Deep Venous Thrombosis of LegNCT05288413
Benefit of Machine Learning to Diagnose Deep Vein Thrombosis Compared to Gold Standard Ultrasound
An observational study of Deep Venous Thrombosis of Leg and Deep Vein Thrombosis, run by Imperial College London at 1 site in United Kingdom.
Trial summary
The study coordinator aims to compare gold standard deep vein thrombosis (DVT) diagnostic performed by a specialist sonographer to a scan by a non-specialist with a newly developed an automated DVT (AutoDVT) detection software device. Read more about this study
- Who is it for?
- Ages 18 to 90 years
- Conditions the study is for
- Deep Venous Thrombosis of Leg, Deep Vein Thrombosis
- What is being tested?
- 3-compression Ultrasound scan with AutoDVT software integrated Ultrasound probe
- Type of treatment
- Medical device
- How many people
- 0 participants
- Where
- 1 siteUnited Kingdom
- Study dates
- 1 Mar 2022to 1 Aug 2022
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 90 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Patients, which present at the DVT-clinic, The Bays at St.Marys Hospital with DVT symptomatic and requiring a scan
Who may be able to join
- The participant has capacity to consent and consent is obtained
- The participant is an adult (18 or older in the UK)
- The participant has symptoms suggestive of a deep venous thrombosis
- The diagnostic DVT algorithm indicates that an ultrasound is needed
Who cannot join
- A patient will not be eligible for this study if they fulfil one or more of the following criteria:
- Participant cannot consent
- Participant is under age of 18
- No data of D-dimer result
- The participant is found to have a distal DVT during the US scan (retrospective exclusion)
- Patient did not sign consent form
Trial locations and contacts
1 site in 1 country.
United Kingdom1 site
Thrombosis Clinic, The Bays at St.Mary's Hospital
London, W2 1NY
About this study
The study coordinator aims to compare gold standard deep vein thrombosis (DVT) diagnostic performed by a specialist sonographer to a scan by a non-specialist with a newly developed an automated DVT (AutoDVT) detection software device.
The title of the project is: Benefit of Machine learning to diagnose Deep Vein thrombosis compared to gold standard Ultrasound.
Read more
Currently the process from the DVT symptom begin, to diagnosis and then treatment is all but not straightforward. It implements a laborious journey for the patient from their general practitioner (GP) to accident and emergency (A\&E), then to a specialist sonographer.
However, handheld Ultrasound devices have recently become available and they have been implemented with a machine learning software. The startup company ThinkSono developed a software which is hoped to divide between thrombosis and no thrombosis. In this single-blinded pilot study, patients which present at St Mary's DVT Clinic will be scanned by the specialist and then by a non-specialist with the machine learning supported device. The accuracy and sensitivity of this device will be compared to the gold standard.
This would mean that DVT could be diagnosed at point of care by a non-specialist such as a community nurse or nursing home nurse, for example beneficial for multimorbid confused nursing home patients. This technology could reduce A\&E crowding and free up specialist sonographer to focus on other clinical tasks. These improvements could significantly reduce the financial burden for the National Health System (NHS).
The AutoDVT has a CE (as the logo CЄ, which means that the manufacturer or importer affirms the good's conformity with European health, safety, and environmental protection standards) Certificate under the directive 93/42/ European Economic Community (EEC) for medical devices. It is classified in Class 1 - Active Medical Device - Ultrasound Imaging System Application Software (40873).
Furthermore, following standards and technical specifications have been applied: British Standard (BS) European Norm (EN) International Organisation for Standardisation (ISO) 13485:2016, BS EN ISO 14971:2012, Data Coordination Board (DCB)0129:2018, ISO 15233-1:2016.
Read the detailed description
"AutoDVT" is a software system designed to assist non-specialist operators, such as nurses, general practitioners (GP) and other allied health professionals in the diagnosis of DVT. The software utilises a "machine learning" algorithm as described below.
This study aims to improve the current laborious, time consuming and expensive diagnostic DVT pathway.
Venous thrombosis (VT) commonly occurs in the deep leg veins as well as the deep veins of the pelvis. DVTs can be divided into above knee (iliac, femoral, popliteal) and below knee (calf veins).
DVT is well recognised to cause globally significant morbidity and mortality both at the time of diagnosis and post-diagnosis. Between 30 - 50 percent of patients diagnosed with DVT will go on to develop a post-thrombotic syndrome, which has a significant impact on patients' long-term quality of life. Patients with DVT are also at risk to develop a fatal pulmonary embolism (PE). According to Charity Thrombosis United Kingdom (UK) dies every 37 seconds a person of a VT in developed countries.
