ArticleJournal of thoracic disease2024
Inflammatory markers as predictors of in-hospital mortality in acute exacerbation of chronic obstructive pulmonary disease (AECOPD) patients with acute respiratory failure: insights from the MIMIC-IV database.
Article in Journal of thoracic disease, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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.
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.
Who cites it
5 citing papers in PubMed.
- One Index Does Not Predict All-Hematological Derived Indices Have Different Predictive Value for ICU Mortality in Critically Ill Patients with Non-Infectious Versus Infectious Acute Exacerbation of COPD.Medicina (Kaunas, Lithuania) · 2026Observational
- Integrated multi-omics identifies CRP as a prognostic biomarker and reveals complement consumption in HIV-associated AECOPD.Frontiers in immunology · 2026Article
- Low levels of serum albumin and blood basophils as 10-year mortality predictors in a nationwide Korean COPD cohort.Scientific reports · 2025Article
- Inflammation and Albumin-Based Biomarkers Are Not Independently Associated with Mortality in Critically Ill COPD Patients: A Retrospective Study.Life (Basel, Switzerland) · 2025Article
- Development and validation of the machine learning model for acute exacerbation of chronic obstructive pulmonary disease prediction based on inflammatory biomarkers.Frontiers in medicine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Acute exacerbation of chronic obstructive pulmonary disease (AECOPD) particularly when coupled with acute respiratory failure (ARF), markedly elevates mortality rates. This investigation focuses on pivotal inflammatory markers in exacerbations of chronic obstructive pulmonary disease (COPD), including the neutrophil-to-lymphocyte ratio (NLR), lactate-to-albumin ratio (LAR), glucose-to-lymphocyte ratio (GLR), prognostic nutritional index (PNI), platelet-to-lymphocyte ratio (PLR), and systemic immune-inflammation index (SII), which are easily determinable from peripheral blood. We aimed to investigate the prognostic value of NLR, LAR, GLR, SII, PNI, and PLR for in-hospital mortality among AECOPD patients with ARF. Methods: This analysis encompassed data from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database, involving patients diagnosed with AECOPD and ARF. The study employed multivariate logistic regression and restricted cubic spline (RCS) models to evaluate the relationship between selected inflammatory markers and in-hospital mortality. The efficacy of these markers as prognostic tools was further assessed through receiver operating characteristic (ROC) curve analysis. Results: The study included 1,209 AECOPD patients with ARF, comprising 1,137 survivors and 72 fatalities, yielding an in-hospital mortality rate of 5.96%. Both NLR and PNI demonstrated non-linear relationships with mortality outcomes in RCS analysis, with inflection points at 6.66 and 43.54, respectively. Elevated GLR were linked with increased mortality risk. These results persisted even after adjusting for covariates. No significant associations were found for SII, LAR, or PLR. Notably, NLR [area under the curve (AUC) =0.684; 95% confidence interval (CI): 0.627-0.741] slightly surpassed PNI (AUC =0.663; 95% CI: 0.557-0.691) and GLR (AUC =0.624; 95% CI: 0.557-0.691) in predictive accuracy. Conclusions: NLR, GLR, and PNI on admission to hospital have moderate predictive utility for in-hospital mortality in patients with AECOPD and ARF. The findings may provide some references for exploring prognostic biomarkers and help clinicians to identify patients with AECOPD and ARF at elevated risk of mortality in an early stage.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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 OpenQuestion graph.