Long-term outcome of children with newly diagnosed pulmonary arterial hypertension: results from the global TOPP registry
Bibliographic record
Abstract
BACKGROUND AND AIMS: The Tracking Outcomes and Practice in Pediatric Pulmonary Hypertension (TOPP) registry is a global network established to gain insights into the disease course and long-term outcomes of paediatric pulmonary arterial hypertension (PAH). Previously published cohorts in paediatric PAH are obscured by survival bias due to the inclusion of both prevalent (previously diagnosed) and incident (newly diagnosed) patients. The current study aims to describe long-term outcome and its predictors in paediatric PAH, exclusively of newly diagnosed patients. METHODS AND RESULTS: Five hundred thirty-one children with confirmed pulmonary hypertension, aged ≥3 months and <18 years, were enrolled in the real-world TOPP registry at 33 centres in 20 countries, from 2008 to 2015. Of these, 242 children with newly diagnosed PAH with at least one follow-up visit were included in the current outcome analyses. During long-term follow-up, 42 (17.4%) children died, 9 (3.7%) underwent lung transplantation, 3 (1.2%) atrial septostomy, and 9 (3.7%) Potts shunt palliation (event rates: 6.2, 1.3, 0.4, and 1.4 events per 100 person-years, respectively). One-, three-, and five-year survival free from adverse outcome was 83.9%, 75.2%, and 71.8%, respectively.Overall, children with open (unrepaired or residual) cardiac shunts had the best survival rates. Younger age, worse World Health Organization functional class, and higher pulmonary vascular resistance index were identified as independent predictors of long-term adverse outcome. Younger age, higher mean right atrial pressure, and lower systemic venous oxygen saturation were specifically identified as independent predictors of early adverse outcome (within 12 months after enrolment). CONCLUSION: This comprehensive analysis of survival from time of diagnosis in a large exclusive cohort of children newly diagnosed with PAH describes current-era outcome and its predictors.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".