Chest
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Survivors of ICU hospitalizations often experience severe and debilitating symptoms long after critical illness has resolved. Many patients experience notable psychiatric sequelae such as depression, anxiety, and posttraumatic stress disorder (PTSD) that may persist for months to years after discharge. The COVID-19 pandemic has produced large numbers of critical illness survivors, warranting deeper understanding of psychological morbidity after COVID-19 critical illness. ⋯ Existing and novel interventions focused on minimizing psychiatric morbidity need to be further investigated to improve critical care survivorship after COVID-19 illness. This review proposes a framework to conceptualize three domains of risk factors (pathophysiologic, iatrogenic, and situational) associated with psychological morbidity caused by COVID-19 critical illness: (1) direct and indirect effects of the COVID-19 virus in the brain; (2) iatrogenic complications of ICU care that may disproportionately affect patients with COVID-19; and (3) social isolation that may worsen psychological morbidity. In addition, we review current interventions to minimize psychological complications after critical illness.
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Overdiagnosis of lung cancer by low-dose CT (LDCT) screening has raised concerns globally. LDCT screening has been used widely in employee health examinations in China since 2011. ⋯ The results provide evidence at a population level for lung cancer overdiagnosis in Chinese women resulting from increasing LDCT screening in the low-risk populations. Criteria for LDCT screening and management of screening-detected nodules need to be addressed fully for expanded application of LDCT screening in China.
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Clinical Trial
A 30-minute Spontaneous Breathing Trial (SBT) misses many children who go on to fail a 120-minute SBT.
The optimal length of spontaneous breathing trials (SBTs) in children is unknown. ⋯ gov.
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Artificial intelligence tools and techniques such as machine learning (ML) are increasingly seen as a suitable manner in which to increase the prediction capacity of currently available clinical tools, including prognostic scores. However, studies evaluating the efficacy of ML methods in enhancing the predictive capacity of existing scores for community-acquired pneumonia (CAP) are limited. We aimed to apply and validate a causal probabilistic network (CPN) model to predict mortality in patients with CAP. ⋯ SeF-ML shows potential for improving mortality prediction among patients with CAP, using structured health data. Additional external validation studies should be conducted to support generalizability.
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The prognosis and therapeutic responses are worse for pulmonary arterial hypertension associated with systemic sclerosis (SSc-PAH) compared with idiopathic pulmonary arterial hypertension (IPAH). This discrepancy could be driven by divergence in underlying metabolic determinants of disease. ⋯ Patients with SSc-PAH are characterized by an unfavorable bioactive metabolic profile that may explain the poor and limited response to therapy. These data provide important metabolic insights into the molecular heterogeneity underlying differences between subgroups of PAH.