Intensive Care Medicine, Mechanical Ventilation
Conditions
Keywords
Invasive mechanical ventilation, Intensive care medicine, critical care medicine, artificial intelligence, decision support
Brief summary
The aim of this observational study is to test the IntelliLung decision support system based on artificial intelligence. This system is intended to help to set the ventilator. The study includes patients with and without ARDS (acute respiratory distress syndrome) who are receiving invasive mechanical ventilation, as well as patients with additional extracorporeal lung support. The study will be conducted in several centers. The main question of the study: How well do the mechanical ventilation settings of healthcare staff match the recommendations of the IntelliLung system?
Interventions
DEVICEArtificial intelligence based decision support system (AI-DSS); software
The device is intended for monitoring and recommending ventilator settings, ventilation mode to qualified Intensive Care Unit (ICU) health care professionals (HCP). This is for medical indications that require invasive mechanical ventilation of the respiratory system in the ICU under international / EU guidelines. The device receives clinical data via the ICU's data integration platform that includes patient physical and demographic data as well as current vital signs, ventilation parameters, blood gas analysis, general blood laboratory reports, fluid balance and medication. Prediction models based on artificial intelligence algorithms are used to deduce therapy suggestions from received data. The algorithm is carried out on a secured cloud platform.
Sponsors
Technische Universität Dresden
Dipartimento di Scienze Chirurgiche e Diagnostiche Integrate, University of Genoa, Genoa, Italy
Critical Care Department, Parc Taulí Hospital Universitari, Institut d'Investigació I Innovació Parc Taulí (I3PT-CERCA), Sabadell, Spain
Department of Anaesthesiology and Intensive Care, National Medical Institute of the Ministry of Interior and Administration, Warsaw, Polan
Department of Intensive Care Medicine. Hospital Universitario de La Princesa. Universidad Autonoma de Madrid, Madrid, Spain
Department of Anesthesiology and Intensive Care Medicine, Pulmonary Engineering Group, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresde
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
1. Male and female patients, age ⪰18 years 2. Written informed consent 3. Invasively mechanically ventilated patients expected to be intubated for more than 24 hours.
Exclusion criteria
1. Expected to die within ≤48 hours 2. Participation in an interventional mechanical ventilation trial 3. Mechanical Ventilation with a closed-loop ventilation mode 4. Persons dependent on the sponsor and/or investigator 5. Subjects who are currently imprisoned or otherwise in confinement ordered by law or other official authorities
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| Relative time of same device settings of healthcare provider and IntelliLung AI-DSS related to the total IntelliLung AI-DSS running time | From enrollment to discharge from the intensive care unit or successful weaning from the ventilator, assessed up to 180 days |
Countries
Germany, Italy, Poland, Spain
Contacts
CONTACTJakob Wittenstein, M.D.
CONTACTRaphael Theilen
PRINCIPAL_INVESTIGATORJakob Wittenstein
epartment of Anesthesiology and Intensive Care Medicine, Pulmonary Engineering Group, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
Outcome results
None listed