Systematic Machine Learning Algorithm for Rapid Thrombosis Detection

Condition(s) studied

Investigational drug(s) / intervention(s)

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

POC 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.

Machine 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.

Study 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.

Eligibility

Primary outcome measure(s)

• 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.

Trial sites (1)

More Ostfold Hospital Trust trials in Norway