Ignite Creation Date:
2024-10-26 @ 3:41 PM
Last Modification Date:
2024-10-26 @ 3:41 PM
Study NCT ID:
NCT06617403
Status:
ACTIVE_NOT_RECRUITING
Last Update Posted:
None
First Post:
2024-09-25
Brief Title:
Pre-operative Characteristics for Prediction of Supraglottic Airway Failure Using Machine Learning ERICA
Sponsor:
None
Organization:
None
Study Overview
Official Title:
Can Pre-operative Characteristics Predict Failure of Supraglottic Airway to Tracheal Tube A Machine Learning Algorithm ERICA
Status:
ACTIVE_NOT_RECRUITING
Status Verified Date:
2024-09
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
No
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
ERICA
Brief Summary:
Supraglottic airway devices SGA are a safe and well-established technique for airway management Nowadays up to 60 of general anaesthetics performed in European countries use SGA In 02-47 SGA fail and require conversion to tracheal tubes
The ERICA study will use artificial intelligence methods to develop a model that can predict the risk of an unplanned SGA conversion based on pre-operative characteristics available during the premedication visit
The ERICA study will use artificial intelligence methods to develop a model that can predict the risk of an unplanned SGA conversion based on pre-operative characteristics available during the premedication visit
Detailed Description:
An intraoperative change of procedure not only leads to time delays but also time delays but also involves measures that are stressful for the patient such as deepening the anaesthesia and manipulating the airway again
Therefore the objective of ERICA is to develop a machine learning algorithm based on preoperative information 1 that can accurately predict the risk of an unplanned SGA conversion and 2 identifies characteristics leading to conversion from SGA to tracheal tube
I Developing the model
The final dataset will be split in a training testing and validation cohort Five models will be created to predict intraoperative conversion from SGA to tracheal tube including generalized linear models GLM deep learning distributed random forest DRF xgboost and gradient boosting machine GBM Then a stacked ensemble model will be constructed through combination of the five models Finally the best artificial intelligence model will be chosen
II Identify characteristics leading to the airway conversion and categorisation
Intraoperative changes of the patients position can alter the risk of conversion therefore operations with positional changes should be considered
Identify patient- and procedure-dependent characteristics that lead to conversion from SGA to tracheal tube and their importance
Therefore the objective of ERICA is to develop a machine learning algorithm based on preoperative information 1 that can accurately predict the risk of an unplanned SGA conversion and 2 identifies characteristics leading to conversion from SGA to tracheal tube
I Developing the model
The final dataset will be split in a training testing and validation cohort Five models will be created to predict intraoperative conversion from SGA to tracheal tube including generalized linear models GLM deep learning distributed random forest DRF xgboost and gradient boosting machine GBM Then a stacked ensemble model will be constructed through combination of the five models Finally the best artificial intelligence model will be chosen
II Identify characteristics leading to the airway conversion and categorisation
Intraoperative changes of the patients position can alter the risk of conversion therefore operations with positional changes should be considered
Identify patient- and procedure-dependent characteristics that lead to conversion from SGA to tracheal tube and their importance
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
None
Is a FDA Regulated Device?:
None
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None