Systematic Machine Learning Algorithm for Rapid Thrombosis Detection

NCT06842446 | Recruiting

• 1 other identifier: 682139
• Study Type: interventional
• Target: 1,000
• Locations: 1 country
• Sites: 1

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.

Conditions

Deep Vein Thrombosis

Keywords

Machine Learning; POC D-dimer; POC Ultrasound

Outcome Measures

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.

  From enrollment to the end of the primary assessment period (90 days)

Secondary Outcomes (6)

• Evaluate the efficiency of the new strategy

  From enrollment to the end of the primary assessment period (90 days)

• Validate the safety and efficiency of the ML-based prediction model

  From enrollment to the end of the primary assessment period (90 days)

• 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.

• 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.

• 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.

Study Arms (1)

All participants

EXPERIMENTAL

All participants will be treated the same way.

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

Interventions

POC D-dimer DIAGNOSTIC_TEST

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

All participants

POC ultrasound DIAGNOSTIC_TEST

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.

All participants

Machine learning model DIAGNOSTIC_TEST

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.

Also known as: Decision support system

All participants

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Patients referred to the ED due to suspicion of DVT
• Age ≥ 18 years
• Able to give informed consent

You may not qualify if:

• Ongoing use of anticoagulation for more than 72 hours
• Previous participation in the study
• Life expectancy of less than three months.

Sponsors & Collaborators

• Ostfold Hospital Trust: lead
• Sahlgrenska University Hospital: collaborator

Study Sites (1)

Østfold Hospital Trust

Sarpsborg, 1714, Norway

RECRUITING

MeSH Terms

Conditions

Venous Thrombosis

Condition Hierarchy (Ancestors)

Thrombosis; Embolism and Thrombosis; Vascular Diseases; Cardiovascular Diseases

Central Study Contacts

Waleed Ghanima, Professor

CONTACT

69860000; waleed.ghanima@so-hf.no

Hans Joakim Myklebust-Hansen, Medical Doctor

CONTACT

97501765; hans.joakim.hansen@so-hf.no

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NA
• Masking: NONE
• Purpose: DIAGNOSTIC
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: December 4, 2024
• First Posted: February 24, 2025
• Study Start: January 6, 2025
• Primary Completion (Estimated): January 5, 2027
• Study Completion (Estimated): January 5, 2029
• Last Updated: February 26, 2025

Record last verified: 2025-02

Locations

NO (1)