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Systematic Machine Learning Algorithm for Rapid Thrombosis Detection

Evaluating a New Diagnostic Strategy for Suspected DVT Consisting of Point of Care D-dimer, AI-based Prediction Model and Compression Ultrasound

Status
Recruiting
Phases
NA
Study type
Interventional
Source
ClinicalTrials.gov
Registry ID
NCT06842446
Acronym
DVT-SMART
Enrollment
1000
Registered
2025-02-24
Start date
2025-01-06
Completion date
2029-01-05
Last updated
2025-02-26

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Deep Vein Thrombosis

Keywords

Machine Learning, POC D-dimer, POC Ultrasound

Brief 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. 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.

Interventions

DIAGNOSTIC_TESTPOC D-dimer

POC D-dimer will be compared to laboratory D-dimer in hospital setting and used in a machine learning model

DIAGNOSTIC_TESTPOC ultrasound

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.

DIAGNOSTIC_TESTMachine learning model

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.

Sponsors

Sahlgrenska University Hospital
CollaboratorOTHER
Ostfold Hospital Trust
Lead SponsorOTHER

Study design

Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
DIAGNOSTIC
Masking
NONE

Eligibility

Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No

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.

Design outcomes

Primary

MeasureTime frameDescription
Safety of the new strategy (POC D-dimer, ML-based prediction model, POC CUS by emergency physician)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

MeasureTime frameDescription
Validate the safety and efficiency of the ML-based prediction modelFrom 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.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 the efficiency of the new strategyFrom 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
Evaluate the hypothetical time to be completed for the novel strategy compared to the standard strategy.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 radiologists90 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.
Evaluate concordance between POC D-dimers in an ED setting and laboratory D-dimers.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.

Countries

Norway

Contacts

Primary ContactWaleed Ghanima, Professor
waleed.ghanima@so-hf.no69860000
Backup ContactHans Joakim Myklebust-Hansen, Medical Doctor
hans.joakim.hansen@so-hf.no97501765

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026