Østfold Hospital Trust
Sarpsborg, 1714, Norway
Location status: Recruiting
Location contact
Hans J Myklebust-Hansen, M.D.
CONTACT
Hans J Myklebust-Hansen, M.D.
SUB_INVESTIGATOR
Recruiting
NCT Number: NCT06842446
Systematic Machine Learning Algorithm for Rapid Thrombosis Detection
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.
Recruiting
Interested in participating?
Request InfoKey information
Conditions
Age range
18 year and older
Sex eligibility
All sexes
Study type
Interventional
Phase
Not applicable
Primary location
About this study
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.
Who can participate
Healthy volunteers accepted: No
Only the study team can determine whether someone qualifies for participation.
Inclusion criteria
- Patients referred to the ED due to suspicion of DVT
- Age ≥ 18 years
- Able to give informed consent
Exclusion criteria
- Ongoing use of anticoagulation for more than 72 hours
- Previous participation in the study
- Life expectancy of less than three months.
Treatment and study plan
POC D-dimer
Diagnostic TestPOC D-dimer will be compared to laboratory D-dimer in hospital setting and used in a machine learning model
POC ultrasound
Diagnostic TestPoint 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 model
Diagnostic TestThe DSS will be compared to the usual strategy. It will also be estimated how many participants where DVT could have been excluded without ultrasound.
Other names: Decision support system
Primary outcomes
-
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.
Secondary outcomes
-
Evaluate the efficiency of the new strategy
Time frame: From enrollment to the end of the primary assessment period (90 days)
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
-
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)
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
-
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.
Determine the proportion of false negative and false positive diagnosis of DVT in emergency physician-performed ultrasound compared with ultrasound performed by radiologists.
-
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.
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.
-
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.
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.
-
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.
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.
Study contacts
Contact information is provided by the study sponsor or research team.
Sponsors and collaborators
Lead sponsor
Ostfold Hospital Trust
Other
Collaborators
- Sahlgrenska University Hospital
Registry information
Official study title
Evaluating a New Diagnostic Strategy for Suspected DVT Consisting of Point of Care D-dimer, AI-based Prediction Model and Compression Ultrasound
Acronym: DVT-SMART
Important dates
- Study start
- 2025
- Primary completion
- 2027
- Study completion
- 2029
- First posted
- Feb 24, 2025
- Registry last updated
- Feb 26, 2025
OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.
View the official ClinicalTrials.gov record (opens in a new tab)This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.
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