CoagulopathyROTEMAINCT07502950
ROTEM Interpretation AI vs Experts
An observational study of Coagulopathy and Thromboelastography, run by Masarykova Nemocnice v Usti nad Labem, Krajska Zdravotni a.s..
Trial summary
This prospective multicenter observational study aims to evaluate the agreement between artificial intelligence (AI)-based interpretation and expert interpretation of rotational thromboelastometry (ROTEM) findings in clinically relevant settings. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Coagulopathy, Thromboelastography, Agreement
- What is being tested?
- Large Language Model (LLM) artificial intelligence assesment
- How many people
- 400 participants
- Where
- No sites listed yet
- Study dates
- 1 Sep 2026to 1 Jul 2027
- 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: The study population will consist of adult patients undergoing rotational thromboelastometry (ROTEM) as part of routine clinical care in participating centers. Patients will be recruited from perioperative and intensive care settings, including major surgery, trauma, and critical illness, where assessment of coagulation status is clinically indicated. This study reflects real-world practice, with no modification of standard care. The unit of analysis will be individual ROTEM measurements rather than individual patients, as repeated measurements may occur during clinical management. Only ROTEM datasets with complete graphical and numerical outputs and meeting predefined quality criteria will be included.
Who may be able to join
- Adult patients (age ≥18 years)
- ROTEM analysis performed using a ROTEM Sigma device as part of routine clinical care
- Availability of complete ROTEM output (graphical and numerical data)
- ROTEM measurement obtained in a clinical context where assessment of coagulation status is indicated (e.g., perioperative setting, trauma, or critical illness)
Who cannot join
- ROTEM measurements performed using the HEPTEM channel
- Incomplete or missing ROTEM data preventing standardized evaluation
- ROTEM measurements obtained under non-standardized or technically unreliable conditions
About this study
Official title: Artificial Intelligence Versus Expert Interpretation of ROTEM: A Prospective Study of Agreement and Clinical Decision-Making
This prospective multicenter observational study aims to evaluate the agreement between artificial intelligence (AI)-based interpretation and expert interpretation of rotational thromboelastometry (ROTEM) findings in clinically relevant settings. ROTEM is widely used to guide hemostatic therapy in perioperative and critically ill patients, but its interpretation is complex and subject to interobserver variability.
The primary objective is to determine whether AI-based interpretation achieves agreement comparable to variability between expert clinicians. Secondary objectives include comparison of interpretation time, assessment of consistency of AI outputs, and evaluation of potential differences in clinical decision-making.
Read more
ROTEM datasets will be independently assessed by multiple expert anesthesiologists and by an AI-based model using standardized input. Agreement between methods and variability of interpretation will be analyzed.
The study aims to determine whether AI-assisted interpretation could serve as a reliable decision-support tool and reduce variability in ROTEM-guided clinical practice.
Read the detailed description
This prospective multicenter observational study is designed to evaluate the agreement between artificial intelligence (AI)-based interpretation and expert interpretation of rotational thromboelastometry (ROTEM) findings, with a focus on clinical decision-making in critically ill patients.
ROTEM is a point-of-care viscoelastic method providing real-time information on coagulation, including clot formation, strength, and fibrinolysis. It is widely used to guide targeted hemostatic therapy in trauma, major surgery, and critical care. However, interpretation of ROTEM findings is complex and requires clinical expertise. Interobserver variability among clinicians may lead to inconsistent therapeutic decisions. Although algorithm-based approaches have been introduced, their implementation remains variable.
Artificial intelligence (AI) has the potential to standardize interpretation by integrating multiple ROTEM parameters and generating consistent recommendations. Previous studies have shown that machine learning models can predict clinical outcomes or transfusion requirements based on viscoelastic data. However, evidence on agreement between AI-based interpretation and expert interpretation, particularly in real-world clinical decision-making, remains limited.
The primary objective of this study is to determine whether AI-based interpretation achieves a level of agreement comparable to inter-expert variability in ROTEM interpretation. This study does not assume a single gold standard; instead, it evaluates agreement between methods, reflecting real-world clinical practice.
