Evidence map›Paper›PMID 42778255›Full record

Observational studyBMJ open2026

Intelligent Lung Support in the Intensive Care Unit (IntelliLung): study protocol for an international observational, prospective, multicentre study.

Raphael Theilen, Robert Huhle, Martin Scharffenberg, Franziska Fischer, Tim Kramer, Thea Koch, Lorenzo Ball, Lluis Blanch, Leonardo Sarlabous, Fernando Suárez-Sipmann and 18 more

Registry-linked trialAbstract readObservational StudyMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Observational study in BMJ open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06595602 (Intelligent Lung Support in the Intensive Care Unit), which is not on this map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT06595602 recruitingnot on this map

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

TypeobservationalSponsorTechnische Universität DresdenRan2025 to 2027Enrolled530ConditionsMechanical Ventilation, Intensive Care MedicineArmsArtificial intelligence based decision support system (AI-DSS), software
3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

28 authors.

Raphael TheilenDepartment 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.
Robert HuhleDepartment 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.
Martin ScharffenbergDepartment 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.
Franziska FischerDepartment 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.
Tim KramerDepartment 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.
Thea KochDepartment 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.
Lorenzo BallAnesthesia and Critical Care, San Martino Policlinico Hospital, Genoa, Italy.
Lluis BlanchCritical Care Department, Parc Taulí Hospital Universitari, Institut d'Investigació I Innovació Parc Taulí (I3PT-CERCA), Sabadell, Spain.
Leonardo SarlabousCritical Care Department, Parc Taulí Hospital Universitari, Institut d'Investigació I Innovació Parc Taulí (I3PT-CERCA), Sabadell, Spain.
Fernando Suárez-SipmannCentro Investigación Biomédica en Red de Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain.ORCID http://orcid.org/0000-0002-7412-2970
Konstanty SzuldrzynskiDepartment of Anaesthesiology and Intensive Care, National Medical Institute Ministry of Interior and Administration, Warsaw, Poland.
Milosz JankowskiDepartment of Anaesthesiology and Intensive Care, National Medical Institute Ministry of Interior and Administration, Warsaw, Poland.
Julia KalinkaCoordination Centre for Clinical Trials, Faculty of Medicine Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany.
Evelyn TripsCoordination Centre for Clinical Trials, Faculty of Medicine Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany.
Barbara DjawidCoordination Centre for Clinical Trials, Faculty of Medicine Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany.
Sarah TsurkanElse Kroener Fresenius Center for Digital Health, TUD Dresden University of Technology, Dresden, Germany.
Valentina JanevInstitute Mihajlo Pupin, University of Belgrade, Belgrade, Serbia.
Miloš NenadovićInstitute Mihajlo Pupin, University of Belgrade, Belgrade, Serbia.
Dejan PaunovićInstitute Mihajlo Pupin, University of Belgrade, Belgrade, Serbia.
Sahar VahdatiNature-Inspired Machine Intelligence Group, Institute for Applied Informatics (InfAI), Dresden, Germany.
Jaume M CastellsBetter Care SL Head of Engineering & Company Co-Founder, Better Care, Sabadelle, Spain.
Andrea KurzRector, Medical University of Graz, Graz, Austria.
Ahmed HallawaDepartment of Information Theory and Data Analytics, RWTH Aachen University, Aachen, Germany.
Ary Serpa NetoAustralian and New Zealand Intensive Care Research Centre (ANZIC-RC), School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.
Marcus J SchultzIntensive Care, Amsterdam University Medical Centers, Amsterdam, Netherlands.
Marcelo Gama de AbreuDepartment 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.
Jakob WittensteinDepartment 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 Jakob.wittenstein@ukdd.de.ORCID http://orcid.org/0000-0003-4397-1467
IntelliLung Consortium

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionMechanical ventilation (MV) is lifesaving in the intensive care unit (ICU) but can cause complications if not individualised according to the patient's needs. Artificial intelligence (AI)-driven decision support systems (AI-DSS) may theoretically optimise MV settings. This international observational, prospective, multicentre study aims to validate the IntelliLung AI-DSS in real clinical environments. METHODS AND ANALYSIS: In this study, patients aged ≥18 years requiring invasive MV for >24 hours are included. The primary objective is to evaluate the agreement between IntelliLung AI-DSS MV recommendations and the ventilator settings implemented by healthcare providers. The IntelliLung AI-DSS continuously analyses patient-specific data, including respiratory mechanics and gas exchange, to recommend optimal MV parameters. The primary endpoints are the relative time of matching ventilator settings for each (1) positive end-expiratory pressure, (2) fraction of inspired oxygen, (3) respiratory rate and (4) tidal volume during volume-controlled ventilation or inspiratory pressure (Pinsp) during pressure-controlled ventilation. Secondary endpoints include assessments of ventilator-free days and clinical decision-making practices. Patient-centred outcomes, such as quality of life and psychological stress, are also evaluated. Data collection spans ICU stay and follow-up at 30 and 180 days after enrolment. This trial is the first to validate the IntelliLung AI-DSS in a prospective, real-world clinical setting by comparing recommendations given by the IntelliLung AI-DSS to local standards of care. The results of the trial will serve as a foundation for future interventional studies to assess the IntelliLung AI-DSS impact on patient outcomes and ICU workflows. The study addresses a critical gap in the application of AI to intensive care, advancing personalised and evidence-based MV management. ETHICS AND DISSEMINATION: The TUD Medical Faculty Ethical Committee for clinical research approved the study on 4 November 2024 (File number Mono-EK-27907202). Additionally, the institutional review board at Sabadell, Madrid and Warsaw approved the study. IntelliLung is designed in accordance with the principles of the Declaration of Helsinki. The final main results will be published in a highly ranked, peer-reviewed scientific journal taking into account the recommendations of the International Committee of Medical Journal Editors. TRIAL REGISTRATION NUMBER: NCT06595602.

Indexed as

Artificial IntelligenceDecision Support Systems, ClinicalIntensive Care UnitsRespiration, ArtificialCritical CareHumansProspective StudiesResearch DesignAdult intensive & critical careArtificial IntelligenceIntensive Care Units

Identifiers

PMID42778255
PMCPMC13630060

What OpenQuestion holds

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Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.