American journal of respiratory and critical care medicine
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Am. J. Respir. Crit. Care Med. · Oct 2022
Observational StudyLung Microbiota of Critically Ill COVID-19 Patients are Associated with Non-Resolving Acute Respiratory Distress Syndrome.
Rationale: Bacterial lung microbiota are correlated with lung inflammation and acute respiratory distress syndrome (ARDS) and altered in severe coronavirus disease (COVID-19). However, the association between lung microbiota (including fungi) and resolution of ARDS in COVID-19 remains unclear. We hypothesized that increased lung bacterial and fungal burdens are related to nonresolving ARDS and mortality in COVID-19. ⋯ Lung microbiota composition was associated with successful extubation (P = 0.0045). Proinflammatory cytokines (e.g., tumor necrosis factor-α) were associated with the microbial burdens. Conclusions: Bacterial and fungal lung microbiota are related to nonresolving ARDS in COVID-19 and represent an important contributor to heterogeneity in COVID-19-related ARDS.
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Am. J. Respir. Crit. Care Med. · Oct 2022
Deep Learning-based Outcome Prediction in Progressive Fibrotic Lung Disease Using High-resolution Computed Tomography.
Rationale: Reliable outcome prediction in patients with fibrotic lung disease using baseline high-resolution computed tomography (HRCT) data remains challenging. Objectives: To evaluate the prognostic accuracy of a deep learning algorithm (SOFIA [Systematic Objective Fibrotic Imaging Analysis Algorithm]), trained and validated in the identification of usual interstitial pneumonia (UIP)-like features on HRCT (UIP probability), in a large cohort of well-characterized patients with progressive fibrotic lung disease drawn from a national registry. Methods: SOFIA and radiologist UIP probabilities were converted to Prospective Investigation of Pulmonary Embolism Diagnosis (PIOPED)-based UIP probability categories (UIP not included in the differential, 0-4%; low probability of UIP, 5-29%; intermediate probability of UIP, 30-69%; high probability of UIP, 70-94%; and pathognomonic for UIP, 95-100%), and their prognostic utility was assessed using Cox proportional hazards modeling. ⋯ Conclusions: Deep learning-based UIP probability on HRCT provides enhanced outcome prediction in patients with progressive fibrotic lung disease when compared with expert radiologist evaluation or guideline-based histologic pattern. In principle, this tool may be useful in multidisciplinary characterization of fibrotic lung disease. The utility of this technology as a decision support system when ILD expertise is unavailable requires further investigation.