Quantifying the Influence of Wedge Pressure, Age, and Heart Rate on the Systolic Thresholds for Detection of Pulmonary Hypertension
Bibliographic record
Abstract
Background The strong linear relation between mean ( MPAP ) and systolic ( SPAP ) pulmonary arterial pressure (eg, SPAP =1.62× MPAP ) has been mainly reported in precapillary pulmonary hypertension. This study sought to quantify the influence of pulmonary arterial wedge pressure ( PAWP ), heart rate, and age on the MPAP ‐ SPAP relation. Methods and Results An allometric equation relating invasive MPAP and SPAP was developed in 1135 patients with pulmonary arterial hypertension, advanced lung disease, chronic thromboembolic pulmonary hypertension , or left heart failure. The equation was validated in 60 885 patients from the United Network for Organ Sharing ( UNOS ) database referred for heart and/or lung transplant. The MPAP / SPAP longitudinal stability was assessed in pulmonary arterial hypertension with repeated right heart catheterization. The equation obtained was SPAP =1.39× MPAP × PAWP −0.07 ×(60/heart rate) 0.12 ×age 0.08 ( P <0.001). It was validated in the UNOS cohort ( R 2 =0.93, P <0.001), regardless of the type of organ(s) patients were listed for (mean bias [−1.96 SD ; 1.96 SD ] was 0.94 [−8.00; 9.88] for heart, 1.34 [−7.81; 10.49] for lung and 0.25 [−16.74; 17.24] mm Hg for heart‐lung recipients). Thresholds of SPAP for MPAP =25 and 20 mm Hg were lower in patients with higher PAWP (37.2 and 29.8 mm Hg) than in those with pulmonary arterial hypertension (40.1 and 32.0 mm Hg). In 186 patients with pulmonary arterial hypertension , the predicted MPAP / SPAP was stable over time (0.63±0.03 at baseline and follow‐up catheterization, P =0.43). Conclusions This study quantifies the impact of PAWP , and to a lesser extent heart rate and age, on the MPAP ‐ SPAP relation, supporting lower SPAP thresholds for pulmonary hypertension diagnosis in patients with higher PAWP for echocardiography‐based epidemiological studies.
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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 | 0.017 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".