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NCT Number: NCT06595602

Intelligent Lung Support in the Intensive Care Unit

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?

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Key information

Conditions

Mechanical Ventilation Intensive Care Medicine

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

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

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

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

Exclusion criteria

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

Treatment and study plan

Artificial intelligence based decision support system (AI-DSS); software

Device

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.

Primary outcomes

Relative time of same device settings of healthcare provider and IntelliLung AI-DSS related to the total IntelliLung AI-DSS running time

Time frame: From enrollment to discharge from the intensive care unit or successful weaning from the ventilator, assessed up to 180 days

Study contacts

Contact information is provided by the study sponsor or research team.

Jakob Wittenstein, M.D.

CONTACT

[email protected]

+49 351 458 19777

Raphael Theilen

CONTACT

[email protected]

+49 351 458 19777

Sponsors and collaborators

Lead sponsor

Technische Universität Dresden

Other

Collaborators

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 Anesthesiology and Intensive Care Medicine, Pulmonary Engineering Group, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresde
Department of Intensive Care Medicine. Hospital Universitario de La Princesa. Universidad Autonoma de Madrid, Madrid, Spain
Dipartimento di Scienze Chirurgiche e Diagnostiche Integrate, University of Genoa, Genoa, Italy

Registry information

Official study title

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

Acronym: IntelliLung

Important dates

Study start: 2025
Primary completion: 2027
Study completion: 2027
First posted: Sep 19, 2024
Registry last updated: Jul 2, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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