Creation of a Warehouse of Clinical Data and Physiological Signals at the Patient's Bedside
Conditions
Brief summary
The investigators wish to build up a database of clinical data and physiological signals with a view to developing a predictive algorithm based on continuous analysis of the intracranial pressure waveform and other parameters commonly used in intensive care to predict the occurrence of an episode of intracranial hypertension (HTIC). This algorithm will be designed using supervised learning statistical methods based on innovative statistical analysis methods (artificial intelligence). These methods are classically used to exploit massive data such as sensor data.
Interventions
None listed
Sponsors
University Hospital, Brest
Study design
Observational model
OTHER
Time perspective
OTHER
Eligibility
Sex/Gender
ALL
Age
18 Years to 99 Years
Healthy volunteers
No
Inclusion criteria
* More than 18 years * admission to intensive care for less than 3 days for a neurological lesion * Sensor placement for intracranial pressure monitoring
Exclusion criteria
* patient under judicial protection * refusal to participate * patients under 18 years of age
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Creation of a database of clinical data and physiological signals to develop a predictive algorithm based on continuous analysis of the intracranial pressure waveform to predict the occurrence of an episode of intracranial hypertension. | 30 minutes | Occurrence of an episode of intracranial hypertension defined as ICP \>20mmHg for 30 minutes |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Predict long-term functional outcome using intracranial pressure wave signal modeling and other parameters collected in real time | 28 days | Occurrence of vasospasm diagnosed by cerebral perfusion angioscanner |
Countries
France
Contacts
Primary ContactOlivier HUET
Outcome results
None listed