Secondary objectives include:
- comparison of interpretation time between AI and expert clinicians,
- assessment of consistency (intra-method variability) of AI compared to inter-expert variability,
- evaluation of potential impact on clinical decision-making, including identification of coagulation abnormalities and proposed treatment strategies.
ROTEM measurements will be collected and presented in a standardized format, including graphical and numerical outputs. Each dataset will be independently evaluated by multiple expert anesthesiologists. The same datasets will be interpreted repeatedly by an AI-based large language model using a predefined standardized prompt, with multiple independent runs to assess intra-model variability.
For each ROTEM dataset, both experts and AI will assess:
- presence of a coagulation disorder,
- dominant underlying abnormality,
- appropriate therapeutic intervention,
- and recommended treatment dose.
Agreement between experts and AI, as well as inter-expert agreement, will be analyzed using appropriate statistical methods for categorical and continuous variables (e.g., kappa statistics and intraclass correlation coefficients). Time required for interpretation will also be recorded and compared.
This study is not designed to determine the absolute correctness of interpretation, but to quantify agreement and variability between human experts and AI. By identifying clinically relevant discrepancies, the study aims to evaluate whether AI-assisted interpretation may serve as a reliable decision-support tool and reduce variability in ROTEM-guided hemostatic management.
- Conditions
- Coagulopathy, Thromboelastography, Agreement
- Being tested
- Large Language Model (LLM) artificial intelligence assesment
- Keywords
- ROTEM, thromboelastography, coagulopathy
- Medical subject headings
- Hemostatic Disorders
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Large Language Model (LLM) artificial intelligence assesmentOther
The thromboelastography record will be assessed by LLM based artificial intelligence.
| Study group | Role | What they receive |
|---|---|---|
| Patients indicated for thromboelastography. | Intensive care unit adult patients that are indicated for thromboelastography. Receives: Other: Large Language Model (LLM) artificial intelligence assesment |
What is being measured
The primary outcome is the main question the study is designed to answer.
Agreement between AI-based and expert interpretation of ROTEM findings
Agreement between artificial intelligence (AI)-based interpretation and expert interpretation of ROTEM findings will be assessed and compared to inter-expert agreement. Agreement will be evaluated for predefined clinical decision outputs, including:
- presence of coagulation disorder (yes/no),
- identification of dominant coagulation abnormality (categorical),
- selection of therapeutic intervention (categorical),
- and recommended treatment dose (continuous).
Agreement will be quantified using appropriate statistical methods for categorical and continuous data (e.g., Cohen's/Fleiss' kappa and intraclass correlation coefficients).
Measured: Up to 24 hours after ROTEM measurement (time required for interpretation and data recording).
Secondary outcomes (4)
Interpretation time
Time required for interpretation of ROTEM findings by expert clinicians compared to AI-based interpretation. For experts, time will be measured from presentation of the ROTEM result to completion of responses. For AI, time will be measured from submission of the standardized query to completion of output generation.
Measured: Up to 24 hours after ROTEM measurement.
Consistency of interpretation (intra-method variability)
Variability of AI-based interpretation across repeated independent runs will be assessed and compared with inter-expert variability. Consistency will be evaluated for all predefined outputs (diagnosis, treatment, dosing).
Measured: Up to 24 hours after ROTEM measurement
Proportion of clinically discordant decisions
Proportion of cases in which AI-based interpretation differs from expert interpretation in clinically relevant outputs, including:
- disagreement in diagnosis of coagulation disorder,
- disagreement in selected treatment strategy,
- or clinically relevant difference in recommended dose.
Measured: Up to 24 hours after ROTEM measurement
Inter-expert agreement
Agreement between individual expert clinicians in interpretation of ROTEM findings will be evaluated to define baseline interobserver variability and provide a reference for comparison with AI-based interpretation.
