Neutrophil-Lymphocyte Ratio as an Independent Predictor of Survival in Pulmonary Arterial Hypertension: An Exploratory Study
Bibliographic record
Abstract
Background The blood neutrophil-to-lymphocyte ratio (NLR) has recently emerged as a powerful predictor of adverse outcomes in some cardiovascular and lung diseases. Pulmonary arterial hypertension (PAH) is a lethal vasculopathy associated with increased inflammation. Although PAH exhibits a higher prevalence among women, men have a poorer prognosis. We investigated the NLR as an independent predictor of transplant-free survival in PAH. Methods We performed a retrospective analysis of 78 PAH patients from the Quebec PAHBiobank (71% female). We used univariate and multivariate (adjusted for age, sex, renal function, and disease severity) Cox regression analyses to assess the relationship between the NLR and transplant-free survival, in the whole sample, and according to sex. The NLR was categorized as high (≥ 4.8) or low (< 4.8) using receiver operating characteristic analysis. Unadjusted Kaplan-Meier analysis estimated survival per NLR category. Results The NLR was higher in patients who died, compared to that in patients who had transplant-free survival ( P < 0.05). The NLR was an independent predictor of event-free survival in PAH (unadjusted hazard ratio: 1.11, 95% confidence interval: 1.04-1.18, which remained significant after adjustment for covariates). The high-NLR group had lower 1-, 3-, and 5-year survival compared to those with a low NLR ( P < 0.001). The NLR remains a predictor of survival in women. Conclusions The NLR is an independent predictor of transplant-free survival in PAH. We report a potential sexual dimorphism in the ability of the NLR to predict mortality in PAH, emphasizing the importance of considering sex-related differences in the development of biomarkers in PAH.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".