From Bench to Bedside: A Machine Learning Tool for the Detection of Inspiratory Leak
ConditionChronic Respiratory Failure
Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age18+
SponsorUniversity of Oslo
About this trial
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.
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
elective hospitalisation for control of non-invasive ventilation
use of ResMedLumis 100/150 ventilator
treatment for >3 months
Disqualifiers
current exacerbation
Trial population
Patients undergoing treatment with Lumis 100/150 for type 2 chronic resiratory failure
Trial design
Design model
Case-only
Time perspective
Prospective
Treatments tested in this trial
Not listed
Trial groups
No trial groups listed
Trial outcomes
Primary outcomes
1
Correct interpretation of inspiratory leak by machine learning tool
Measured in comparison with god standard method of polygraphy
Time frame
one yearSponsors and contacts
Click on the lead sponsor to view all of their trials.
University of Oslo
Lead sponsor
Oslo University Hospital
Collaborator
Source ClinicalTrials.gov