Pre-operative Characteristics for Prediction of Supraglottic Airway Failure Using Machine Learning (ERICA)

NCT ID: NCT06617403

Last Updated: 2024-09-27

Basic Information

• Recruitment Status: ACTIVE_NOT_RECRUITING
• Total Enrollment: 44000 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2022-12-01
• Study Completion Date: 2024-12-31

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 0.2-4.7% 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.

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 patient's 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.

Conditions

Anesthesia, General; Postoperative Complications; Laryngeal Masks; Treatment Failure

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: RETROSPECTIVE

Interventions

non

non

Intervention Type: OTHER

Eligibility Criteria

Inclusion Criteria

• Adult patients (≥18 years) receiving general anaesthesia for non-cardiac surgery with a supraglottic airway device

Exclusion Criteria

• None

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Technical University of Munich

OTHER

Sponsor Role: collaborator

University Hospital Ulm

OTHER

Sponsor Role: lead

Responsible Party

Flora Scheffenbichler

Dr. med.

Responsibility Role: PRINCIPAL_INVESTIGATOR

Locations

University Hospital Ulm

Ulm, Baden-Wurttemberg, Germany

Technical University Munich

Munich, Bavaria, Germany

Countries

Germany

Other Identifiers

ERICA

Identifier Type: -

Identifier Source: org_study_id