Systematic Machine Learning Algorithm for Rapid Thrombosis Detection (DVT-SMART)

Ostfold Hospital Trust

Status: Enrolling

Conditions: Deep Vein Thrombosis

Treatments: Diagnostic Test: POC D-dimer; Diagnostic Test: POC ultrasound; Diagnostic Test: Machine learning model

Study type: Interventional

Funder types: Other

Identifiers

NCT06842446

682139

Details and patient eligibility

About

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.

Full 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.

Enrollment: 1,000 estimated patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

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.

Trial design

Primary purpose: Diagnostic

Allocation: N/A

Interventional model: Single Group Assignment

Masking: None (Open label)

---

1,000 participants in 1 patient group

All participants

Experimental group

Description:

All participants will be treated the same way.

Treatment:

Diagnostic Test: Machine learning model

Diagnostic Test: POC ultrasound

Diagnostic Test: POC D-dimer