Revisiting the relationship between pulse transit time and arterial stiffness
Bibliographic record
Abstract
Abstract Introduction: Unlike secondary prevention, primary prevention of the major cardiovascular events has not yet been successful. One of the reasons is that current methodology has not succeeded in selecting high risk patients who are suitable candidates for primary prevention. Increased arterial stiffness, a known risk factor for cardiovascular events, has been commonly indexed by the pulse transit time (PTT), even though PTT is influenced by short-term blood pressure (BP) change.Δ Methods and Results PTT and arterial BP of 93 human adults were analyzed. To determine the relationship between PTT and systolic blood pressure (SBP), the data was fitted to an exponential equation (\(\:\text{P}\text{T}\text{T}={\alpha\:}\times\:{\text{e}}^{\frac{-{\beta\:}\times\:\text{S}\text{B}\text{P}}{2}}\)) which is derived from physiological theory. The index β in this equation, which reflects arterial stiffness, was found to be independent of SBP (R 2 = 0.006, p = 0.5088), while PTT had poor correlation with β (R 2 = 0.202, p < 0.0001). We propose a new index of arterial stiffness, \(\:\frac{-2\times\:\frac{\varDelta\:\text{P}\text{T}\text{T}}{\varDelta\:\text{S}\text{B}\text{P}}}{\text{P}\text{T}\text{T}\text{m}\text{e}\text{a}\text{n}}\), which is independent of BP. This index is shown to approximate β and correlate well with it (R 2 = 0.935, p < 0.0001). Conclusions PTT is affected by short-term BP changes, which makes it difficult to use for estimating arterial stiffness based on a single measurement in the hospital. Furthermore, it is difficult to estimate BP only from PTT, because BP itself is affected by arterial stiffness in addition to PTT. The proposed index characterizes the severity of atherosclerosis, independently of temporary BP change. (238 words)
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".