Pulmonary Vascular Compromise Is Associated With Survival in Pediatric Pulmonary Hypertension: A New Computational Model
Bibliographic record
Abstract
ABSTRACT Pediatric pulmonary arterial hypertension (PAH) has a long asymptomatic period with progressive vascular loss. A recent computational model of simulated PAH in humans has demonstrated that up to 70% of the pulmonary vasculature is lost before clinical PAH criteria are met. We used this model in pediatric subjects with PAH to evaluate whether estimated pulmonary vascular loss or compromise (PVC) was associated with hemodynamic variables, survival, and other clinical outcomes. Retrospective and prospective cohort data were collected for subjects with PAH between 1999 and 2022 treated at our center. Cardiac catheterization and clinical data were compared with PVC estimated by the computational model. Transplant‐free survival was associated with lower PVC (72% vs. 88%, p < 0.001) and was also associated with a decrease in PVC over time with no significant change in PVC in subjects who died or underwent transplant. By Kaplan‐Meier analysis, 10‐year survival was 54% (IQR 35%, 81%) when PVC was more than 80%, compared with 100% survival (IQR 100%, 100%) when PVC was less than 80% ( p < 0.001). By Cox proportional hazard regression, PVC was associated with mortality (HR 1.44, p = 0.008). Lower PVC was associated with better clinical outcomes including percent predicted 6‐min walk distance, brain natriuretic peptide, and estimated 1‐year mortality. These findings demonstrate that PVC is a new computational hemodynamic variable estimating vascular area loss and is associated with transplant‐free survival and other clinical outcomes in pediatric PAH. Further, PVC provides an adjunctive tool to potentially capture pulmonary vascular loss early in disease, progression, and response to therapy.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".