ArticleJournal of inflammation research2025
Dynamic Trends of Albumin-to-C-Reactive Protein Ratio: A Prognostic Indicator in Elderly Patients with Community-Acquired Pneumonia.
Article in Journal of inflammation research, 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.
- A machine learning-based model for assessing community-acquired pneumonia severity using routine blood tests.Frontiers in cellular and infection microbiology · 2025Article
- Use of Routine Hematological and Biochemical Parameters to Predict Clinical Outcomes in Community-acquired Pneumonia.Saudi journal of medicine & medical sciencesArticle
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The prognostic significance of dynamic changes in the albumin-to-C-reactive protein ratio (ACR) in elderly patients with community-acquired pneumonia (CAP) has not been fully elucidated. This study aims to evaluate the utility of ACR as a dynamic biomarker for predicting 28-day mortality and enhancing risk stratification in this high-risk population. Methods: A retrospective cohort study was conducted on 437 elderly CAP patients (≥65 years). Serum albumin and C-reactive protein (CRP) levels were measured at admission (T0), 24 hours (T1), and 3 days (T2) post-admission. ACR was calculated for each time point, and its prognostic value was assessed using advanced statistical methods. Results: The 28-day mortality rate was 16.7%. ACR levels were consistently lower in non-survivors across all time points (P < 0.001). RCS analysis revealed a nonlinear relationship between ACR and mortality risk. Time-varying ROC analysis demonstrated that ACR consistently outperformed CRP in predicting mortality, with superior area under the curve (AUC) values at all time points. Random-effects modeling indicated minimal inter-individual variability in ACR (random effects variance: 0.030; standard deviation: 0.175). Time-varying Cox regression confirmed a strong negative association between dynamic ACR changes and mortality risk, with a C-statistic of 0.833 (P < 0.001). Conclusion: Dynamic monitoring of ACR is a robust and clinically applicable tool for predicting short-term mortality in elderly CAP patients. By integrating markers of inflammation and nutritional status, ACR facilitates early identification of high-risk patients and supports personalized treatment strategies. These findings highlight the potential of ACR as a novel biomarker for improving clinical outcomes in this vulnerable population.
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