Trial Outcomes & Findings for Evaluation of Musculoskeletal Microcirculation With Ultrasound (NCT NCT02086331)

Results Overview

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.

Recruitment status

TERMINATED

Study phase

NA

Target enrollment

5 participants

Primary outcome timeframe

Baseline, 20 minutes

Results posted on

2020-11-25

Participant Flow

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.

Participant milestones

Participant milestones
Measure	Healthy Healthy participants
Overall Study STARTED	5
Overall Study COMPLETED	5
Overall Study NOT COMPLETED	0

Reasons for withdrawal

Withdrawal data not reported

Baseline Characteristics

Evaluation of Musculoskeletal Microcirculation With Ultrasound

Baseline characteristics by cohort

Baseline characteristics by cohort
Measure	Healthy Subjects n=5 Participants
Age, Continuous	23 years n=93 Participants
Sex: Female, Male Female	2 Participants n=93 Participants
Sex: Female, Male Male	3 Participants n=93 Participants

PRIMARY outcome

Timeframe: Baseline, 20 minutes

Population: 5 recruits imaged and analysed. One recruit of five was excluded due to acquisition error

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.

Outcome measures

Outcome measures
Measure	Subject 1 n=1 Participants	Subject 2 n=1 Participants	Subject 3 n=1 Participants	Subject 4 n=1 Participants
Threshold of High to Low Frequency Ratio (Threshold HLFR) At rest	0.12 Threshold ratio (no units)	0.093 Threshold ratio (no units)	0.16 Threshold ratio (no units)	0.4 Threshold ratio (no units)
Threshold of High to Low Frequency Ratio (Threshold HLFR) After treadmill protocol, 20mins	0.167 Threshold ratio (no units)	0.14 Threshold ratio (no units)	0.15 Threshold ratio (no units)	0.35 Threshold ratio (no units)

POST_HOC outcome

Timeframe: Test repeated on consecutive day to measure repeatability

Population: One subject out of five excluded for acquisition error

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