Hypochloremia Is a Noninvasive Predictor of Mortality in Pulmonary Arterial Hypertension
Bibliographic record
Abstract
Background Pulmonary arterial hypertension (PAH) is a lethal disease. In resource-limited countries PAH outcomes are worse because therapy costs are prohibitive. To improve global outcomes, noninvasive and widely available biomarkers that identify high-risk patients should be defined. Serum chloride is widely available and predicts mortality in left heart failure, but its prognostic utility in PAH requires further investigation. Methods and Results In this study 475 consecutive PAH patients evaluated at the University of Minnesota and Vanderbilt University PAH clinics were examined. Clinical characteristics were compared by tertiles of serum chloride. Both the Kaplan-Meier method and Cox regression analysis were used to assess survival and predictors of mortality, respectively. Categorical net reclassification improvement and relative integrated discrimination improvement compared prediction models. PAH patients in the lowest serum chloride tertile (≤101 mmol/L: hypochloremia) had the lowest 6-minute walk distance and highest right atrial pressure despite exhibiting no differences in pulmonary vascular disease severity. The 1-, 3-, and 5-year survival was reduced in hypochloremic patients when compared with the middle- and highest-tertile patients (86%/64%/44%, 95%/78%/59%, and, 91%/79%/66%). After adjustment for age, sex, diuretic use, serum sodium, bicarbonate, and creatinine, the hypochloremic patients had increased mortality when compared with the middle-tertile and highest-tertile patients. The Minnesota noninvasive model (functional class, 6-minute walk distance, and hypochloremia) was as effective as the French noninvasive model (functional class, 6-minute walk distance, and elevated brain natriuretic peptide or N-terminal pro-brain natriuretic peptide) for predicting mortality. Conclusions Hypochloremia (≤101 mmol/L) identifies high-risk PAH patients independent of serum sodium, renal function, and diuretic use.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".