Anesthesia, GeneralERICANCT06617403
Pre-operative Characteristics for Prediction of Supraglottic Airway Failure Using Machine Learning (ERICA)
An observational study of Anesthesia, General and Postoperative Complications, run by University Hospital Ulm at 2 sites in Germany.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Anesthesia, General, Postoperative Complications, Laryngeal Masks
- What is being tested?
- non
- How many people
- 44,000 participants
- Where
- 2 sitesGermany
- Study dates
- 1 Dec 2022to 31 Dec 2024
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Adult patients (≥18 years) receiving non-cardiac surgery using a supraglottic airway device
Who may be able to join
- Adult patients (≥18 years) receiving general anaesthesia for non-cardiac surgery with a supraglottic airway device
Who cannot join
- None
Trial locations and contacts
2 sites in 1 country.
Germany2 sites
Technical University Munich
Munich, Bavaria, 81675
University Hospital Ulm
Ulm, Baden-Wurttemberg, 89073
About this study
Official title: Can Pre-operative Characteristics Predict Failure of Supraglottic Airway to Tracheal Tube? A Machine Learning Algorithm (ERICA)
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.
Read the 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.
- 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.
- Being tested
- non
- Keywords
- "Neural Networks, Computer Artificial Intelligence"[Mesh], "Laryngeal Masks"[Mesh], "Treatment Failure"[Mesh], "Treatment Outcome"[Mesh], "Risk Factors"[Mesh]
- Medical subject headings
- Postoperative Complications
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
nonOther
non
What is being measured
The primary outcome is the main question the study is designed to answer.
Risk of unplanned SGA conversion
Measured: intraoperative
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted27 Sep 2024
- Study start1 Dec 2022
- Primary completion30 Nov 2024
- Study completion31 Dec 2024
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Anesthesia, GeneralStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 44,000 participants (actual)
Sponsor and investigators
- Lead sponsor
- University Hospital Ulm
- Sponsor type
- University, hospital or other
- Collaborators
- Technical University of Munich
- Responsible party
- Principal investigator, Flora Scheffenbichler, University Hospital Ulm
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06617403
- Sponsor's protocol ID
- ERICA
- Acronym
- ERICA
- Status last verified
- Sep 2024
- First submitted
- 25 Sep 2024
- Last updated
- 13 May 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 13 May 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Can Pre-operative Characteristics Predict Failure of Supraglottic Airway to Tracheal Tube? A Machine Learning Algorithm (ERICA)
- ClinicalTrials.gov ID
- NCT06617403
- Acronym / sponsor ID
- ERICA / ERICA
- Keywords
- "Neural Networks, Computer Artificial Intelligence"[Mesh], "Laryngeal Masks"[Mesh], "Treatment Failure"[Mesh], "Treatment Outcome"[Mesh], "Risk Factors"[Mesh]
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 44,000 participants (actual)
- Lead sponsor
- University Hospital UlmUniversity, hospital or other
- Collaborators
- Technical University of Munich
- Study start
- 1 Dec 2022 (actual)
- Primary completion
- 30 Nov 2024 (actual)
- Study completion
- 31 Dec 2024 (actual)
- First posted
- 27 Sep 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
- 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.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Adult patients (≥18 years) receiving non-cardiac surgery using a supraglottic airway device
Key inclusion criteria (1 listed)
- Adult patients (≥18 years) receiving general anaesthesia for non-cardiac surgery with a supraglottic airway device
Key exclusion criteria (1 listed)
- None
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| nonnon | Other | – |
Primary outcome
Risk of unplanned SGA conversion
Time frame: intraoperative
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 13 May 2026.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Anesthesia, General and Postoperative Complications, run by University Hospital Ulm at 2 sites in Germany.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 13 May 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites in Germany. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Risk of unplanned SGA conversion (time frame: intraoperative). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 13 May 2026.