Between 75-88 percent of suspected DVT cases, when fully investigated, are negative. The cost for diagnosing DVT over a decade ago was between 42-202 British Pound (£), such that the cost to the NHS of investigating all patients who present with DVT symptoms was approximately £175 million annually as stated in the study 'Non-invasive diagnosis of deep vein thrombosis from ultrasound imaging with machine learning' by Prof. Kainz from Imperial College London.
It is important to note that this value does not take into account any additional indirect costs such as time lost from work, hospitalisation, treatment costs and costs for repeat ultrasound scans. It is difficult to diagnose a DVT by clinical exam alone. The current standard approach to diagnose a proximal DVT involves an algorithm combining pre-test probability (Wells Score), D-dimer (blood) testing, and compression ultrasonography (typically a three-point compression examination).
There are new handheld ultrasound (US) probes available, meaning only the US probe is required for diagnostic purposes in conjunction with a mobile phone or tablet. At present, although the new handheld probes are smaller and are better suited for point of care diagnosis, they still require an experienced radiologist or sonographer to perform the three-point compression exam.
This means, that these devices can only be used wherever specialists such as radiographers or radiologists are based. However, due to recent advances in "machine learning", a software has now been developed for these 'app-based' probes that can assist non-specialist healthcare professionals to carry out the compression US exam with minimal training and divide between DVT and not DVT.
The previous data-collecting study for this device at Oxford University Hospital (OUH) was primarily used to improve the AutoDVT software but it also highlighted in a small pilot study that this technology had a similar diagnostic test accuracy to standard compression US. The study outlined in this protocol will test this hypothesis.
- Being tested
- 3-compression Ultrasound scan with AutoDVT software integrated Ultrasound probe
- Keywords
- thrombosis, ultrasound, diagnostic, machine learning
- Medical subject headings
- Venous Thrombosis, Thrombosis, Disease
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
3-compression Ultrasound scan with AutoDVT software integrated Ultrasound probeDevice
The study coordinator, Miss Kerstin Saupe will perform a three-point compression ultrasound scan (USS) of the upper leg with the AutoDVT software. The AutoDVT software will store the results of the scan for retroperspective analysis and review. Effectiveness of AutoDVT as diagnostic tool will be evaluated in perspective to different patient groups, eg. patients with adipositas or maligner disease,
| Study group | Role | What they receive |
|---|---|---|
| Goldstandard DVT diagnostic through specialist sonographer | Patients with DVT symptomatic of lower limb which present at the DVT Clinic the Bays at St. Mary's Hospital will receive Goldstandard Scan through specialist. Receives: Device: 3-compression Ultrasound scan with AutoDVT software integrated Ultrasound probe | |
| DVT diagnostic with Ultrasound probe with AutoDVT Software | Above patients will receive a second Ultrasound scan by a non-specialist with AutoDVT device. Receives: Device: 3-compression Ultrasound scan with AutoDVT software integrated Ultrasound probe | |
| Retrospective data evaluation | result of AutoDVT is equivalent to diagnosis of specialist sonographer. Results specifically highlighted in patients with adipositas or malignant disease. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Sensitivity of AutoDVT diagnostic software compared to diagnose by a trained sonographer
The compressibility of veins is measured in a 3-compression ultrasound performed with a n AutoDVT diagnostic software as tool. The results will be compared to the gold standard scan by a specialist.
Measured: Day 1, no follow up
Secondary outcomes (2)
Sensitivity of AutoDVT as diagnostic tool in obese patients
The compressibility of veins is measured in a 3-compression ultrasound performed with an AutoDVT diagnostic software as tool. The results will be compared to the gold standard scan by a specialist.
Measured: Day 1, no follow up
Sensitivity of AutoDVT as diagnostic tool in patients with previous history of malignancy
The compressibility of veins is measured in a 3-compression ultrasound performed with an AutoDVT diagnostic software as tool. The results will be compared to the gold standard scan by a specialist.
Measured: Day 1, no follow up
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted21 Mar 2022
- Study start1 Mar 2022
- Primary completion1 Aug 2022
- Study completion1 Aug 2022
Why it stopped: abandoned before opening
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Deep Venous Thrombosis of LegStudies of the same condition, open and under way.
- More trials run by Imperial College LondonThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Cross-sectional
- Enrolment
- 0 participants (actual)
Sponsor and investigators
- Lead sponsor
- Imperial College London
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Mohammed F Aslam, MBA, PhD, Study director, Imperial College London
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05288413
- Sponsor's protocol ID
- IRAS313321
- Status last verified
- Jan 2023
- First submitted
- 16 Feb 2022
- Last updated
- 30 Jan 2023
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Kainz B, Heinrich MP, Makropoulos A, Oppenheimer J, Mandegaran R, Sankar S, Deane C, Mischkewitz S, Al-Noor F, Rawdin AC, Ruttloff A, Stevenson MD, Klein-Weigel P, Curry N. Non-invasive diagnosis of deep vein thrombosis from ultrasound imaging with machine learning. NPJ Digit Med. 2021 Sep 15;4(1):137. doi: 10.1038/s41746-021-00503-7.
