Discovery and Analysis of the Relationship between Organic Components in Exhaled Breath and Bronchiectasis.

Abstract

The prevalence of patients with bronchiectasis (BE) has been rising in recent years, which increases the substantial burden on the family and society. Exploring a convenient, effective, and low-cost screening tool for the diagnosis of BE is urgent. We expect to identify the accuracy of breath biomarkers(BBs) for the diagnosis of BE through breathomics testing and explore the association between BBs and clinical features of BE. Method: Exhaled breath samples were collected and detected by high-pressure photon ionization time-of-flight mass spectrometry(HPPI-TOF MS) in a cross-sectional study. Exhaled breath samples were from 215 patients with BE and 295 control individuals. The potential BBs were selected via the machine learning method. The overall performance was assessed for the BBs-based BE detection model. The significant BBs between different subgroups such as the severity of BE, acute or stable stage, combined with hemoptysis or not, with or without Nontuberculous Mycobacterium (NTM), Pseudomonas aeruginosa (P.a) isolation or not, and the BBs related to the number of involved lung lobes and lung function were discovered and analyzed. Results: The top 10 BBs based machine learning model achieved an area under the curve (AUC) of 0.940, sensitivity of 90.7%, specificity of 85%, and accuracy of 87.4% in BE diagnosis. Except for the top ten BBs, other BBs were found also related to the severity, acute/stable status, hemoptysis or not, NTM infection, P.a isolation, the number of involved lobes, and three lung functional paramters in BE patients. Conclusions: BBs-based BE detection model showed good accuracy for diagnosis. BBs have a close relationship with the clinical features of BE. The breath test method may provide a new strategy for bronchiectasis screening and personalized management. Clinical Trail Number: NCT05293314 .