ArticleJournal of inflammation research2025
Association and Predictive Ability between Inflammatory Burden Index and Fever Following Endobronchial Forceps Biopsy in Lung Cancer Patients.
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. Not yet cited in PubMed.
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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.
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