Comparison of the EndoPAT device with a unique vascular assessment of reactive hyperemia
Bibliographic record
Abstract
The EndoPAT device (Itamar Medical) is used to determine endothelial function by using peripheral arterial tonometry during reactive hyperemia. It has been shown to be related to the gold standard non-invasive technique of flow-mediated dilation ultrasound assessment. These methodologies have technical requirements and/or ongoing operational costs that could be prohibitive to under-resourced clinics or researchers. We hypothesized that using a re-usable and cost-effective procedure of determining the reactive hyperemic response using piezo-electric pulse transducers (ADInstruments) would give similar results to the EndoPAT. Reactive hyperemia was induced by occluding blood flow to the forearm for 5 minutes using a blood pressure cuff inflated to +50mmHg above systolic blood pressure. The vascular response to the release of the occlusion was assessed using EndoPAT on the index fingers and using piezo-electric transducers on the middle fingers, concurrently. Twenty-five healthy participants (15 female; 31±12years) participated in the study. Participants refrained from caffeine, alcohol, fatty foods, smoking and strenuous exercise for at least 12 hours prior to testing. The EndoPAT reactive hyperemia index (RHI) was determined using proprietary software. The area under the curve (AUC) of the finger pulse waves from the piezoelectric pulse transducers was also determined. The ratio of the AUC of the peak hyperemic response (10s average) compared to the baseline period (3.5min) in the occluded arm was compared to the equivalent ratio in the control arm to determine the pulse-wave reactive hyperemia index (PWRHI). Univariate linear regression and correlation analysis was conducted between RHI and PWRHI. When comparing the two methods, Spearman’s r was 0.67 (p=0.0002) with a coefficient of determination of 0.58. We have found that the two methods correlate well to each other and the PWRHI technique has the potential to make microvascular assessments more accessible to clinicians and researchers. Our PWRHI method still needs to be validated in larger populations with vascular dysfunction and over a wider age-range. Funding provided by the Natural Sciences and Engineering Research Council of Canada. This is the full abstract presented at the American Physiology Summit 2023 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".