Evidence map›Paper›PMID 41357739›Full record

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.

Zishu Zhang, Yuexian Liu, Zongbo Shen, Koudong Zhang, Honglan Gao, Zhimin Chen, Zhongxiang Liu

Abstract readMulticenter StudyObservational Study
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Zishu Zhang *Department of Respiratory and Critical Care Medicine, The People's Hospital of Xiangshui, Yancheng, 224600, People's Republic of China.
Yuexian Liu *Department of Neurology, 920th Hospital of the Joint Logistic Support Force, Kunming, 650032, People's Republic of China.
Zongbo Shen *Department of Respiratory and Critical Care Medicine, The People's Hospital of Lincang, Lincang, 677000, People's Republic of China.
Koudong Zhang *Department of Respiratory and Critical Care Medicine, The Yancheng Clinical College of Xuzhou Medical University, The First People's Hospital of Yancheng, Yancheng, 224000, People's Republic of China.
Honglan GaoDepartment of Clinical Nutrition, The Yancheng Clinical College of Xuzhou Medical University, The First People's Hospital of Yancheng, Yancheng, 224000, People's Republic of China.
Zhimin ChenDepartment of Clinical Nutrition, The Yancheng Clinical College of Xuzhou Medical University, The First People's Hospital of Yancheng, Yancheng, 224000, People's Republic of China.
Zhongxiang LiuDepartment of Respiratory and Critical Care Medicine, The Yancheng Clinical College of Xuzhou Medical University, The First People's Hospital of Yancheng, Yancheng, 224000, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

HypercapniaInflammationInflammation MediatorsMalnutritionNutritional StatusPulmonary Disease, Chronic ObstructiveRespiratory InsufficiencyAgedBiomarkersCause of DeathChinaFemaleHumansMaleMiddle AgedNeutrophilsBiomarkersInflammation Mediatorsall-cause mortalityCOPDhypercapnic respiratory failureinflammationnutrition

Identifiers

PMID41357739
PMCPMC12680505

What Socratic holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.