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

NCT06617403 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 44000

Last updated 2026-05-13

No results posted yet for this study

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.

Conditions

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

Interventions

OTHER

non

non

Sponsors & Collaborators

  • Technical University of Munich

    collaborator OTHER

  • University Hospital Ulm

    lead OTHER

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2022-12-01
Primary Completion
2024-11-30
Completion
2024-12-31

Countries

  • Germany

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06617403 on ClinicalTrials.gov