Normal and Abnormal Relationships of Pulmonary Artery to Wedge Pressure During Exercise
Bibliographic record
Abstract
Background Resting right heart catheterization can assess both left heart filling and pulmonary artery (PA) pressures to identify and classify pulmonary hypertension. Although exercise may further elucidate hemodynamic abnormalities, current pulmonary hypertension classifications do not consider the expected interrelationship between PA and left heart filling pressures. This study explored the utility of this relationship to enhance the classification of exercise hemodynamic phenotypes in pulmonary hypertension. Methods and Results Data from 36 healthy individuals (55, 50-60 years, 50% male) and 85 consecutive patients (60, 49-71 years, 48% male) with dyspnea and/or suspected pulmonary hypertension of uncertain etiology were analyzed. Right heart catheterization was performed at rest and during semiupright submaximal cycling. To classify exercise phenotypes in patients, upper 95% CIs were identified from the healthy individuals for the change from rest to exercise in mean PA pressure over cardiac output (ΔmPAP/ΔCO ≤3.2 Wood units [WU]), pulmonary artery wedge pressure over CO (ΔPAWP/ΔCO ≤2 mm Hg/L per minute), and exercise PA pulse pressure over PAWP (PP/PAWP ≤2.5). Among patients with a ΔmPAP/ΔCO ≤3.2 WU, the majority (84%) demonstrated a ΔPAWP/ΔCO ≤2 mm Hg/L per minute, yet 23% demonstrated an exercise PP/PAWP >2.5. Among patients with a ΔmPAP/ΔCO >3.2 WU, 37% had an exercise PP/PAWP >2.5 split between ΔPAWP/ΔCO groups. Patients with normal hemodynamic classification declined from 52% at rest to 36% with exercise. Conclusions The addition of PP/PAWP to classify exercise hemodynamics uncovers previously unrecognized abnormal phenotypes within each ΔmPAP/ΔCO group. Our study refines abnormal exercise hemodynamic phenotypes based on an understanding of the interrelationship between PA and left heart filling pressures.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".