Pre-operative Characteristics for Prediction of Supraglottic Airway Failure Using Machine Learning (ERICA)
ERICA
Can Pre-operative Characteristics Predict Failure of Supraglottic Airway to Tracheal Tube? A Machine Learning Algorithm (ERICA)
1 other identifier
observational
44,000
1 country
2
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.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P75+ for all trials
Started Dec 2022
Typical duration for all trials
2 active sites
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
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Study Timeline
Key milestones and dates
Study Start
First participant enrolled
December 1, 2022
CompletedFirst Submitted
Initial submission to the registry
September 25, 2024
CompletedFirst Posted
Study publicly available on registry
September 27, 2024
CompletedPrimary Completion
Last participant's last visit for primary outcome
November 30, 2024
CompletedStudy Completion
Last participant's last visit for all outcomes
December 31, 2024
CompletedSeptember 27, 2024
September 1, 2024
2 years
September 25, 2024
September 25, 2024
Conditions
Keywords
Outcome Measures
Primary Outcomes (1)
Risk of unplanned SGA conversion
intraoperative
Interventions
non
Eligibility Criteria
Adult patients (≥18 years) receiving non-cardiac surgery using a supraglottic airway device
You may qualify if:
- Adult patients (≥18 years) receiving general anaesthesia for non-cardiac surgery with a supraglottic airway device
You may not qualify if:
- None
Contact the study team to confirm eligibility.
Sponsors & Collaborators
- University Hospital Ulmlead
- Technical University of Munichcollaborator
Study Sites (2)
University Hospital Ulm
Ulm, Baden-Wurttemberg, 89073, Germany
Technical University Munich
Munich, Bavaria, 81675, Germany
MeSH Terms
Conditions
Condition Hierarchy (Ancestors)
Study Design
- Study Type
- observational
- Observational Model
- COHORT
- Time Perspective
- RETROSPECTIVE
- Sponsor Type
- OTHER
- Responsible Party
- PRINCIPAL INVESTIGATOR
- PI Title
- Dr. med.
Study Record Dates
First Submitted
September 25, 2024
First Posted
September 27, 2024
Study Start
December 1, 2022
Primary Completion
November 30, 2024
Study Completion
December 31, 2024
Last Updated
September 27, 2024
Record last verified: 2024-09