Systematic Machine Learning Algorithm for Rapid Thrombosis Detection

Официальное название

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

Обзор

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.

Подробное описание

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.

Вмешательства

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

Первичные конечные точки

• Safety of the new strategy (POC D-dimer, ML-based prediction model, POC CUS by emergency physician) [Срок оценки: From enrollment to the end of the primary assessment period (90 days)]

Вторичные конечные точки (6)

• Evaluate the efficiency of the new strategy [Срок оценки: From enrollment to the end of the primary assessment period (90 days)]
• Validate the safety and efficiency of the ML-based prediction model [Срок оценки: From enrollment to the end of the primary assessment period (90 days)]
• Evaluate concordance between CUS performed by emergency physicians and radiologists. [Срок оценки: 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. [Срок оценки: 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. [Срок оценки: From time of enrollment until time of discharge from the emergency department either discharged from the hospital or hospitalized, assessed up to 24 hours.]
• Evaluate the safety of a limited ultrasound protocol (two-point and proximal) compared to full-leg CUS performed by emergency physicians and radiologists [Срок оценки: 90 days after enrollment.]

Критерии участия

Критерии включения

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

Критерии исключения

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

Критерии приведены из реестра в оригинале (на английском). Окончательную оценку соответствия проводит исследовательский центр.

Здоровые добровольцы: Нет

Дизайн исследования

Распределение

Не применимо

Модель

Одна группа

Маскирование

Открытое

Основная цель

Диагностика

Центры проведения

Норвегия · 1 центр

• Østfold Hospital Trust — Sarpsborg

Идентификаторы

NCT: NCT06842446 · 682139

Первоисточники (государственные реестры)

• ClinicalTrials.gov (NIH, США)

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