Measured: Up to 24 hours after ROTEM measurement
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted31 Mar 2026
- Study start1 Sep 2026 (estimated)
- Primary completion1 Jun 2027 (estimated)
- Study completion1 Jul 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for CoagulopathyStudies 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
- Other
- Time perspective
- Prospective
- Enrolment
- 400 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Masarykova Nemocnice v Usti nad Labem, Krajska Zdravotni a.s.
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Michal Kalina, Masarykova Nemocnice v Usti nad Labem, Krajska Zdravotni a.s.
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07502950
- Sponsor's protocol ID
- ROTEMAI
- Acronym
- ROTEMAI
- Status last verified
- Mar 2026
- First submitted
- 25 Mar 2026
- Last updated
- 31 Mar 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 31 Mar 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Artificial Intelligence Versus Expert Interpretation of ROTEM: A Prospective Study of Agreement and Clinical Decision-Making
- ClinicalTrials.gov ID
- NCT07502950
- Acronym / sponsor ID
- ROTEMAI / ROTEMAI
- Conditions
- Coagulopathy, Thromboelastography, Agreement
- Keywords
- ROTEM, thromboelastography, coagulopathy
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 400 participants (estimated)
- Lead sponsor
- Masarykova Nemocnice v Usti nad Labem, Krajska Zdravotni a.s.University, hospital or other
- Study start
- 1 Sep 2026 (estimated)
- Primary completion
- 1 Jun 2027 (estimated)
- Study completion
- 1 Jul 2027 (estimated)
- First posted
- 31 Mar 2026 (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.
This prospective multicenter observational study aims to evaluate the agreement between artificial intelligence (AI)-based interpretation and expert interpretation of rotational thromboelastometry (ROTEM) findings in clinically relevant settings. ROTEM is widely used to guide hemostatic therapy in perioperative and critically ill patients, but its interpretation is complex and subject to interobserver variability.
The primary objective is to determine whether AI-based interpretation achieves agreement comparable to variability between expert clinicians. Secondary objectives include comparison of interpretation time, assessment of consistency of AI outputs, and evaluation of potential differences in clinical decision-making.
ROTEM datasets will be independently assessed by multiple expert anesthesiologists and by an AI-based model using standardized input. Agreement between methods and variability of interpretation will be analyzed.
The study aims to determine whether AI-assisted interpretation could serve as a reliable decision-support tool and reduce variability in ROTEM-guided clinical practice.
Detailed description
This prospective multicenter observational study is designed to evaluate the agreement between artificial intelligence (AI)-based interpretation and expert interpretation of rotational thromboelastometry (ROTEM) findings, with a focus on clinical decision-making in critically ill patients.
ROTEM is a point-of-care viscoelastic method providing real-time information on coagulation, including clot formation, strength, and fibrinolysis. It is widely used to guide targeted hemostatic therapy in trauma, major surgery, and critical care. However, interpretation of ROTEM findings is complex and requires clinical expertise. Interobserver variability among clinicians may lead to inconsistent therapeutic decisions. Although algorithm-based approaches have been introduced, their implementation remains variable.
Artificial intelligence (AI) has the potential to standardize interpretation by integrating multiple ROTEM parameters and generating consistent recommendations. Previous studies have shown that machine learning models can predict clinical outcomes or transfusion requirements based on viscoelastic data. However, evidence on agreement between AI-based interpretation and expert interpretation, particularly in real-world clinical decision-making, remains limited.
The primary objective of this study is to determine whether AI-based interpretation achieves a level of agreement comparable to inter-expert variability in ROTEM interpretation. This study does not assume a single gold standard; instead, it evaluates agreement between methods, reflecting real-world clinical practice.
Secondary objectives include:
- comparison of interpretation time between AI and expert clinicians,
- assessment of consistency (intra-method variability) of AI compared to inter-expert variability,
- evaluation of potential impact on clinical decision-making, including identification of coagulation abnormalities and proposed treatment strategies.
ROTEM measurements will be collected and presented in a standardized format, including graphical and numerical outputs. Each dataset will be independently evaluated by multiple expert anesthesiologists. The same datasets will be interpreted repeatedly by an AI-based large language model using a predefined standardized prompt, with multiple independent runs to assess intra-model variability.