PubMed 34526639
Study at a glance
From the registry record, last updated by the sponsor on 30 Jan 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Benefit of Machine Learning to Diagnose Deep Vein Thrombosis Compared to Gold Standard Ultrasound
- ClinicalTrials.gov ID
- NCT05288413
- Acronym / sponsor ID
- IRAS313321
- Keywords
- thrombosis, ultrasound, diagnostic, machine learning
- Study type
- Observational
- Recruitment status
- WithdrawnReason stopped: abandoned before opening
- Enrolment
- 0 participants (actual)
- Lead sponsor
- Imperial College LondonUniversity, hospital or other
- Study start
- 1 Mar 2022 (actual)
- Primary completion
- 1 Aug 2022 (actual)
- Study completion
- 1 Aug 2022 (actual)
- First posted
- 21 Mar 2022 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
The study coordinator aims to compare gold standard deep vein thrombosis (DVT) diagnostic performed by a specialist sonographer to a scan by a non-specialist with a newly developed an automated DVT (AutoDVT) detection software device.
The title of the project is: Benefit of Machine learning to diagnose Deep Vein thrombosis compared to gold standard Ultrasound.
Currently the process from the DVT symptom begin, to diagnosis and then treatment is all but not straightforward. It implements a laborious journey for the patient from their general practitioner (GP) to accident and emergency (A\&E), then to a specialist sonographer.
However, handheld Ultrasound devices have recently become available and they have been implemented with a machine learning software. The startup company ThinkSono developed a software which is hoped to divide between thrombosis and no thrombosis. In this single-blinded pilot study, patients which present at St Mary's DVT Clinic will be scanned by the specialist and then by a non-specialist with the machine learning supported device. The accuracy and sensitivity of this device will be compared to the gold standard.
This would mean that DVT could be diagnosed at point of care by a non-specialist such as a community nurse or nursing home nurse, for example beneficial for multimorbid confused nursing home patients. This technology could reduce A\&E crowding and free up specialist sonographer to focus on other clinical tasks. These improvements could significantly reduce the financial burden for the National Health System (NHS).
The AutoDVT has a CE (as the logo CЄ, which means that the manufacturer or importer affirms the good's conformity with European health, safety, and environmental protection standards) Certificate under the directive 93/42/ European Economic Community (EEC) for medical devices. It is classified in Class 1 - Active Medical Device - Ultrasound Imaging System Application Software (40873).
Furthermore, following standards and technical specifications have been applied: British Standard (BS) European Norm (EN) International Organisation for Standardisation (ISO) 13485:2016, BS EN ISO 14971:2012, Data Coordination Board (DCB)0129:2018, ISO 15233-1:2016.
Detailed description
"AutoDVT" is a software system designed to assist non-specialist operators, such as nurses, general practitioners (GP) and other allied health professionals in the diagnosis of DVT. The software utilises a "machine learning" algorithm as described below.
This study aims to improve the current laborious, time consuming and expensive diagnostic DVT pathway.
Venous thrombosis (VT) commonly occurs in the deep leg veins as well as the deep veins of the pelvis. DVTs can be divided into above knee (iliac, femoral, popliteal) and below knee (calf veins).
DVT is well recognised to cause globally significant morbidity and mortality both at the time of diagnosis and post-diagnosis. Between 30 - 50 percent of patients diagnosed with DVT will go on to develop a post-thrombotic syndrome, which has a significant impact on patients' long-term quality of life. Patients with DVT are also at risk to develop a fatal pulmonary embolism (PE). According to Charity Thrombosis United Kingdom (UK) dies every 37 seconds a person of a VT in developed countries.
Between 75-88 percent of suspected DVT cases, when fully investigated, are negative. The cost for diagnosing DVT over a decade ago was between 42-202 British Pound (£), such that the cost to the NHS of investigating all patients who present with DVT symptoms was approximately £175 million annually as stated in the study 'Non-invasive diagnosis of deep vein thrombosis from ultrasound imaging with machine learning' by Prof. Kainz from Imperial College London.
It is important to note that this value does not take into account any additional indirect costs such as time lost from work, hospitalisation, treatment costs and costs for repeat ultrasound scans. It is difficult to diagnose a DVT by clinical exam alone. The current standard approach to diagnose a proximal DVT involves an algorithm combining pre-test probability (Wells Score), D-dimer (blood) testing, and compression ultrasonography (typically a three-point compression examination).
