Left heart disease: a frequent cause of early pulmonary hypertension in systemic sclerosis, unrelated to elevated NT-proBNP levels or overt cardiac fibrosis but associated with increased levels of MR-proANP and MR-proADM: retrospective analysis of a French Canadian cohort
Bibliographic record
Abstract
OBJECTIVES: Pulmonary hypertension (PH) causes mortality in systemic sclerosis (SSc). Pulmonary arterial hypertension (PAH) and left heart disease (LHD) are frequent causes of PH. Therefore, we studied PAH and LHD in early PH. METHOD: A total of 432 French Canadian SSc patients were studied retrospectively. All underwent screening for PH. We analysed clinical, serological, and radiographic data from 26 patients with early PH diagnosed by right heart catheterization (RHC). SSc patients with (n = 21) and without PH (n = 19) were prospectively re-evaluated by cardiac magnetic resonance imaging (MRI) and serial measurements of N-terminal pro-brain natriuretic peptide (NT-proBNP) and the haemodynamic biomarkers mid-regional pro-atrial natriuritic peptide (MR-proANP) and mid-regional pro-adrenomedullin (MR-proADM). RESULTS: The most frequent cause of early PH was LHD (58%). PAH was seen in 34% of patients. No association was found between the type of PH and autoantibodies. Early LHD-PH, but not early PAH, was associated with lower NT-proBNP (p = 0.024), but MR-proANP and MR-proADM levels were higher in early LHD-PH than in patients without PH (p = 0.014 and p = 0.012, respectively). Only one patient had abnormal cardiac MRI explaining LHD-PH. CONCLUSIONS: Early PH in SSc, like late PH, is heterogeneous and RHC is essential for determining its underlying cause. The most frequent cause of early PH was LHD. Levels of MR-proANP and MR-proADM, but not NT-proBNP, were increased in early LHD-PH, and may be more reliable than NT-proBNP as a biomarker of early PH in this subgroup of patients. Cardiac MRI did not explain LHD-PH. This study is the first to identify a high frequency of LHD in early PH correlating with normal NT-proBNP levels but increased MR-proANP and MR-proADM levels in SSc patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".