Biomarkers (NLR, PLR, SII) for Frequent COPD Exacerbations: Diagnostic and Clinical Management Implications in a Retrospective Study.

OBJECTIVE

To evaluate the diagnostic and predictive value of the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and systemic immune-inflammation index (SII) for frequent exacerbations of chronic obstructive pulmonary disease (AECOPD), and to develop a risk stratification scoring system to optimize clinical management in resource-limited healthcare settings.

PATIENTS AND METHODS

This retrospective observational study enrolled 16,849 AECOPD patients, categorized into frequent exacerbators (≥2 exacerbations/year, n=3,488) and non-frequent exacerbators (<2 exacerbations/year, n=13,361). Comparative analyses of clinical characteristics and inflammatory biomarkers (NLR, PLR, SII, CRP, PCT) were conducted. Spearman correlation, receiver operating characteristic (ROC) curve analysis, and binary logistic regression were employed to assess biomarker performance. A risk scoring system was developed using odds ratios (OR) and regression coefficients (β) of NLR and PLR.

RESULTS

The frequent exacerbators group exhibited significantly higher median NLR (6.71 vs 5.10, P < 0.001), mean PLR (239 ± 204 vs 218 ± 195, P < 0.001), and median SII (1,137.48 vs 847.54, P < 0.001). NLR, PLR and SII showed strong positive correlations with CRP and PCT (P < 0.001). ROC analysis identified NLR (specificity = 84.1%) and PLR (sensitivity = 55%) as optimal diagnostic indicators. Regression analysis confirmed NLR and PLR as independent risk factors for frequent exacerbations. The risk stratification system categorized patients into low-risk (<290 points; annual exacerbation rate 17%), intermediate-risk (290-768 points; 19.1%), and high-risk (>768 points; 23.4%) groups.

CONCLUSION

NLR and PLR serve as cost-effective biomarkers for identifying high-risk frequent exacerbators patients with COPD in primary care settings. The percentile-based scoring system enables management strategies to address clinical needs in resource-constrained healthcare environments.