Sheth

Jha

2023

Stroke

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Scientific advances have informed many aspects of acute stroke care but have also highlighted the complexity and heterogeneity of cerebrovascular diseases. While practice guidelines are essential in supporting the clinical decision-making process, they may not capture the nuances of individual cases. Personalized stroke care in ICU has traditionally relied on integrating clinical examinations, neuroimaging studies, and physiologic monitoring to develop a treatment plan tailored to the individual patient. However, to realize the potential of precision medicine in stroke, we need advances and evidence in several critical areas, including data capture, clinical phenotyping, serum biomarker development, neuromonitoring, and physiology-based treatment targets. Mathematical tools are being developed to analyze the multitude of data and provide clinicians with real-time information and personalized treatment targets for the critical care management of patients with cerebrovascular diseases. This review summarizes research advances in these areas and outlines principles for translating precision medicine into clinical practice.

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HDF5-Based Data Format for Archiving Complex Neuro-monitoring Data in Traumatic Brain Injury Patients

Abstract: The new file format was implemented in ICM+ software and validated as part of a collaboration with participating centres across Europe.

Cited by 10 publications

References 3 publications

Neurocritical Care: Bench to Bedside (Eds. Claude Hemphill, Michael James) Integrating and Using Big Data in Neurocritical Care

Neurocritical Care: Bench to Bedside (Eds. Claude Hemphill, Michael James) Integrating and Using Big Data in Neurocritical Care

Targeting Autoregulation-Guided Cerebral Perfusion Pressure after Traumatic Brain Injury (COGiTATE): A Feasibility Randomized Controlled Clinical Trial

Precision Medicine in Neurocritical Care for Cerebrovascular Disease Cases

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