Diagnostic Test Accuracy of Pedal Acceleration Time to Identify Peripheral Artery Disease
Bibliographic record
Abstract
OBJECTIVE: Pedal acceleration time (PAT) is a novel method of using diagnostic ultrasound to evaluate the haemodynamic characteristics of pedal arteries and has potential as an adjunctive vascular testing method. The primary objective of this study was to assess the diagnostic accuracy of PAT in identifying peripheral artery disease (PAD) in a population with clinically suspected PAD. METHODS: This was a multicentre cross sectional study to estimate the diagnostic test accuracy. Participants with clinically suspected PAD were recruited via consecutive sampling at four centres. Colour duplex ultrasound (reference standard) and toe brachial index (TBI) were measured by a vascular sonographer. A second vascular sonographer, blinded to all other measures, conducted the PAT measurements on the same limb. PAD was defined as a > 50% stenosis in any vessel from the distal aorta to the foot. Sensitivity, specificity, positive and negative predictive values, and positive and negative likelihood ratios were estimated for all PAT values. Receiver operating characteristic curves were also generated. RESULTS: One hundred and eighty-eight participants (227 limbs) were recruited, with a mean age of 71 years (standard deviation 10, range 43 - 93) with 56 women (29.8%) including 133 (70.7) limbs having PAD (59%) and 61 (32.4%) claudicants. Area under the curve for PAT: lateral plantar artery 0.72 (0.65 - 0.79), medial plantar artery 0.72 (0.65 - 0.79), dorsal metatarsal artery 0.77 (0.70 - 0.84), arcuate artery 0.71 (0.64 - 0.78), and deep plantar artery 0.73 (0.67 - 0.80). Utilising the worst case PAT measure for the limb for identifying PAD had an AUC of 0.79 (0.74 - 0.85) and positive and negative predictive values of 0.81 (0.57 - 0.89) and 0.66 (0.57 - 0.75), respectively. Area under the curve for the toe brachial index was 0.78 (0.71 - 0.85) and that of ankle brachial index was 0.70 (0.62 - 0.77). CONCLUSION: PAT had an acceptable diagnostic test accuracy as an assessment tool to identify PAD in a population with clinically suspected PAD. All five measures yielded similar accuracy to toe pressure and TBI; however, using the worst case PAT value yielded the highest diagnostic test accuracy of all PAT measures. The PAT diagnostic threshold for the presence of PAD may be revised to > 85 ms to optimise the performance of the test for the identification of PAD.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".