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

NCT ID: NCT06617403

Last Updated: 2024-09-27

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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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

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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

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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

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Anesthesia, General Postoperative Complications Laryngeal Masks Treatment Failure

Keywords

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"Neural Networks, Computer Artificial Intelligence"[Mesh] "Laryngeal Masks"[Mesh] "Treatment Failure"[Mesh] "Treatment Outcome"[Mesh] "Risk Factors"[Mesh]

Study Design

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Observational Model Type

COHORT

Study Time Perspective

RETROSPECTIVE

Interventions

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non

non

Intervention Type OTHER

Eligibility Criteria

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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

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Technical University of Munich

OTHER

Sponsor Role collaborator

University Hospital Ulm

OTHER

Sponsor Role lead

Responsible Party

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Flora Scheffenbichler

Dr. med.

Responsibility Role PRINCIPAL_INVESTIGATOR

Locations

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University Hospital Ulm

Ulm, Baden-Wurttemberg, Germany

Site Status

Technical University Munich

Munich, Bavaria, Germany

Site Status

Countries

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Germany

Other Identifiers

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ERICA

Identifier Type: -

Identifier Source: org_study_id