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Background:Transbronchial lung cryobiopsy (TBCB) plays an increasing role in the evaluation of diffuse parenchymal lung disease with acceptable diagnostic yield and safety profile compared with that of video-assisted thoracoscopic surgical lung biopsy on most reports. However, published outcomes with regard to safety and complication rates vary. We aim to determine the safety profile of TBCB when performed using a standardized protocol consistent with recently published expert guidelines.Materials and Methods:We reviewed prospectively maintained databases from 5 Australian tertiary referral centers. The procedures were performed in accordance with a recent expert statement recommending standardization of TBCB procedures, in particular with fluoroscopy, a secured airway, and prophylactic bronchial blockers. Periprocedural complications were assessed along with clinical outcomes. Result(s):A total of 121 patients underwent TBCB between August 2013 and August 2017 following a standardized protocol using general anesthesia. Of them, 84 patients (66.7%) were discharged on the day of the procedure. Pneumothorax occurred in 18 patients (14.9%), 13 (10.7%) of whom required chest tube drainage. Moderate bleeding occurred in 15 patients (13.2%) and severe bleeding in 1 (0.83%). Histopathologic diagnosis was made in 80 patients (66.1%). Conclusion(s):Pooled outcomes from Australian tertiary centers indicate that TBCB is safe when performed in a protocolized fashion. Active measures to anticipate and manage bleeding and to direct biopsy position result in low rates of major complications.Copyright © 2020 Lippincott Williams and Wilkins. All rights reserved.

Contains fulltext : 202693.pdf (publisher's version ) (Open Access), Schizophrenia is a severe mental disorder characterized by numerous subtle changes in brain structure and function. Machine learning allows exploring the utility of combining structural and functional brain magnetic resonance imaging (MRI) measures for diagnostic application, but this approach has been hampered by sample size limitations and lack of differential diagnostic data. Here, we performed a multi-site machine learning analysis to explore brain structural patterns of T1 MRI data in 2668 individuals with schizophrenia, bipolar disorder or attention-deficit/ hyperactivity disorder, and healthy controls. We found reproducible changes of structural parameters in schizophrenia that yielded a classification accuracy of up to 76% and provided discrimination from ADHD, through it lacked specificity against bipolar disorder. The observed changes largely indexed distributed grey matter alterations that could be represented through a combination of several global brain-structural parameters. This multi-site machine learning study identified a brain-structural signature that could reproducibly differentiate schizophrenia patients from controls, but lacked specificity against bipolar disorder. While this currently limits the clinical utility of the identified signature, the present study highlights that the underlying alterations index substantial global grey matter changes in psychotic disorders, reflecting the biological similarity of these conditions, and provide a roadmap for future exploration of brain structural alterations in psychiatric patients.