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

泛免疫炎症值升高与慢性阻塞性肺疾病急性加重患者的住院时间延长相关

阅读:1

Abstract

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(2)-PIV values were significantly associated with prolonged hospital stay in patients with AECOPD. Multivariate regression analysis revealed that log(2)-PIV (≥ 10.08) was an independent predictor of prolonged hospital stay (odds ratio = 1.57; 95% confidence Interval: 1.21-2.02; P = 0.001). Furthermore, RCS regression demonstrated a linear correlation between log(2)-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.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。