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

Retrospective Use of Patient Treatment Data for the Evaluation and Further Development of an Artificial Intelligence-based Algorithm for Clinical Decision Support in Invasive Mechanical Ventilation of Intensive Care Patients

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
ClinicalTrials.gov
Registry ID
NCT05668637
Acronym
IntelliLung
Enrollment
318542
Registered
2022-12-30
Start date
2023-01-01
Completion date
2026-09-01
Last updated
2026-04-21

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Invasive Mechanical Ventilation

Keywords

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

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.

Interventions

OTHERArtificial Intelligence-based Decision support

Decision support to optimise invasive mechanical ventilation settings

Sponsors

Technische Universität Dresden
Lead SponsorOTHER

Study design

Observational model
OTHER
Time perspective
RETROSPECTIVE

Eligibility

Sex/Gender
ALL
Age
18 Years to No maximum

Inclusion criteria

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

Exclusion criteria

• Patients receiving one-lung ventilation

Design outcomes

Primary

MeasureTime frame
Relative time of same device settings of the health care provider and the IntelliLung algorithmFrom date of intubation to date of extubation or date of discharge, which ever came first, assessed up to 12 month

Countries

Germany, Serbia, Spain, Switzerland, United States

Contacts

CONTACTJakob Wittenstein, MD
jakob.wittenstein@ukdd.de+49 351 458 19887
CONTACTThea Koch, PhD
thea.koch@ukdd.de
PRINCIPAL_INVESTIGATORJakob Wittenstein, MD

University Hospital Carl Gustav Carus at Technischen Universität Dresden, Germany

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Apr 22, 2026