Clinical trial NCT06842446: Systematic Machine Learning Algorithm for Rapid Thrombosis Detection
Official title: Evaluating a New Diagnostic Strategy for Suspected DVT Consisting of Point of Care D-dimer, AI-based Prediction Model and Compression Ultrasound
Study type: INTERVENTIONAL
Overall status: RECRUITING
Phase: NA
Conditions: Deep Vein Thrombosis
Keywords: Machine Learning
POC D-dimer
POC Ultrasound
Lead sponsor: Ostfold Hospital Trust
Sponsor class: OTHER
Collaborators: name: Sahlgrenska University Hospital
First posted: 2025-02-24
Enrollment: 1000
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: NA
Intervention model: SINGLE_GROUP
Primary purpose: DIAGNOSTIC

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.

Eligibility criteria: 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.

Primary outcomes:
measure: Safety of the new strategy (POC D-dimer, ML-based prediction model, POC CUS by emergency physician)
timeFrame: From enrollment to the end of the primary assessment period (90 days)
Secondary outcomes:
measure: Evaluate the efficiency of the new strategy
timeFrame: From enrollment to the end of the primary assessment period (90 days)
measure: Validate the safety and efficiency of the ML-based prediction model
timeFrame: From enrollment to the end of the primary assessment period (90 days)
measure: Evaluate concordance between CUS performed by emergency physicians and radiologists.
timeFrame: From time of enrollment until time of ultrasound examination performed by radiologist, assessed up to 48 hours.
measure: Evaluate concordance between POC D-dimers in an ED setting and laboratory D-dimers.
timeFrame: From enrollment to the completion of D-dimer analysis, assessed up to 24 hours.
measure: Evaluate the hypothetical time to be completed for the novel strategy compared to the standard strategy.
timeFrame: From time of enrollment until time of discharge from the emergency department either discharged from the hospital or hospitalized, assessed up to 24 hours.
measure: Evaluate the safety of a limited ultrasound protocol (two-point and proximal) compared to full-leg CUS performed by emergency physicians and radiologists
timeFrame: 90 days after enrollment.
Other outcomes:
Locations:
city: Sarpsborg
country: Norway
facility: Østfold Hospital Trust
Study officials:
References: