Evaluation and Further Development of an Artificial Intelligence-based Algorithm for Clinical Decision Support

NCT05668637 | Recruiting

• 1 other identifier: TUD-IntelliLung-Study-A
• Study Type: observational
• Target: 318,542
• Locations: 5 countries
• Sites: 8

Brief Summary

Invasive mechanical ventilation is one of the most important and life-saving therapies in the intensive care unit (ICU). In most severe cases, extracorporeal lung support is initiated when mechanical ventilation is insufficient. However, mechanical ventilation is recognised as potentially harmful, because inappropriate mechanical ventilation settings in ICU patients are associated with organ damage, contributing to disease burden. Studies revealed that mechanical ventilation is often not provided adequately despite clear evidence and guidelines. Variables at the ventilator and extracorporeal lung support device can be set automatically using optimization functions and clinical recommendations, but the handling of experts may still deviate from those settings depending upon the clinical characteristics of individual patients. Artificial intelligence can be used to learn from those deviations as well as the patient's condition in an attempt to improve the combination of settings and accomplish lung support with reduced risk of damage.

Conditions

Invasive Mechanical Ventilation

Keywords

Mechanical ventilation; ventilator-induced lung injury; ARDS; artificial intelligence

Outcome Measures

Primary Outcomes (1)

• Relative time of same device settings of the health care provider and the IntelliLung algorithm

  From date of intubation to date of extubation or date of discharge, which ever came first, assessed up to 12 month

Interventions

Artificial Intelligence-based Decision support OTHER

Decision support to optimise invasive mechanical ventilation settings

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Intensive care patients receiving invasive mechanical ventilation

You may qualify if:

• Subjects who are 18 years or older and receive invasive mechanical ventilation for \> 4 hours

You may not qualify if:

• Patients receiving one-lung ventilation

Sponsors & Collaborators

• Technische Universität Dresden: lead

Study Sites (8)

Cleveland Clinic Foundation, Cleveland, USA

Cleveland, Ohio, 44195, United States

RECRUITING

University Hospital Carl Gustav Carus Dresden

Dresden, Germany

RECRUITING

Institut Fur Angewandte Informatik (Infai) Ev

Leipzig, Germany

ACTIVE NOT RECRUITING

Institut Mihajlo Pupin

Belgrade, Serbia

ACTIVE NOT RECRUITING

Better Care Sl

Sabadell, Spain

ACTIVE NOT RECRUITING

Fundacio Parc Tauli

Sabadell, Spain

COMPLETED

Fundacion Publica Andaluza Progreso Y Salud

Seville, Spain

ACTIVE NOT RECRUITING

Inselspital, Universitätsspital Bern

Bern, Switzerland

COMPLETED

MeSH Terms

Conditions

Ventilator-Induced Lung Injury

Condition Hierarchy (Ancestors)

Lung Injury; Lung Diseases; Respiratory Tract Diseases

Study Officials

• Jakob Wittenstein, MD

  University Hospital Carl Gustav Carus at Technischen Universität Dresden, Germany

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Jakob Wittenstein, MD

CONTACT

+49 351 458 19887; jakob.wittenstein@ukdd.de

Thea Koch, PhD

CONTACT

thea.koch@ukdd.de

Study Design

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

Study Record Dates

• First Submitted: December 9, 2022
• First Posted: December 30, 2022
• Study Start: January 1, 2023
• Primary Completion (Estimated): September 1, 2026
• Study Completion (Estimated): September 1, 2026
• Last Updated: April 21, 2026

Record last verified: 2025-09

Locations

US (1); ES (3); CH (1); SE (1); DE (2)