Brain Injuries, AcuteNCT03362346
Signal Analysis for Neurocritical Patients
An observational study of Brain Injuries, Acute, run by Far Eastern Memorial Hospital at 1 site in Taiwan.
Trial summary
The project uses big data analysis techniques such as wavelet transform and deep learning to analyze physiological signals from neurocritical patients and build a model to evaluate intracranial condition and to predict neurological outcome. Read more about this study
- Who is it for?
- Ages 20 years and older
- Conditions the study is for
- Brain Injuries, Acute
- What is being tested?
- intracranial pressure monitoring
- Type of treatment
- Medical device
- How many people
- 156 participants
- Where
- 1 siteTaiwan
- Study dates
- 18 Dec 2017to 31 May 2018
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 20 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Neurocritical patients admitted to intensive care unit (ICU), including but not limited to traumatic brain injury, hemorrhagic stroke, ischemic stroke, brain infection, brain tumor and acute hydrocephalus.
Who may be able to join
- Age equal to or older than 20 years
- Neurocritical patients admitted to intensive care unit (ICU), including but not limited to traumatic brain injury, hemorrhagic stroke, ischemic stroke, brain infection, brain tumor and acute hydrocephalus.
- Patients who have undergone cranial surgery and had intracranial pressure monitor inserted or external ventricular drainage. The central monitor of ICU is able to collect the data continuously
Who cannot join
- Age younger than 20 years.
- Continuous monitoring of intracranial pressure is not feasible.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Analysis of Physiological Signals From Neurocritical Patients in Intensive Care Units Using Wavelet Transform and Deep Learning
The project uses big data analysis techniques such as wavelet transform and deep learning to analyze physiological signals from neurocritical patients and build a model to evaluate intracranial condition and to predict neurological outcome. By identification of correlations among these parameters and their trends, we may achieve early detection of anomalies and enhance the ability in judgement of current neurological condition and prediction of prognosis. By continuous input of the past and contemporary data in the ICU, the model will be modified repeatedly and its accuracy improves as the model grows. The model can be used to recognize abnormalities earlier and provide a warning system. Clinicians taking care of neurocritical patients can adjust their treatment policy and evaluate the outcome according to such system.
- Conditions
- Brain Injuries, Acute
- Being tested
- intracranial pressure monitoring
- Keywords
- Wavelet transform, Deep learning
- Medical subject headings
- Brain Injuries
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
intracranial pressure monitoringDevice
Also called physiological monitoring
The patients may have either intracranial pressure (ICP) monitor insertion or external ventricular drainage that can be used as ICP monitor.
| Study group | Role | What they receive |
|---|---|---|
| Neurocritical patients | Patients with brain injury from trauma, ischemic stroke, hemorrhage stroke (intracerebral hemorrhage, subarachnoid hemorrhage), brain tumor with increased intracranial pressure, brain infection, hydrocephalus, among others. Receives: Device: intracranial pressure monitoring |
What is being measured
The primary outcome is the main question the study is designed to answer.
Neurological status
Glasgow coma scale/Mortality
Measured: Discharge out of the intensive care unit, averaged 2 weeks
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted5 Dec 2017
- Study start18 Dec 2017
- Primary completion24 May 2018
- Study completion31 May 2018
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (7)Papers the sponsor has linked to this study.
- Other trials for Brain Injuries, AcuteStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 156 participants (actual)
Sponsor and investigators
- Lead sponsor
- Far Eastern Memorial Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Yi-Hsin Tsai, Far Eastern Memorial Hospital
- Investigators
- Yi-Hsin Tsai, M.D., Principal investigator, Far Eastern Memorial Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT03362346
- Sponsor's protocol ID
- 106152-E
- Status last verified
- Jun 2018
- First submitted
- 29 Nov 2017
- Last updated
- 6 Jun 2018
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Diederik P. Kingma and Jimmy Lei Ba. Adam: A method for stochastic optimization. Conference paper at ICLR 2015.
Michael Unser and Akram Aldroubi. (1996 Apr) A review of wavelets in biomedical applications. Proceedings of the IEEE 84(4): 626-638.
LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015 May 28;521(7553):436-44. doi: 10.1038/nature14539.
PubMed 26017442Christopher Torrence and Gilbert P. Compo. (1998 Jan) A practical guide to wavelet analysis. Bulletin of the American Meteorological Society 79(1):61-78.
Theis, Fabian & Meyer-Base, Anke. (2010). Biomedical Signal Analysis - Contemporary Methods and Applications. Biomedical Signal Analysis: Contemporary Methods and Applications.
Min S, Lee B, Yoon S. Deep learning in bioinformatics. Brief Bioinform. 2017 Sep 1;18(5):851-869. doi: 10.1093/bib/bbw068.
