Pulmonary therapy
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Idiopathic pulmonary fibrosis (IPF) is a progressive fibrotic lung disease with a median survival of 3-4 years from time of initial diagnosis, similar to the time course of many malignancies. A hallmark of IPF is its unpredictable disease course, ranging from long periods of clinical stability to acute exacerbations with rapid decompensation. As the disease progresses, patients with chronic cough and progressive exertional dyspnea become oxygen dependent. ⋯ In addition to focusing on symptom management, there is emphasis on supplemental oxygen use, pulmonary rehabilitation, quality of life, and end-of-life care. Importantly, increased use of support groups and national foundation forums have served as venues for further disease education, communication, and advanced care planning outside of the hospital settings. The purpose of this review article is to discuss the clinical features of IPF, the role of palliative care in chronic disease management, current data supporting benefits of palliative care in IPF, its role in symptom management, and practices to help patients and their caregivers achieve their best quality of life.
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Professional societies have developed recommendations for patient triage protocols, but wide variations in triage patterns for many acute conditions exist among hospitals in the United States. Differences in hospitals' triage patterns can be attributed to factors such as physician behavior, hospital policy and real-time conditions such as intensive care unit capacity. ⋯ Because standardization of a national triage policy is not feasible due to differing resources available at each hospital, local guidelines should prevail that take into account hospitals' local resources. The goal would be to better match intensive care unit bed supply with demand.
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Machine learning (ML) is a discipline of computer science in which statistical methods are applied to data in order to classify, predict, or optimize, based on previously observed data. Pulmonary and critical care medicine have seen a surge in the application of this methodology, potentially delivering improvements in our ability to diagnose, treat, and better understand a multitude of disease states. Here we review the literature and provide a detailed overview of the recent advances in ML as applied to these areas of medicine. In addition, we discuss both the significant benefits of this work as well as the challenges in the implementation and acceptance of this non-traditional methodology for clinical purposes.
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The Editors-in-Chief of Pulmonary Therapy have prepared podcasts summarizing their current research, recent highlights from the field, and future predictions. Audio-only versions and the transcripts can be downloaded here: https://doi.org/10.6084/m9.figshare.11938863. Following this is a written summary of the journal's recent developments and the transcripts from the podcasts.