Real-World Data for Identifying Predictors of Virtual Bronchoscopic Navigation Success

<bold>Background:</bold> Identifying predictive factors that influence the diagnostic yield of Virtual bronchoscopic navigation (VBN) is essential to optimise patient selection. <bold>Methods:</bold> This retrospective study included 256 patients who underwent VBN at our institution between 12/2019 and 03/2024. A multivariate logistic regression model was developed to identify predictors of diagnostic outcome, defined as either a true positive or true negative result. <bold>Results:</bold> A total of 256 patients (52.3% female, mean age 67.0 ± 10.3 years) were analysed, encompassing 266 lung lesions (mean diameter 13.7 ± 5.7 mm; Brock score 33.1 ± 20.6%; 44.7% with positive bronchus sign). A total of 147 diagnostic outcomes were obtained. The multivariate model (<bold>Fig. 1</bold>) identified five significant predictors: the presence of a bronchus sign (OR 2.29), the number of airway branches of the navigation path (OR 0.68), the distance from the calculated point of entry to the target lesion (OR 0.70), successful attainment of the virtual target in fused fluoroscopy (OR 1.86), and at least a semicircular signal in R-EBUS (OR 2.25). The use of cryobiopsy showed a statistical trend with an OR of 1.86 (p = 0.09). <bold>Interpretation:</bold> This real-world study confirms that VBN is a reliable diagnostic method. The multivariate model highlights key predictive factors that can guide diagnostic outcomes both before and during the procedure, thereby enhancing targeted patient selection. <fig><object-id>erj;66/suppl_69/PA1958/F1</object-id><object-id>F1</object-id><object-id>F1</object-id><graphic></graphic></fig>