Systematic Machine Learning Algorithm for Rapid Thrombosis Detection
ConditionDeep Vein Thrombosis
Trial statusRecruiting
Trial phaseNot applicable
Trial typeInterventional
Biological sexAll
Age18+
SponsorOstfold Hospital Trust
About this trial
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.
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
Patients referred to the ED due to suspicion of DVT
Age ≥ 18 years
Able to give informed consent
Disqualifiers
Ongoing use of anticoagulation for more than 72 hours
Previous participation in the study
Life expectancy of less than three months.
Trial design
Design model
Single group
Treatments tested in this trial
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.
Treatment groups
1,000 Participants
are divided into 1 treatment groupGroup A: All participantsExperimental treatment 3 interventions
Trial outcomes
Primary outcomes
1
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.
Time frame
From enrollment to the end of the primary assessment period (90 days)Secondary outcomes
1
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
Time frame
From enrollment to the end of the primary assessment period (90 days)2
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
Time frame
From enrollment to the end of the primary assessment period (90 days)3
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.
Time frame
From time of enrollment until time of ultrasound examination performed by radiologist, assessed up to 48 hours.4
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.
Time frame
From enrollment to the completion of D-dimer analysis, assessed up to 24 hours.Sponsors and contacts
Click on the lead sponsor to view all of their trials.
Ostfold Hospital Trust
Lead sponsor
Sahlgrenska University Hospital
Collaborator
Source ClinicalTrials.gov