Systematic Machine Learning Algorithm for Rapid Thrombosis Detection

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.

All participants will be treated the same way.

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

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

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.

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.

Time frame: 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.

Time frame: From 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

Time frame: From 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

Time frame: 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.

Time frame: 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.

Time frame: 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.

Time frame: 90 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.

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