Assessing the Validity of Computerized Algorithms for Determining Pulse Wave Velocity: A Clinical Study
Bibliographic record
Abstract
Introduction: Aortic stiffness, assessed through carotid-femoral pulse wave velocity (PWV), has been associated with an increased risk of cardiovascular events and mortality. Measurements of PWV are based on the proper identification of the foot of the pulse waveform by either the maximum of the second-derivative method (as used in Complior) or the intersecting tangents algorithms (as used in SphygmoCor). These approaches can give different results, especially at higher PWV ranges. However, these devices also differ by signal acquisition technology, signal filtering, and quality control algorithms, making the true contribution of analytical algorithms uncertain. The aim of the present study was to identify the differences in pulse transit time (PTT) and PWV calculated by these two algorithms when provided with the same input signal. Methods: In 113 subjects, 346 recordings of 10 s were obtained using the Complior Analyse system (PWVComp-2nd). The pulse waves were imported into MATLAB and filtered (n = 4,102 pairs of pulse waves), where after inspection 3,770 pairs were available for determination of PTT using second-derivative and intersecting tangents algorithms (PTTMat-2nd and PTTMat-IT) and the respective PWVMat-2nd and PWVMat-IT for each pair. Additionally, the same pulse wave recordings were analyzed using the SphygmoCor system in simulation mode, employing the intersecting tangents algorithm (PWVSphyg-IT). Results: The mean beat-by-beat PTTMat-2nd and PTTMat-IT were 54.55 ± 18.55 ms (range 15.00–129.00) and 54.61 ± 18.61 ms (range 15.00–126.00) (p = 0.09), respectively. The mean per participant PWVMat-2nd and PWVMat-IT were 9.67 ± 3.46 m/s and 9.66 ± 3.4 m/s with a mean difference of 0.01 ± 0.32 m/s (p = 0.35). The PWVComp-2nd and PWVSphyg-IT were 9.48 ± 3.25 m/s and 9.59 ± 3.25 m/s with a mean difference of 0.11 ± 0.66 m/s (p = 0.04). Conclusion: The present study shows that the difference between the two algorithms is negligible across a wide range of PTT and hence does not support the need for adjusting PWV according to the algorithm used for determining PTT.
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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 | 0.023 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".