Pulmonary Pulse Wave Transit Time is Associated with Right Ventricular–Pulmonary Artery Coupling in Pulmonary Arterial Hypertension
Bibliographic record
Abstract
Pulmonary pulse wave transit time (pPTT), defined as the time for the systolic pressure pulse wave to travel from the pulmonary valve to the pulmonary veins, has been reported to be reduced in pulmonary arterial hypertension (PAH); however, the underlying mechanism of reduced pPTT is unknown. Here, we investigate the hypothesis that abbreviated pPTT in PAH results from impaired right ventricular–pulmonary artery (RV‐PA) coupling. We quantified pPTT using pulsed‐wave Doppler ultrasound from 10 healthy age‐ and sex‐matched controls and 36 patients with PAH. pPTT was reduced in patients with PAH compared with controls. Univariate analysis revealed the following significant predictors of reduced pPTT: age, right ventricular fractional area change (RV FAC), tricuspid annular plane excursion (TAPSE), pulmonary arterial pressures (PAP), diastolic pulmonary gradient, transpulmonary gradient, pulmonary vascular resistance, and RV‐PA coupling (defined as RV FAC/mean PAP or TAPSE/mean PAP). Although the correlations between pPTT and invasive markers of pulmonary vascular disease were modest, RV FAC (r = 0.64, P < 0.0001), TAPSE (r = 0.67, P < 0.0001), and RV‐PA coupling (RV FAC/mean PAP: r = 0.72, P < 0.0001; TAPSE/mean PAP: r = 0.74, P < 0.0001) had the strongest relationships with pPTT. On multivariable analysis, only RV FAC, TAPSE, and RV‐PA coupling were independent predictors of pPTT. We conclude that shortening of pPTT in patients with PAH results from altered RV‐PA coupling, probably occurring as a result of reduced pulmonary arterial compliance. Thus, pPTT allows noninvasive determination of the status of both the pulmonary vasculature and the response of the RV in patients with PAH, thereby allowing monitoring of disease progression and regression.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".