Guiance, Irene Riestra

Abstract Introduction Multiple pulmonary nodules (MPNs) are commonly encountered in patients at elevated risk for thoracic malignancy and represent a spectrum of disease states and stages. We evaluated the safety, and procedural outcomes of a combined robotic assisted bronchoscopy (RaB) and linear endobronchial ultrasound (EBUS) approach to patients with MPNs during a single bronchoscopic procedure. Methods This multicenter observational registry included patients who underwent RaB for evaluation of MPNs. Data were prospectively collected from six centers between 2019 and 2025. Demographics, imaging characteristics, safety events, and procedural outcomes were analyzed and associated with final diagnosis. Results A total of 191 patients underwent RaB with biopsy of MPN during a single procedure; 167 patients also underwent linear EBUS lymph node sampling. The strict per nodule diagnostic yield was 71.5% overall (first nodule 70.7%, second 74.3%, third 71.4%). At least one malignant nodule was identified in 127 patients (66.8%), and 85 patients (44.5%) had two or more malignant nodules; 15 patients (17.6%) demonstrated heterogeneous histopathology between malignant nodules. Among patients with non–small cell lung cancer, adequate cellularity for next generation sequencing was achieved in 82.6% of nodules. Malignant lymph nodes were identified in 10.8% (18/167). No patients with two pure ground glass nodules (0/8) had malignant nodal involvement. Pneumothorax occurred in 7.3% of cases, with 2.1% requiring chest tube placement; unplanned admissions occurred in 3.7%. Conclusion This study provides a large multisite registry evaluation of performance of simultaneous bronchoscopic sampling of multiple pulmonary nodules with EBUS staging during a single anesthesia event demonstrating an acceptable safety profile and adequacy for molecular profiling.

To identify medical phrases utilized by the critical care team that may have an unintended impact on the critically ill patient, we administered an anonymous survey to multi-professional critical care team members. We elicited examples of imprecise language that may have a negative emotional impact on the critically ill. Of the 1600 providers surveyed, 265 offered 1379 examples (912 unique) which were clustered into 5 categories. Medical jargon (eg, “riding the vent”) was most prevalent (n = 549). There were 217 negative suggestions (eg, “you will feel a stick and a burn”). Hyperboles (eg, “black cloud”) were common (n = 198) while homonyms (ie “he fibbed”) accounted for 150 examples. Phrases such as “code brown in there” were categorized as metonyms (n = 144). 121 metaphors/similes (eg, “rearranging deck chairs on the Titanic”) were provided. Phrases that have the potential to negatively impact critically ill patient perceptions are commonplace in critical care practice. Whether these everyday communication habits lead to an unintended nocebo effect on mental health outcomes of the critically ill deserves further study.

Abstract Overgrowth of the fungus Wallemia mellicola in the intestines of mice enhances the severity of asthma. Wallemia mellicola interacts with the immune system through Dectin-2 expressed on the surface of myeloid and intestinal epithelial cells. Using Dectin-2-deficient mice, we show that the interaction of W. mellicola with Dectin-2 is essential for the gut-lung pathways, enhancing the severity of asthma in mice with W. mellicola intestinal dysbiosis. These findings offer better insight into dysbiosis-associated inflammation and highlight the role pattern recognition receptors have in immune recognition of commensal fungi in the gut, leading to alterations in immune function in the lungs.

1552 Background: Immune checkpoint inhibitor therapy-related (ICI) pneumonitis poses a significant challenge in patients with cancer. There is a need for predictive modeling to identify those at high risk of developing serious adverse events of ICI pneumonitis. Methods: A newly created database of cancer patients diagnosed with ICI pneumonitis seen at Mayo Clinic from 2014-2022 was utilized for exploratory data analysis. Modeling was used to determine if three clinical outcomes could be predicted, namely risk of death from: pneumonitis prior to starting immunotherapy, pneumonitis at time of diagnosis of ICI pneumonitis, and any cause at time of ICI pneumonitis diagnosis. The data consisted of 170 patients with annotation for 71 clinical features. Data was divided into features available at time of cancer diagnosis and at time of ICI pneumonitis diagnosis. Exploratory modeling was performed using the gradient boosting technique Xgboost (Chen, 2015) and conducted using k-fold balanced cross validation best practices as well as a train/test/validate schema with 70%/20%/10% data proportions, respectively. Model reverse engineering was done with Shapley statistics (Lundberg, 2017) to determine which features had the largest contribution per model. Once identified, only those highly weighted features were used for logistic regression analysis providing more reproducible predictions by decreasing model variance. Results: Risk of death from pneumonitis prior to starting immunotherapy was modeled with an area under the curve of the receiver operator characteristic (AUC-ROC) of 0.79 with the most contributory features including lymphocyte count, oxygen dependence, PFT values, and PD-L1 expression. Logistic regression produced an AUC-ROC of 0.87 (95% CI 0.72-1.0, p < 0.0013). Risk of death from pneumonitis at the time of ICI pneumonitis diagnosis was modeled with an AUC-ROC of 0.85 and the most contributory features were similar to the list described in the previous endpoint. Logistic regression produced an AUC-ROC of 0.89 (95% CI 0.81-0.96, p < 0.0001). Risk of death from any cause at the time of ICI pneumonitis diagnosis until follow up concluded by 12/2022 produced a model with an AUC-ROC of 0.75 with the most contributory features including supplemental oxygen, PFT values, basic laboratory values, and PD-L1 expression, among others. Logistic regression produced an AUC-ROC of 0.85 (95% CI 0.76-0.93, p < 0.0001). Conclusions: We demonstrate that commonly available clinical data can be used to identify patients at high risk of death from ICI pneumonitis. This study identified clinical features that were predictive in each scenario from which further concerted effort could produce a new clinical model to provide clinician decision support when considering immunotherapy. Further studies should be done to further elucidate feature interdependence and generalizability.