Intelligent Lung Support in the Intensive Care Unit

NCT06595602 | Recruiting DMC

• 2 other identifiers: TUD-INTELL-086101057434
• Study Type: observational
• Target: 530
• Locations: 4 countries
• Sites: 5

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?

Conditions

Mechanical Ventilation; Intensive Care Medicine

Keywords

Invasive mechanical ventilation; Intensive care medicine; critical care medicine; artificial intelligence; decision support

Outcome Measures

Primary Outcomes (1)

• 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

Study Arms (1)

Invasively mechanically ventilated patients expected to stay ventilated for more than 24 h.

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

Interventions

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.

Invasively mechanically ventilated patients expected to stay ventilated for more than 24 h.

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Invasively mechanically ventilated, adult, acute respiratory distress syndrome (ARDS) and non-ARDS patients expected to stay intubated and ventilated for more than 24 hours.

You may qualify if:

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

You may not qualify if:

• 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

Sponsors & Collaborators

• Technische Universität Dresden: lead
• Critical Care Department, Parc Taulí Hospital Universitari, Institut d'Investigació I Innovació Parc Taulí (I3PT-CERCA), Sabadell, Spain: collaborator
• Department of Anaesthesiology and Intensive Care, National Medical Institute of the Ministry of Interior and Administration, Warsaw, Polan: collaborator
• Department of Intensive Care Medicine. Hospital Universitario de La Princesa. Universidad Autonoma de Madrid, Madrid, Spain: collaborator
• Department of Anesthesiology and Intensive Care Medicine, Pulmonary Engineering Group, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresde: collaborator
• Dipartimento di Scienze Chirurgiche e Diagnostiche Integrate, University of Genoa, Genoa, Italy: collaborator

Study Sites (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, 01307, Germany

RECRUITING

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

Genova, Italy

NOT YET RECRUITING

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

Warsaw, Poland

RECRUITING

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

Madrid, Spain

NOT YET RECRUITING

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

Sabadell, Spain

RECRUITING

Study Officials

• Jakob 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

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Jakob Wittenstein, M.D.

CONTACT

+49 351 458 19777; jakob.wittenstein@ukdd.de

Raphael Theilen

CONTACT

+49 351 458 19777; raphael.theilen@ukdd.de

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: September 5, 2024
• First Posted: September 19, 2024
• Study Start: May 25, 2025
• Primary Completion (Estimated): December 1, 2027
• Study Completion (Estimated): December 1, 2027
• Last Updated: April 17, 2026

Record last verified: 2026-04

Locations

PL (1); DE (1); IT (1); ES (2)