Deep Vein ThrombosisDVT-SMARTNCT06842446
Systematic Machine Learning Algorithm for Rapid Thrombosis Detection
A study testing POC D-dimer, POC ultrasound and Machine learning model in people with Deep Vein Thrombosis, run by Ostfold Hospital Trust at 1 site in Norway.
Trial summary
The goal of this clinical trial is to compare the use of a machine learning-based algorithm and point-of-care D-dimer to laboratory D-dimer and compression ultrasound to exclude deep vein thrombosis in the under extremities in patients referred to a medical department suspected of having deep vein thrombosis. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Deep Vein Thrombosis
- What is being tested?
- POC D-dimer, POC ultrasound and Machine learning model
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 1,000 participants
- Where
- 1 siteNorway
- Study dates
- 6 Jan 2025to 5 Jan 2029
- Phase
- Interventional
- 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
Who may be able to join
- Patients referred to the ED due to suspicion of DVT
- Age ≥ 18 years
- Able to give informed consent
Who cannot join
- Ongoing use of anticoagulation for more than 72 hours
- Previous participation in the study
- Life expectancy of less than three months.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Norway1 site, 1 recruiting
- Recruiting
Østfold Hospital Trust
Sarpsborg, 1714
Hans J Myklebust-Hansen, M.D., 004797501765, hansjoakim_mh@outlook.com
Hans J Myklebust-Hansen, M.D.
About this study
Official title: Evaluating a New Diagnostic Strategy for Suspected DVT Consisting of Point of Care D-dimer, AI-based Prediction Model and Compression Ultrasound
The goal of this clinical trial is to compare the use of a machine learning-based algorithm and point-of-care D-dimer to laboratory D-dimer and compression ultrasound to exclude deep vein thrombosis in the under extremities in patients referred to a medical department suspected of having deep vein thrombosis. The main aim is to answer are if a machine learning algorithm and point of care D-dimer can exclude deep vein thrombosis in more patients than clinical assessment and D-dimer alone.
Read the detailed description
All participants will follow the usual diagnostic algorithm used for patients with suspected DVT referred to Ostfold Hospital (all patients are examined by a physician, D-dimer is analyzed in all patients, ultrasound is performed by a radiologist in patients with positive D-dimer). In addition to usual care, POC D-dimer, POC ultrasound (performed by ED physicians), blood sampling for biobanking, and photographies of the under extremities will be performed. The machine learning model will be tested to see if the prediction is correct. In participants where ultrasound is performed, it will also be assessed whether the machine learning algorithm could have excluded the participant without the use of ultrasound. None of the additional procedures will have any impact on the patient diagnostics or treatment.
- Conditions
- Deep Vein Thrombosis
- Being tested
- POC D-dimer, POC ultrasound, Machine learning model
- Keywords
- Machine Learning, POC D-dimer, POC Ultrasound
- Medical subject headings
- Venous Thrombosis
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
POC D-dimerDiagnostic test
POC D-dimer will be compared to laboratory D-dimer in hospital setting and used in a machine learning model
POC ultrasoundDiagnostic test
Point of care (POC) ultrasound performed by ED physicians compared to ultrasound performed by radiologist. POC ultrasound 3 point examination performed by ED physician will be compared with POC ultrasound full leg examination performed by ED physician.
Machine learning modelDiagnostic test
Also called Decision support system
The DSS will be compared to the usual strategy. It will also be estimated how many participants where DVT could have been excluded without ultrasound.
| Study group | Role | What they receive |
|---|---|---|
| All participants | Experimental | All participants will be treated the same way. Receives: Diagnostic Test: POC D-dimer; Diagnostic Test: POC ultrasound; Diagnostic Test: Machine learning model |
What is being measured
The primary outcome is the main question the study is designed to answer.
Safety of the new strategy (POC D-dimer, ML-based prediction model, POC CUS by emergency physician)
Evaluate the safety of a new strategy consisting of POC D-dimer and an ML-based prediction model followed by CUS performed by emergency physicians by comparing the new strategy's safety with our standard care by measuring the proportion of patients in whom DVT is excluded according to the new strategy but was diagnosed with DVT by standard care or in whom DVT is diagnosed within the 90-day follow up.
