Circulatory FailureAI4ICU-ObsNCT06814327
Assessment of Organ Failure Risk Predictions in ICU
An observational study of Circulatory Failure and Respiratory Failure, run by ETH Zurich at 1 site in Switzerland.
Trial summary
During this observational study, the investigators aim to assess the ability of ICU clinicians to predict the risk of impending organ failure and retrospectively compare it to the performance of previously published machine learning models. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Circulatory Failure, Respiratory Failure, Renal Failure
- How many people
- 499 participants
- Where
- 1 siteSwitzerland
- Study dates
- 18 Nov 2024to 15 May 2026
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Adult Intensive Care Unit at the University Hospital Bern
Who may be able to join
- patient minimum age of 18 years
- emergency admission to the ICU
- arterial line in place
Who cannot join
- documented refusal (on the general consent form) to participate to clinical research
- patients with neurologic conditions that impair the patient's level of consciousness (including, but not limited to stroke, traumatic brain injury, intracranial hemorrhage, CNS infections; except polytrauma)
- patients on mechanical circulatory support systems (IABP, VA-ECMO, Impella, VAD) or extracorporeal membrane oxygenation (VV-ECMO) at any time during their ICU stay;
- patients receiving end-of-life care or are admitted for the sole purpose of evaluating organ donation
Trial locations and contacts
1 site in 1 country.
Switzerland1 site
University Hospital Inselspital, Berne
Bern, Canton of Bern, 3010
About this study
Official title: Prospective Assessment of Risk Predictions of Organ Failure in the Intensive Care Unit - Comparing Accuracy of Human and AI Risk Predictions
During this observational study, the investigators aim to assess the ability of ICU clinicians to predict the risk of impending organ failure and retrospectively compare it to the performance of previously published machine learning models. The central hypothesis of this study is that the treating physician can predict impending organ failure in adult ICU patients with similar accuracy as the best previously publishes machine learning models.
Read the detailed description
In this observational study, clinician's (physicians and nurses) assessment of the estimated imminent organ failure risk in an ICU setting are prospectively collected. Circulatory failure is investigated in the primary objective, and respiratory failure, renal failure, and mortality are investigated in secondary objectives. These assessments investigate the predictive performance and influencing factors for clinician prediction. The assessments will be collected in questionnaires and be performed by the clinicians directly involved in the patient treatment and by clinicians who are not actively responsible for the patient treatment. Furthermore, this study aims to benchmark these risk assessments made by healthcare professionals against retrospectively generated AI risk scores for the same patients and timepoints. The AI risk scores will be calculated retrospectively from a set of models from a systematic search of the current literature. The AI models that will be employed for this analysis will be identified as indicated by a systematic review protocol and must satisfy the following two criteria: they do not require any data beyond what is routinely collected during an ICU stay and may be accessed as open source. Such a comparison is vital for the understanding of the relative accuracy and reliability of AI-based predictions in the context of organ failure risk compared to human performance. The data and findings from this study are anticipated to provide evidence for the clinical utility of AI-based risk scores and pave the way for future research into the optimization of AI systems for healthcare applications.
- Conditions
- Circulatory Failure, Respiratory Failure, Renal Failure
- Keywords
- Machine learning, Intensive care, artificial intelligence
- Medical subject headings
- Shock, Respiratory Insufficiency, Renal Insufficiency
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Adult ICU patients | Adult ICU patients |
What is being measured
The primary outcome is the main question the study is designed to answer.
Clinician prediction of circulatory failure within 8 hours compared to published ML models
This outcome compares the area under the receiver operating characteristic curve (auROC) for two methods of predicting circulatory failure within 8 hours of each assessment time point: (1) ICU clinicians' risk estimates, and (2) previously published machine learning (ML) models applied retrospectively. For each assessment, we compute the auROC separately for clinicians and for the ML model for the same time points and patients. The difference in auROC (clinician minus ML) is the main measure of interest, evaluated under a non-inferiority framework with a margin of 0.025.
Measured: Assessments are collected within the first 72 hours following admission.
Secondary outcomes (1)
Clinician prediction of respiratory failure within 24 hours compared to published ML models
This outcome compares the area under the receiver operating characteristic curve (auROC) for two methods of predicting respiratory failure within 24 hours of each assessment time point: (1) ICU clinicians' risk estimates, and (2) previously published machine learning (ML) models applied retrospectively. For each assessment, we compute the auROC separately for clinicians and for the ML model. The difference in auROCs (clinician minus ML) is the main measure of interest, evaluated using the same methodological framework as the primary outcome.
