肺癌患者支气管内钳取活检后炎症负荷指数与发热之间的关联及预测能力

Association and Predictive Ability between Inflammatory Burden Index and Fever Following Endobronchial Forceps Biopsy in Lung Cancer Patients.

作者信息

Zhang Minlong, Yang Cuiping, Guo Yinghua

机构信息

College of Pulmonary & Critical Care Medicine, 8th Medical Centre, Chinese PLA General Hospital, Beijing, People's Republic of China.

出版信息

J Inflamm Res. 2025 May 2;18:5911-5922. doi: 10.2147/JIR.S521144. eCollection 2025.

DOI:10.2147/JIR.S521144

PMID:40331157

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12054546/

Abstract

INTRODUCTION

Fever is a very common complication during endobronchial forceps biopsy (EBFB). Inflammatory burden index (IBI) are prognostic indicators for a multitude of inflammation and cancers, and our study focuses on evaluating the prognostic significance of the IBI on fever post-EBFB in lung cancer patients.

METHODS

501 patients with primary lung cancer undergone EBFB were enrolled in this study. The connection between the IBI and the risk of fever was studied using logistic regression analysis, restricted cubic spline (RCS) was employed to assess the association's form. Then, the most influential factors were selected through the application of Boruta algorithm and LASSO regression method and nomogram model was developed using multivariate logistic regression. Internal validation was performed using bootstrapping. Model performance was evaluated using the area under the curve (AUC), calibration curves, and decision curve analysis (DCA).

RESULTS

With an upwards shift in IBI vertices, the rate of fever post-EBFB steadily rose. The RCS analysis indicated J-shaped associations. Inflection points occurred at IBI=8.615 for fever post-EBFB. Patients in the highest IBI quartile had a significantly higher risk of fever post-EBFB compared to those in the lowest quartile. Sensitivity subgroup analyses also verified this association (all HRs > 1.0). Finally, the integration of Boruta and LASSO methodologies identified neutrophil percentage, C-reactive protein, examination time, nausea or vomiting, bleeding as significant predictors. We applied these predictors (model 1) separately and combined them with IBI (model 2) to develop two predictive models. The AUC of model 1 was 0.956 (95% CI, 0.936-0.972), and it was 0.958 (95% CI, 0.941-0.972) in model 2. The predictive model was well calibrated and DCA indicated its potential clinical usefulness. The predictive performance of Model 2 is better than that of Model 1.

DISCUSSION

IBI can serve as effective indicators for predicting the fever post-EBFB in lung cancer patients.

摘要

引言

发热是支气管内钳取活检（EBFB）过程中非常常见的并发症。炎症负荷指数（IBI）是多种炎症和癌症的预后指标，我们的研究重点是评估IBI对肺癌患者EBFB后发热的预后意义。

方法

本研究纳入了501例行EBFB的原发性肺癌患者。采用逻辑回归分析研究IBI与发热风险之间的关系，采用受限立方样条（RCS）评估这种关联的形式。然后，通过应用Boruta算法和LASSO回归方法选择最有影响的因素，并使用多变量逻辑回归建立列线图模型。使用自举法进行内部验证。使用曲线下面积（AUC）、校准曲线和决策曲线分析（DCA）评估模型性能。

结果

随着IBI顶点的向上移动，EBFB后发热率稳步上升。RCS分析表明存在J形关联。EBFB后发热的拐点出现在IBI = 8.615处。与最低四分位数的患者相比，最高IBI四分位数的患者EBFB后发热风险显著更高。敏感性亚组分析也证实了这种关联（所有HR>1.0）。最后，Boruta和LASSO方法的整合确定中性粒细胞百分比、C反应蛋白、检查时间、恶心或呕吐、出血为重要预测因素。我们分别应用这些预测因素（模型1）并将它们与IBI（模型2）结合，建立了两个预测模型。模型1的AUC为0.956（95%CI，0.936 - 0.972），模型2为0.958（95%CI，0.941 - 0.972）。预测模型校准良好，DCA表明其具有潜在的临床实用性。模型2的预测性能优于模型1。