Standardised Claudication Treadmill Test
Conditions
Keywords
treadmill test
Brief summary
This study aims to validate the use of Dynamic Contrast-Enhanced Ultrasound in measuring the blood supply to the muscles of the leg, and how this changes with exercise and vascular pathology.
Detailed description
We propose a model for the use of Dynamic Contrast-Enhanced Ultrasound (DCEU) to directly evaluate the microcirculation of the musculoskeletal system. We believe that this will be a valuable research tool into diseases of the microcirculation, and in the future may also offer a clinical benefit by quantifying and monitoring disease over time and after intervention. It may also allow targeting of therapies towards those patients most at risk of ulcers and peripheral neuropathy, and those that would get the maximum benefit from these therapies.
Interventions
PROCEDUREContrast enhanced ultrasound
Infusion of microbubbles to enhance blood vessels in the leg for ultrasound
BEHAVIORALTreadmill test
Exposes you to exercise in a controlled environment. We will see if we can detect this change with our new ultrasound protocol
Sponsors
Imperial College London
Study design
Allocation
NON_RANDOMIZED
Intervention model
PARALLEL
Primary purpose
DIAGNOSTIC
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
Yes
Inclusion criteria
Group 1 - Healthy subjects Inclusion * 18+ years old Exclusion * Personal history of diabetes or peripheral arterial disease, current pregnancy, previous surgery to the lower limb, heart attack within 4 weeks, or unstable angina Group 2 - Peripheral arterial disease subjects Inclusion * 18+ years old, radiological evidence of peripheral arterial disease (arterial doppler or angiogram), ankle-brachial pressure index (ABPI) 0.5-0.8 Exclusion * Personal history of diabetes, current pregnancy, previous amputation, heart attack within 4 weeks, or unstable angina Group 3 - Diabetic subjects Inclusion * 18+ years old, with a clinical diagnoses of diabetes (1, 2) Exclusion \- ABPI\<0.9, previous amputation, current pregnancy or breastfeeding, heart attack within 4 weeks, or unstable angina.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Threshold of High to Low Frequency Ratio (Threshold HLFR) | Baseline, 20 minutes | Please see published article for in depth method. This ratio is used to classify a given signal as either microbubble or noise. For a pixel containing a micro vessel, as microbubbles occasionally pass this otherwise dark pixel, its temporal signal is expected to have a higher proportion of lower frequency components than white noise. Consequently, the HLFR of the pixel is expected to be smaller than that of noise. A histogram of normalised HLFR shows two expected peaks (one noise, one bubbles). A threshold (Threshold HLFR) is determined to then separate the two peaks. this is done using a double Gaussian model, and where they intercept is deemed the Threshold HLFR. |
Countries
United Kingdom
Participant flow
Pre-assignment details
This study was terminated early after the proof of concept stage. We showed that our methodology worked in healthy subjects, then ran out of time thus no participants were enrolled in either the Symptomatic peripheral arterial disease or Symptomatic diabetic peripheral neuropathy arms.
Participants by arm
| Arm | Count |
|---|---|
| Healthy Subjects | 5 |
| Total | 5 |
Baseline characteristics
| Characteristic | Healthy Subjects |
|---|---|
| Age, Continuous | 23 years |
| Sex: Female, Male Female | 2 Participants |
| Sex: Female, Male Male | 3 Participants |
Adverse events
| Event type | EG000 affected / at risk | EG001 affected / at risk | EG002 affected / at risk | EG003 affected / at risk | EG004 affected / at risk |
|---|---|---|---|---|---|
| deaths Total, all-cause mortality | 0 / 1 | 0 / 1 | 0 / 1 | 0 / 1 | 0 / 1 |
| other Total, other adverse events | 0 / 1 | 0 / 1 | 0 / 1 | 0 / 1 | 0 / 1 |
| serious Total, serious adverse events | 0 / 1 | 0 / 1 | 0 / 1 | 0 / 1 | 0 / 1 |
Outcome results
Primary
Threshold of High to Low Frequency Ratio (Threshold HLFR)
Please see published article for in depth method. This ratio is used to classify a given signal as either microbubble or noise. For a pixel containing a micro vessel, as microbubbles occasionally pass this otherwise dark pixel, its temporal signal is expected to have a higher proportion of lower frequency components than white noise. Consequently, the HLFR of the pixel is expected to be smaller than that of noise. A histogram of normalised HLFR shows two expected peaks (one noise, one bubbles). A threshold (Threshold HLFR) is determined to then separate the two peaks. this is done using a double Gaussian model, and where they intercept is deemed the Threshold HLFR.
Time frame: Baseline, 20 minutes
Population: 5 recruits imaged and analysed. One recruit of five was excluded due to acquisition error
| Arm | Measure | Group | Value (NUMBER) |
|---|---|---|---|
| Subject 1 | Threshold of High to Low Frequency Ratio (Threshold HLFR) | At rest | 0.12 Threshold ratio (no units) |
| Subject 1 | Threshold of High to Low Frequency Ratio (Threshold HLFR) | After treadmill protocol, 20mins | 0.167 Threshold ratio (no units) |
| Subject 2 | Threshold of High to Low Frequency Ratio (Threshold HLFR) | After treadmill protocol, 20mins | 0.14 Threshold ratio (no units) |
| Subject 2 | Threshold of High to Low Frequency Ratio (Threshold HLFR) | At rest | 0.093 Threshold ratio (no units) |
| Subject 3 | Threshold of High to Low Frequency Ratio (Threshold HLFR) | At rest | 0.16 Threshold ratio (no units) |
| Subject 3 | Threshold of High to Low Frequency Ratio (Threshold HLFR) | After treadmill protocol, 20mins | 0.15 Threshold ratio (no units) |
| Subject 4 | Threshold of High to Low Frequency Ratio (Threshold HLFR) | At rest | 0.4 Threshold ratio (no units) |
| Subject 4 | Threshold of High to Low Frequency Ratio (Threshold HLFR) | After treadmill protocol, 20mins | 0.35 Threshold ratio (no units) |
Post Hoc
Average Percentage Change in Microbubble Track Density Measure (MTD)
The number of pixels identified as having bubble signals was normalised by dividing by area of region of interest (ROI). MTD = pixels with microbubbles / area of ROI Used as a surrogate for active vascular density. The percentage change between rest and exercise (20 minute treadmill protocol) was calculated on each day
Time frame: Test repeated on consecutive day to measure repeatability
Population: One subject out of five excluded for acquisition error
| Arm | Measure | Value (MEAN) | Dispersion |
|---|---|---|---|
| Subject 1 | Average Percentage Change in Microbubble Track Density Measure (MTD) | 138.2 % change from baseline | Standard Deviation 79.8 |
| Subject 2 | Average Percentage Change in Microbubble Track Density Measure (MTD) | 119.4 % change from baseline | Standard Deviation 62.7 |