Measured: Assessments are collected within the first 72 hours following admission.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted7 Feb 2025
- Study start18 Nov 2024
- Primary completion15 May 2025
- Study completion15 May 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Circulatory FailureStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 499 participants (actual)
Sponsor and investigators
- Lead sponsor
- ETH Zurich
- Sponsor type
- University, hospital or other
- Collaborators
- Insel Gruppe AG, University Hospital Bern
- Responsible party
- Sponsor
- Investigators
- Martin Faltys, Dr. med., Principal investigator, Insel Gruppe AG, University Hospital Bern
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06814327
- Sponsor's protocol ID
- BASEC-01046
- Acronym
- AI4ICU-Obs
- Status last verified
- Nov 2024
- First submitted
- 18 Nov 2024
- Last updated
- 28 Aug 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 28 Aug 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Prospective Assessment of Risk Predictions of Organ Failure in the Intensive Care Unit - Comparing Accuracy of Human and AI Risk Predictions
- ClinicalTrials.gov ID
- NCT06814327
- Acronym / sponsor ID
- AI4ICU-Obs / BASEC-01046
- Conditions
- Circulatory Failure, Respiratory Failure, Renal Failure
- Keywords
- Machine learning, Intensive care, artificial intelligence
- Study type
- Observational
- Recruitment status
- Active, not recruiting
- Enrolment
- 499 participants (actual)
- Lead sponsor
- ETH ZurichUniversity, hospital or other
- Collaborators
- Insel Gruppe AG, University Hospital Bern
- Study start
- 18 Nov 2024 (actual)
- Primary completion
- 15 May 2025 (actual)
- Study completion
- 15 May 2026 (estimated)
- First posted
- 7 Feb 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
During this observational study, the investigators aim to assess the ability of ICU clinicians to predict the risk of impending organ failure and retrospectively compare it to the performance of previously published machine learning models. The central hypothesis of this study is that the treating physician can predict impending organ failure in adult ICU patients with similar accuracy as the best previously publishes machine learning models.
Detailed description
In this observational study, clinician's (physicians and nurses) assessment of the estimated imminent organ failure risk in an ICU setting are prospectively collected. Circulatory failure is investigated in the primary objective, and respiratory failure, renal failure, and mortality are investigated in secondary objectives. These assessments investigate the predictive performance and influencing factors for clinician prediction. The assessments will be collected in questionnaires and be performed by the clinicians directly involved in the patient treatment and by clinicians who are not actively responsible for the patient treatment. Furthermore, this study aims to benchmark these risk assessments made by healthcare professionals against retrospectively generated AI risk scores for the same patients and timepoints. The AI risk scores will be calculated retrospectively from a set of models from a systematic search of the current literature. The AI models that will be employed for this analysis will be identified as indicated by a systematic review protocol and must satisfy the following two criteria: they do not require any data beyond what is routinely collected during an ICU stay and may be accessed as open source. Such a comparison is vital for the understanding of the relative accuracy and reliability of AI-based predictions in the context of organ failure risk compared to human performance. The data and findings from this study are anticipated to provide evidence for the clinical utility of AI-based risk scores and pave the way for future research into the optimization of AI systems for healthcare applications.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Adult ICU patients | – | –Adult ICU patients |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Adult Intensive Care Unit at the University Hospital Bern
Key inclusion criteria (3 listed)
- patient minimum age of 18 years
- emergency admission to the ICU
- arterial line in place
Key exclusion criteria (4 listed)
- documented refusal (on the general consent form) to participate to clinical research
- patients with neurologic conditions that impair the patient's level of consciousness (including, but not limited to stroke, traumatic brain injury, intracranial hemorrhage, CNS infections; except polytrauma)
- patients on mechanical circulatory support systems (IABP, VA-ECMO, Impella, VAD) or extracorporeal membrane oxygenation (VV-ECMO) at any time during their ICU stay;
- patients receiving end-of-life care or are admitted for the sole purpose of evaluating organ donation
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Clinician prediction of circulatory failure within 8 hours compared to published ML models
Time frame: Assessments are collected within the first 72 hours following admission.
This outcome compares the area under the receiver operating characteristic curve (auROC) for two methods of predicting circulatory failure within 8 hours of each assessment time point: (1) ICU clinicians' risk estimates, and (2) previously published machine learning (ML) models applied retrospectively. For each assessment, we compute the auROC separately for clinicians and for the ML model for the same time points and patients. The difference in auROC (clinician minus ML) is the main measure of interest, evaluated under a non-inferiority framework with a margin of 0.025.
1 secondary outcome
Clinician prediction of respiratory failure within 24 hours compared to published ML models
Time frame: Assessments are collected within the first 72 hours following admission.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 28 Aug 2025.
- Individual participant dataSharing planned: Undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Martin Faltys, Dr. med. principal investigator
Insel Gruppe AG, University Hospital Bern
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Circulatory Failure and Respiratory Failure, run by ETH Zurich at 1 site in Switzerland.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: under way, not looking for participants, as of the sponsor's last update on 28 Aug 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Switzerland. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Clinician prediction of circulatory failure within 8 hours compared to published ML models (time frame: Assessments are collected within the first 72 hours following admission.). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 28 Aug 2025.