RecruitingNCT06595602

Intelligent Lung Support in the Intensive Care Unit

Intelligent Lung Support in the Intensive Care Unit (IntelliLung): An Observational, Prospective, Multicentre Study

Sponsor

Technische Universität Dresden

Enrollment

530 participants

Start Date

May 25, 2025

Study Type

OBSERVATIONAL

Conditions

Intensive Care Medicine Mechanical Ventilation

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?

Eligibility

Min Age: 18 Years

Inclusion Criteria3

Male and female patients, age ⪰18 years
Written informed consent
Invasively mechanically ventilated patients expected to be intubated for more than 24 hours.

Exclusion Criteria5

Expected to die within ≤48 hours
Participation in an interventional mechanical ventilation trial
Mechanical Ventilation with a closed-loop ventilation mode
Persons dependent on the sponsor and/or investigator
Subjects who are currently imprisoned or otherwise in confinement ordered by law or other official authorities

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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.

Locations(5)

Department 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

Dresden, Germany

Dipartimento di Scienze Chirurgiche e Diagnostiche Integrate, University of Genoa, Genoa, Italy

Genova, Italy

Department of Anaesthesiology and Intensive Care, National Medical Institute of the Ministry of Interior and Administration, Warsaw, Poland

Warsaw, Poland

Department of Intensive Care Medicine. Hospital Universitario de La Princesa. Universidad Autonoma de Madrid, Madrid, Spain

Madrid, Spain

Critical Care Department, Parc Taulí Hospital Universitari, Institut d'Investigació I Innovació Parc Taulí (I3PT-CERCA), Sabadell, Spain

Sabadell, Spain

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