Pulmonary Arterial Hypertension in Systemic Lupus Erythematosus: Prevalence and Predictors
Bibliographic record
Abstract
OBJECTIVE: Pulmonary arterial hypertension (PAH) prevalence has been reported to be between 0.5% and 17% in systemic lupus erythematosus (SLE). This study assessed PAH prevalence and predictors in an SLE cohort. METHODS: The Borg dyspnea scale, DLCO, N-terminal pro-brain natriuretic peptide (NT-proBNP), and Doppler echocardiographic (DE) were performed. An echocardiographic Doppler exercise test was conducted in selected patients. When DE systolic pulmonary arterial pressure was ≥ 45 mmHg or increased during exercise > 20 mmHg, a right heart catheterization was performed. Hemodynamic during exercise was measured if rest mean pulmonary arterial pressure was < 25 mmHg. RESULTS: Of the 203 patients with SLE, 152 were included. The mean age was 44.9 ± 12.3 years, and 94% were women. Three patients had known PAH. The algorithm diagnosed 1 patient with chronic thromboembolic pulmonary hypertension and 5 with exercise-induced pulmonary artery pressure increase (4 with occult left diastolic dysfunction). These patients had significantly more dyspnea, higher NT-proBNP, and lower DLCO. CONCLUSION: These data confirm the low prevalence of PAH in SLE. In our cohort, occult left ventricular diastolic dysfunction was a frequent diagnosis of unexplained dyspnea. Dyspnea, DLCO, and NT-proBNP could be predictors of pulmonary hypertension in patients with SLE.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".