Measured: From enrollment to the end of the primary assessment period (90 days)
Secondary outcomes (6)
Evaluate the efficiency of the new strategy
The proportion of patients in whom DVT can be ruled out by the ML-based prediction model with POC D-dimer compared to the efficiency of Wells score and laboratory D-dimer
Measured: From enrollment to the end of the primary assessment period (90 days)
Validate the safety and efficiency of the ML-based prediction model
Safety will be determined by the proportion of patients in whom DVT is excluded by the ML-model but diagnosed by standard care. Efficiency will be determined by the proportion of patients in whom DVT can be excluded by the ML-based model
Measured: From enrollment to the end of the primary assessment period (90 days)
Evaluate concordance between CUS performed by emergency physicians and radiologists.
Determine the proportion of false negative and false positive diagnosis of DVT in emergency physician-performed ultrasound compared with ultrasound performed by radiologists.
Measured: From time of enrollment until time of ultrasound examination performed by radiologist, assessed up to 48 hours.
Evaluate concordance between POC D-dimers in an ED setting and laboratory D-dimers.
Compare the two POC D-dimers with the STA-Liatest D-dimer and Siemens INNOVANCE by direct comparison of the true/false positive/negative results.
Measured: From enrollment to the completion of D-dimer analysis, assessed up to 24 hours.
Evaluate the hypothetical time to be completed for the novel strategy compared to the standard strategy.
Estimating the total management time defined as time from ED registration to ED discharge in patients evaluated according to the new strategy compared to standard care.
Measured: From time of enrollment until time of discharge from the emergency department either discharged from the hospital or hospitalized, assessed up to 24 hours.
Evaluate the safety of a limited ultrasound protocol (two-point and proximal) compared to full-leg CUS performed by emergency physicians and radiologists
Estimating the proportion of patients in whom DVT was ruled out by the limited ultrasound protocol but was diagnosed with DVT by the whole-leg ultrasound.
Measured: 90 days after enrollment.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted24 Feb 2025
- Study start6 Jan 2025
- Primary completion5 Jan 2027 (estimated)
- Study completion5 Jan 2029 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Deep Vein ThrombosisStudies 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
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Enrolment
- 1,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Ostfold Hospital Trust
- Sponsor type
- University, hospital or other
- Collaborators
- Sahlgrenska University Hospital
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06842446
- Sponsor's protocol ID
- 682139
- Acronym
- DVT-SMART
- Status last verified
- Feb 2025
- First submitted
- 4 Dec 2024
- Last updated
- 26 Feb 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided: The plan to share IPD with other researchers are not yet decided because it has not yet been discussed.
Study at a glance
From the registry record, last updated by the sponsor on 26 Feb 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Evaluating a New Diagnostic Strategy for Suspected DVT Consisting of Point of Care D-dimer, AI-based Prediction Model and Compression Ultrasound
- ClinicalTrials.gov ID
- NCT06842446
- Acronym / sponsor ID
- DVT-SMART / 682139
- Conditions
- Deep Vein Thrombosis
- Keywords
- Machine Learning, POC D-dimer, POC Ultrasound
- Study type
- Interventional
- Recruitment status
- Recruiting
- Enrolment
- 1,000 participants (estimated)
- Lead sponsor
- Ostfold Hospital TrustUniversity, hospital or other
- Collaborators
- Sahlgrenska University Hospital
- Study start
- 6 Jan 2025 (actual)
- Primary completion
- 5 Jan 2027 (estimated)
- Study completion
- 5 Jan 2029 (estimated)
- First posted
- 24 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.
The goal of this clinical trial is to compare the use of a machine learning-based algorithm and point-of-care D-dimer to laboratory D-dimer and compression ultrasound to exclude deep vein thrombosis in the under extremities in patients referred to a medical department suspected of having deep vein thrombosis. The main aim is to answer are if a machine learning algorithm and point of care D-dimer can exclude deep vein thrombosis in more patients than clinical assessment and D-dimer alone.
