Recruiting NCT07428694
From Bench to Bedside: A Machine Learning Tool for the Detection of Inspiratory Leak
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- This is an observational study: the protocol does not assign a study treatment.
- Who it may be relevant to
- Registry conditions: Chronic Respiratory Failure. Basic parameters: from 18 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- Norway
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Overview
Study of the applicability of machine learning tools in detecting inspiratory leakage in longterm non-invasive ventilation. The study was conducted in two stages. Firstly the ML model was trained on both bench model created scenarios and then ten patients. And secondly the success of the model was assessed in a proof of concept pilot study of ten patients.
Primary outcome measures
- Correct interpretation of inspiratory leak by machine learning tool [Time frame: one year]
Eligibility criteria
Inclusion criteria
- elective hospitalisation for control of non-invasive ventilation
- use of ResMedLumis 100/150 ventilator
- treatment for >3 months
Exclusion criteria
- current exacerbation
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Observational model
- Case-only
Study locations
Norway · 1 center
- Oslo University Hospital — Oslo
Identifiers
NCT: NCT07428694 · 878631