There are new handheld ultrasound (US) probes available, meaning only the US probe is required for diagnostic purposes in conjunction with a mobile phone or tablet. At present, although the new handheld probes are smaller and are better suited for point of care diagnosis, they still require an experienced radiologist or sonographer to perform the three-point compression exam.
This means, that these devices can only be used wherever specialists such as radiographers or radiologists are based. However, due to recent advances in "machine learning", a software has now been developed for these 'app-based' probes that can assist non-specialist healthcare professionals to carry out the compression US exam with minimal training and divide between DVT and not DVT.
The previous data-collecting study for this device at Oxford University Hospital (OUH) was primarily used to improve the AutoDVT software but it also highlighted in a small pilot study that this technology had a similar diagnostic test accuracy to standard compression US. The study outlined in this protocol will test this hypothesis.
Study design
- Observational model
- Other
- Time perspective
- Cross-sectional
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| Goldstandard DVT diagnostic through specialist sonographer | – | Device: 3-compression Ultrasound scan with AutoDVT software integrated Ultrasound probePatients with DVT symptomatic of lower limb which present at the DVT Clinic the Bays at St. Mary's Hospital will receive Goldstandard Scan through specialist. |
| DVT diagnostic with Ultrasound probe with AutoDVT Software | – | Device: 3-compression Ultrasound scan with AutoDVT software integrated Ultrasound probeAbove patients will receive a second Ultrasound scan by a non-specialist with AutoDVT device. |
| Retrospective data evaluation | – | –result of AutoDVT is equivalent to diagnosis of specialist sonographer. Results specifically highlighted in patients with adipositas or malignant disease. |
Who may qualify
- Ages
- 18 to 90 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients, which present at the DVT-clinic, The Bays at St.Marys Hospital with DVT symptomatic and requiring a scan
Key inclusion criteria (4 listed)
- The participant has capacity to consent and consent is obtained
- The participant is an adult (18 or older in the UK)
- The participant has symptoms suggestive of a deep venous thrombosis
- The diagnostic DVT algorithm indicates that an ultrasound is needed
Key exclusion criteria (6 listed)
- A patient will not be eligible for this study if they fulfil one or more of the following criteria:
- Participant cannot consent
- Participant is under age of 18
- No data of D-dimer result
- The participant is found to have a distal DVT during the US scan (retrospective exclusion)
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| 3-compression Ultrasound scan with AutoDVT software integrated Ultrasound probeThe study coordinator, Miss Kerstin Saupe will perform a three-point compression ultrasound scan (USS) of the upper leg with the AutoDVT software. The AutoDVT software will store the results of the scan for retroperspective analysis and review. Effectiveness of AutoDVT as diagnostic tool will be evaluated in perspective to different patient groups, eg. patients with adipositas or maligner disease, | Device | DVT diagnostic with Ultrasound probe with AutoDVT Software, Goldstandard DVT diagnostic through specialist sonographer |
Primary outcome
Sensitivity of AutoDVT diagnostic software compared to diagnose by a trained sonographer
Time frame: Day 1, no follow up
The compressibility of veins is measured in a 3-compression ultrasound performed with a n AutoDVT diagnostic software as tool. The results will be compared to the gold standard scan by a specialist.
2 secondary outcomes
Sensitivity of AutoDVT as diagnostic tool in obese patients
Time frame: Day 1, no follow up
Sensitivity of AutoDVT as diagnostic tool in patients with previous history of malignancy
Time frame: Day 1, no follow up
Results and safety
The study was withdrawn before enrolling participants, so there are no results.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 30 Jan 2023.
- Results publication: PubMed 34526639Kainz B, Heinrich MP, Makropoulos A, Oppenheimer J, Mandegaran R, Sankar S, Deane C, Mischkewitz S, Al-Noor F, Rawdin AC, Ruttloff A, Stevenson MD, Klein-Weigel P, Curry N. Non-invasive diagnosis of deep vein thrombosis from ultrasound imaging with machine learning. NPJ Digit Med. 2021 Sep 15;4(1):137. doi: 10.1038/s41746-021-00503-7.
- Thrombosis in UK in 2019Link provided by the sponsor.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Mohammed F Aslam, MBA, PhD study director
Imperial College London
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Deep Venous Thrombosis of Leg and Deep Vein Thrombosis, run by Imperial College London at 1 site in United Kingdom.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 6 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: withdrawn, as of the sponsor's last update on 30 Jan 2023. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United Kingdom. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study was withdrawn before enrolling participants, so there are no results.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Sensitivity of AutoDVT diagnostic software compared to diagnose by a trained sonographer (time frame: Day 1, no follow up). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05288413, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 30 Jan 2023.