Detailed description
All participants will follow the usual diagnostic algorithm used for patients with suspected DVT referred to Ostfold Hospital (all patients are examined by a physician, D-dimer is analyzed in all patients, ultrasound is performed by a radiologist in patients with positive D-dimer). In addition to usual care, POC D-dimer, POC ultrasound (performed by ED physicians), blood sampling for biobanking, and photographies of the under extremities will be performed. The machine learning model will be tested to see if the prediction is correct. In participants where ultrasound is performed, it will also be assessed whether the machine learning algorithm could have excluded the participant without the use of ultrasound. None of the additional procedures will have any impact on the patient diagnostics or treatment.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| All participants | Experimental | Diagnostic Test: POC D-dimer, Diagnostic Test: POC ultrasound, Diagnostic Test: Machine learning modelAll participants will be treated the same way. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (3 listed)
- Patients referred to the ED due to suspicion of DVT
- Age ≥ 18 years
- Able to give informed consent
Key exclusion criteria (3 listed)
- Ongoing use of anticoagulation for more than 72 hours
- Previous participation in the study
- Life expectancy of less than three months.
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 |
|---|---|---|
| POC D-dimerPOC D-dimer will be compared to laboratory D-dimer in hospital setting and used in a machine learning model | Diagnostic test | All participants |
| POC ultrasoundPoint of care (POC) ultrasound performed by ED physicians compared to ultrasound performed by radiologist. POC ultrasound 3 point examination performed by ED physician will be compared with POC ultrasound full leg examination performed by ED physician. | Diagnostic test | All participants |
| Machine learning modelAlso: Decision support systemThe DSS will be compared to the usual strategy. It will also be estimated how many participants where DVT could have been excluded without ultrasound. | Diagnostic test | All participants |
Primary outcome
Safety of the new strategy (POC D-dimer, ML-based prediction model, POC CUS by emergency physician)
Time frame: From enrollment to the end of the primary assessment period (90 days)
Evaluate the safety of a new strategy consisting of POC D-dimer and an ML-based prediction model followed by CUS performed by emergency physicians by comparing the new strategy's safety with our standard care by measuring the proportion of patients in whom DVT is excluded according to the new strategy but was diagnosed with DVT by standard care or in whom DVT is diagnosed within the 90-day follow up.
6 secondary outcomes
Evaluate the efficiency of the new strategy
Time frame: From enrollment to the end of the primary assessment period (90 days)
Validate the safety and efficiency of the ML-based prediction model
Time frame: From enrollment to the end of the primary assessment period (90 days)
Evaluate concordance between CUS performed by emergency physicians and radiologists.
Time frame: From time of enrollment until time of ultrasound examination performed by radiologist, assessed up to 48 hours.
Evaluate concordance between POC D-dimers in an ED setting and laboratory D-dimers.
Time frame: From enrollment to the completion of D-dimer analysis, assessed up to 24 hours.
Evaluate the hypothetical time to be completed for the novel strategy compared to the standard strategy.
Time frame: From time of enrollment until time of discharge from the emergency department either discharged from the hospital or hospitalized, assessed up to 24 hours.
Evaluate the safety of a limited ultrasound protocol (two-point and proximal) compared to full-leg CUS performed by emergency physicians and radiologists
Time frame: 90 days after enrollment.
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 26 Feb 2025.
- Individual participant dataSharing planned: Undecided. The plan to share IPD with other researchers are not yet decided because it has not yet been discussed.
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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.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing POC D-dimer, POC ultrasound and Machine learning model in people with Deep Vein Thrombosis, run by Ostfold Hospital Trust at 1 site in Norway.
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 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: looking for participants now, as of the sponsor's last update on 26 Feb 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 Norway. 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?
Contact Waleed Ghanima, Professor (69860000 ext. 0047, waleed.ghanima@so-hf.no), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Safety of the new strategy (POC D-dimer, ML-based prediction model, POC CUS by emergency physician) (time frame: From enrollment to the end of the primary assessment period (90 days)). 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 26 Feb 2025.