Creation of a clinically annotated, multimodal biosignal database of endoscopy sedatio
Recruiting
- Conditions
- sedation in endoscopy
- Registration Number
- DRKS00030290
- Lead Sponsor
- niversitätsklinikum Halle (Saale)
- Brief Summary
Not available
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- Recruiting
- Sex
- All
- Target Recruitment
- 1272
Inclusion Criteria
Persons of full age who are capable of giving consent and who have undergone elective endoscopy under sedation and anticipated endoscopy duration of at least 20 minutes are included.
Exclusion Criteria
A known structural brain disease, cerebral neoplasia or cerebral metastatic malignancy, a known seizure disorder or impaired consciousness with Glagow Coma Scale < 15 are exclusion criteria. Furthermore, patients with profound hearing loss or allergies to the sedatives administered are excluded.
Study & Design
- Study Type
- observational
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method Creation of a clinically annotated, multimodal biosignal database of sedation in endoscopy [without endpoint].<br>Surrogate objective for the primary objective: Development of a valid, reliable and robust data-driven artificial intelligence model for EEG-based prediction of consciousness status and sedation depth [surrogate endpoint: AUC of consciousness status classification and PK score for clinical sedation depth classification].
- Secondary Outcome Measures
Name Time Method - Detection and prediction of safety-relevant clinical events, such as (Serious) Adverse Events and too deep sedation [Endpoint: AUCs to predict AEs and SAEs. Classification accuracy for detecting burst suppression patterns as signs of deep sedation]. <br>- Optimisation of known pEEG parameters<br>- Improved predictions of fluctuating depth of sedation<br>- Improved differentiation of shallow sedation depths<br>[Endpoint: AUC/PK values of classification of state of consciousness and clinical depth of sedation].<br>- Strengthening the robustness of pEEG parameters [Endpoint: AUC/PK values of the classification of consciousness status and clinical sedation depth as a function of the epochal artefact fraction].<br>- Further development of modern deep learning methods (with representation learning) and comparison with classical ML approaches (without representation learning) [Endpoint: AUC of classification of consciousness status and PK value for classification of clinical sedation depth].