High Pan-Immune Inflammation Values are Associated with Prolonged Length of Hospital Stay in Patients with Acute Exacerbation of Chronic Obstructive Pulmonary Disease.

PURPOSE

Inflammation is a major contributor to prolonged hospital stays, increased healthcare costs, and poor prognosis in patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD). This study aimed to investigate the relationship between the Pan-Immune Inflammation Value (PIV), a novel immune-inflammatory biomarker, and the prolonged hospital stays in patients hospitalized for the first time with AECOPD to provide an effective risk assessment tool for clinical practice.

PATIENTS AND METHODS

We retrospectively analyzed clinical data from 5810 patients admitted to the Affiliated Dongyang Hospital of Wenzhou Medical University between January 2010 and March 2024, with AECOPD as the primary diagnosis. Prolonged hospital stay was defined as a stay exceeding the 75th percentile for all included patients (length of hospital stay > 10 days). The association between PIV and prolonged hospital stay in patients with AECOPD was assessed using multi-model logistic regression analysis, restricted cubic spline (RCS) curves, and subgroup analysis.

RESULTS

Higher log-PIV values were significantly associated with prolonged hospital stay in patients with AECOPD. Multivariate regression analysis revealed that log-PIV (≥ 10.08) was an independent predictor of prolonged hospital stay (odds ratio = 1.57; 95% confidence Interval: 1.21-2.02; = 0.001). Furthermore, RCS regression demonstrated a linear correlation between log-PIV and the risk of prolonged hospital stay. Subgroup analysis confirmed the consistency of this association across different patient populations.

CONCLUSION

PIV is a potential biomarker for predicting prolonged hospital stay in patients hospitalized for the first time with AECOPD, providing a new assessment tool for clinical practice. The results of this study can help guide clinical decision-making, optimize treatment strategies, improve patient prognosis, and provide a scientific basis for the rational allocation of healthcare resources.