For each ROTEM dataset, both experts and AI will assess:
- presence of a coagulation disorder,
- dominant underlying abnormality,
- appropriate therapeutic intervention,
- and recommended treatment dose.
Agreement between experts and AI, as well as inter-expert agreement, will be analyzed using appropriate statistical methods for categorical and continuous variables (e.g., kappa statistics and intraclass correlation coefficients). Time required for interpretation will also be recorded and compared.
This study is not designed to determine the absolute correctness of interpretation, but to quantify agreement and variability between human experts and AI. By identifying clinically relevant discrepancies, the study aims to evaluate whether AI-assisted interpretation may serve as a reliable decision-support tool and reduce variability in ROTEM-guided hemostatic management.
Study design
- Observational model
- Other
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Patients indicated for thromboelastography. | – | Other: Large Language Model (LLM) artificial intelligence assesmentIntensive care unit adult patients that are indicated for thromboelastography. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- The study population will consist of adult patients undergoing rotational thromboelastometry (ROTEM) as part of routine clinical care in participating centers. Patients will be recruited from perioperative and intensive care settings, including major surgery, trauma, and critical illness, where assessment of coagulation status is clinically indicated. This study reflects real-world practice, with no modification of standard care. The unit of analysis will be individual ROTEM measurements rather than individual patients, as repeated measurements may occur during clinical management. Only ROTEM datasets with complete graphical and numerical outputs and meeting predefined quality criteria will be included.
Key inclusion criteria (4 listed)
- Adult patients (age ≥18 years)
- ROTEM analysis performed using a ROTEM Sigma device as part of routine clinical care
- Availability of complete ROTEM output (graphical and numerical data)
- ROTEM measurement obtained in a clinical context where assessment of coagulation status is indicated (e.g., perioperative setting, trauma, or critical illness)
Key exclusion criteria (3 listed)
- ROTEM measurements performed using the HEPTEM channel
- Incomplete or missing ROTEM data preventing standardized evaluation
- ROTEM measurements obtained under non-standardized or technically unreliable conditions
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Large Language Model (LLM) artificial intelligence assesmentThe thromboelastography record will be assessed by LLM based artificial intelligence. | Other | Patients indicated for thromboelastography. |
Primary outcome
Agreement between AI-based and expert interpretation of ROTEM findings
Time frame: Up to 24 hours after ROTEM measurement (time required for interpretation and data recording).
Agreement between artificial intelligence (AI)-based interpretation and expert interpretation of ROTEM findings will be assessed and compared to inter-expert agreement. Agreement will be evaluated for predefined clinical decision outputs, including: * presence of coagulation disorder (yes/no), * identification of dominant coagulation abnormality (categorical), * selection of therapeutic intervention (categorical), * and recommended treatment dose (continuous). Agreement will be quantified using appropriate statistical methods for categorical and continuous data (e.g., Cohen's/Fleiss' kappa and intraclass correlation coefficients).
4 secondary outcomes
Interpretation time
Time frame: Up to 24 hours after ROTEM measurement.
Consistency of interpretation (intra-method variability)
Time frame: Up to 24 hours after ROTEM measurement
Proportion of clinically discordant decisions
Time frame: Up to 24 hours after ROTEM measurement
Inter-expert agreement
Time frame: Up to 24 hours after ROTEM measurement
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 31 Mar 2026.
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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.
The record lists no contact. Use the official record to reach the sponsor.
Questions about this trial
What is this clinical trial studying?
An observational study of Coagulopathy and Thromboelastography, run by Masarykova Nemocnice v Usti nad Labem, Krajska Zdravotni a.s..
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 4 inclusion and 3 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: opening soon, as of the sponsor's last update on 31 Mar 2026. Status can change between updates, so confirm with the study contact before making plans.
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?
Agreement between AI-based and expert interpretation of ROTEM findings (time frame: Up to 24 hours after ROTEM measurement (time required for interpretation and data recording).). 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 31 Mar 2026.