PubMed 27473064Yi Mao, Wenlin Chen, Yixin Chen, Chenyang Lu, Marin Kollef, and Thomas Bailey. (2012) An integrated data mining approach to real-time clinical monitoring and deterioration warning. Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining Pages 1140-1148. doi>10.1145/2339530.2339709
Study at a glance
From the registry record, last updated by the sponsor on 6 Jun 2018. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Analysis of Physiological Signals From Neurocritical Patients in Intensive Care Units Using Wavelet Transform and Deep Learning
- ClinicalTrials.gov ID
- NCT03362346
- Acronym / sponsor ID
- 106152-E
- Conditions
- Brain Injuries, Acute
- Keywords
- Wavelet transform, Deep learning
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 156 participants (actual)
- Lead sponsor
- Far Eastern Memorial HospitalUniversity, hospital or other
- Study start
- 18 Dec 2017 (actual)
- Primary completion
- 24 May 2018 (actual)
- Study completion
- 31 May 2018 (actual)
- First posted
- 5 Dec 2017 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
The project uses big data analysis techniques such as wavelet transform and deep learning to analyze physiological signals from neurocritical patients and build a model to evaluate intracranial condition and to predict neurological outcome. By identification of correlations among these parameters and their trends, we may achieve early detection of anomalies and enhance the ability in judgement of current neurological condition and prediction of prognosis. By continuous input of the past and contemporary data in the ICU, the model will be modified repeatedly and its accuracy improves as the model grows. The model can be used to recognize abnormalities earlier and provide a warning system. Clinicians taking care of neurocritical patients can adjust their treatment policy and evaluate the outcome according to such system.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Neurocritical patients | – | Device: intracranial pressure monitoringPatients with brain injury from trauma, ischemic stroke, hemorrhage stroke (intracerebral hemorrhage, subarachnoid hemorrhage), brain tumor with increased intracranial pressure, brain infection, hydrocephalus, among others. |
Who may qualify
- Ages
- 20 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Neurocritical patients admitted to intensive care unit (ICU), including but not limited to traumatic brain injury, hemorrhagic stroke, ischemic stroke, brain infection, brain tumor and acute hydrocephalus.
Key inclusion criteria (3 listed)
- Age equal to or older than 20 years
- Neurocritical patients admitted to intensive care unit (ICU), including but not limited to traumatic brain injury, hemorrhagic stroke, ischemic stroke, brain infection, brain tumor and acute hydrocephalus.
- Patients who have undergone cranial surgery and had intracranial pressure monitor inserted or external ventricular drainage. The central monitor of ICU is able to collect the data continuously
Key exclusion criteria (2 listed)
- Age younger than 20 years.
- Continuous monitoring of intracranial pressure is not feasible.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| intracranial pressure monitoringAlso: physiological monitoringThe patients may have either intracranial pressure (ICP) monitor insertion or external ventricular drainage that can be used as ICP monitor. | Device | Neurocritical patients |
Primary outcome
Neurological status
Time frame: Discharge out of the intensive care unit, averaged 2 weeks
Glasgow coma scale/Mortality
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 6 Jun 2018.
- BackgroundDiederik P. Kingma and Jimmy Lei Ba. Adam: A method for stochastic optimization. Conference paper at ICLR 2015.
- BackgroundMichael Unser and Akram Aldroubi. (1996 Apr) A review of wavelets in biomedical applications. Proceedings of the IEEE 84(4): 626-638.
- Background: PubMed 26017442LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015 May 28;521(7553):436-44. doi: 10.1038/nature14539.
- BackgroundChristopher Torrence and Gilbert P. Compo. (1998 Jan) A practical guide to wavelet analysis. Bulletin of the American Meteorological Society 79(1):61-78.
- BackgroundTheis, Fabian & Meyer-Base, Anke. (2010). Biomedical Signal Analysis - Contemporary Methods and Applications. Biomedical Signal Analysis: Contemporary Methods and Applications.
- Background: PubMed 27473064Min S, Lee B, Yoon S. Deep learning in bioinformatics. Brief Bioinform. 2017 Sep 1;18(5):851-869. doi: 10.1093/bib/bbw068.
- BackgroundYi Mao, Wenlin Chen, Yixin Chen, Chenyang Lu, Marin Kollef, and Thomas Bailey. (2012) An integrated data mining approach to real-time clinical monitoring and deterioration warning. Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining Pages 1140-1148. doi>10.1145/2339530.2339709
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Yi-Hsin Tsai, M.D. principal investigator
Far Eastern Memorial Hospital
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Brain Injuries, Acute, run by Far Eastern Memorial Hospital at 1 site in Taiwan.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 6 Jun 2018. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Taiwan. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Neurological status (time frame: Discharge out of the intensive care unit, averaged 2 weeks). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT03362346, which also lists 7 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 6 Jun 2018.