Observational studyInternational journal of chronic obstructive pulmonary disease2025
Nutritional and Inflammatory Predictors of All-Cause Mortality in COPD Patients with Hypercapnic Respiratory Failure: A Two-Center Prospective Cohort Study.
Observational study in International journal of chronic obstructive pulmonary disease, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Development and internal validation of a nomogram for predicting in-hospital mortality in patients with acute exacerbation of chronic obstructive pulmonary disease.Journal of thoracic disease · 2026Article
- The predictive value of malnutrition on the prognosis of severe respiratory failure in elderly patients: a multicenter retrospective study based on interpretable machine learning.Frontiers in nutrition · 2026Article
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Authors and funding
7 authors.
Funding
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Abstract
Background: Patients with chronic obstructive pulmonary disease (COPD) complicated by hypercapnic respiratory failure (HRF) often have a poor prognosis. Systemic inflammation and malnutrition are associated with adverse outcomes in COPD, yet the prognostic value of nutritional/inflammatory markers remains underexplored in COPD patients with HRF. Methods: This prospective two-center cohort study enrolled 582 COPD complicated by HRF patients. Six indices, including Platelet-to-Lymphocyte Ratio (PLR), Neutrophil-to-Lymphocyte Ratio (NLR), Systemic Immune-Inflammation Index (SII), Prognostic Nutritional Index (PNI), Neutrophil Percentage-to-Albumin Ratio (NPAR), and Hemoglobin-Albumin-Lymphocyte-Platelet index (HALP), were calculated from admission biomarkers. Associations with 24-month all-cause mortality were evaluated using restricted cubic splines, Kaplan-Meier analysis, multivariable Cox regression, machine learning (Random Survival Forests, Boruta), threshold effect and subgroup analysis. Predictive performance was assessed via the receiver operating characteristic curve (ROC) analysis. Results: Over 24 months, 263 patients (45.2%) died. Non-survivors exhibited significantly higher NLR, PLR, SII, and NPAR, but lower PNI and HALP (P < 0.05). Kaplan-Meier analysis and Cox models confirmed that higher PNI (HR=0.72, 95% CI:0.54-0.96) and HALP (HR=0.55, 95% CI:0.41-0.74) were negatively correlated with all-cause mortality, while elevated PLR (HR=1.39, 95% CI:1.04-1.85), NLR (HR=1.39, 95% CI:1.02-1.88), SII (HR=1.51, 95% CI:1.11-2.05), and NPAR (HR=1.46, 95% CI:1.10-1.95) were positively correlated with all-cause mortality. For each one-standard-deviation increase in the indicators, all-cause mortality statistically significantly increased or decreased (P for trend < 0.05), with the exception of SII. Machine learning and ROC analyses consistently identified HALP, PNI, and NPAR as top predictors, with HALP demonstrating the highest importance. Subgroup analyses confirmed consistent prognostic utility for PNI, HALP, and NPAR. Conclusion: PNI, HALP, and NPAR are promising, readily available predictors of all-cause mortality in COPD patients with HRF, potentially enhancing risk stratification and personalized management.
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